Orbits of Non-periodic Fourier Series: Simple Introduction, Cool Applications

These Fourier series can be considered as bivariate time series (X(t), Y(t)) where t is the time, X(t) is a weighted sum of cosine terms of arbitrary periods, and Y(t) is the same sum, except that cosine is replaced by sine. The orbit at time t is

where n can be finite or infinite, and Ak, Bk are the coefficients or weights. The shape of the orbit varies greatly depending on the coefficients: it can be periodic, smooth or chaotic, exhibits holes (or not), or fill dense areas of the plane. For instance, if Bk = k – 1, we are dealing with standard Fourier series, and the orbit is periodic. Also, X(t) and Y(t) can be viewed respectively as the real and imaginary part of a function taking values in the complex plane, as in one of the examples discussed here.

The goal of this article is to feature two interesting applications, focusing on exploratory analysis rather than advanced  mathematics, and to provide beautiful visualizations. There is no attempt at categorizing these orbits: this would be the subject of an entire book. Finally, a number of interesting, off-the-beaten-path exercises are provided, ranging from simple to very difficult.

The orbit is always symmetric with respect to the X-axis, since Y(-t) = –Y(t).

1. Application in astronomy

We are interested in the center of gravity (centroid) of n planets P1, …, Pn of various masses, rotating at various speeds, around a star located at the origin (0, 0), in a two-dimensional framework (the ecliptic plane). In this model, celestial bodies are assumed to be points, and gravitational forces between the planets are ignored. Also, for simplification, the orbit of each planet is circular rather than elliptic. Planet Pk has mass Mk, and its orbit is circular with radius Rk. Its rotation period is 2π / Bk. Also, at t = 0, all the planets are aligned on the X-axis.  Let M = M1 + … + Mn. Then the orbit of the centroid has the same formula as above, with Ak = Rk Mk / M for k = 1, …, n.

In the figures below, the left part represents the orbit of the centroid between t = 0 and t = 1,000 while the right part represents the orbit between t = 0 and t = 10,000.

Figure 1

Figure 2

Figure 3

In figure 1, we have n = 100 planets, all the planets have the same mass, Bk = k + 1, and Rk = 1 / (k + 1)^0.7 [ that is, 1 / (k + 1) at power 0.7]. The orbit is periodic because the Bk‘s are integers, though the period involves numerous little loops due to the large number of planets. The periodicity is masked by the thickness of the blue curve, but would be obvious to the naked eye on the right part of figure 1, if we only had 10 planets. I chose 100 planets because it creates a more beautiful, original plot.

Figure 2 is the same as figure 1, except that planet P50 has a mass 100 times bigger than all other planets. You would think that the orbit of the centroid should be close to the orbit of the dominant planet, and thus close to a circle. However this is not the case, and you need a much bigger “outlier planet” to get an orbit (for the centroid) close to a circle.

In figure 3, n = 50, Mk = 1 / SQRT(k+1), Ak = 1.75^(k+1), and Bk = log(k+1). This time, the orbit is non periodic. The area in blue on the right side becomes truly dense when t becomes infinite; it is not a visual effect. Note that in all our examples, there is  hole encompassing the origin. In many other examples (not shown here), there is no hole. Figure 3 is related to our discussion in section 2.

None of the above examples is realistic, as they violate both Kepler’s third law (see here) specifying the periods of the planets given Rk (thus determining Bk), and Titius-Bode law (see here) specifying the distances Rk between the star and its k-th planet. In other words, it applies either to a universe governed by laws other than gravity, or in the early process of planet formation when individual planet orbits are not yet in equilibrium. It would be an easy exercise to input the correct values of Ak and Bk corresponding to the solar system, and see the resulting non periodic orbit for the centroid of the planets.

2. The Riemann Hypothesis

The Riemann hypothesis is one of the most famous unsolved mathematical conjectures. It states that the Riemann Zeta function has no zero in a certain area of the (complex) plane, or in other words, that there is a hole around the origin in its orbit, depending on the parameter s, just like in Figures 1, 2 and 3. Its orbit corresponds to Ak = 1 / k^s, Bk = log k, and n infinite. Unfortunately, the cosine and sine series X(t), Y(t) diverge if s is equal to or less than 1. So in practice, instead of working with the Riemann Zeta function, one works with its sister called the Dirichlet Eta function, replacing X(t) and Y(t) by their alternating version, that is Ak = (-1)^(k+1) / k^s. Then we have convergence in the critical strip 0.5  <  s  <  1. Proving that there is a hole around the origin if 0.5  <  s  <  1 amounts to proving the Riemann Hypothesis. The non periodic orbit in question can be seen in this article as well as in figure 4.

Figure 4

Figure 4 shows the orbit, when n = 1,000. The right part seems to indicate that the orbit eventually fills the hole surrounding the origin, as t becomes large. However this is caused by using only n = 1,000 terms in the cosine and sine series. These series converge very slowly and in a chaotic way. Interestingly, if n = 4, there is a well defined hole, see figure 5. For larger values of n, the hole disappears, but it starts reappearing as n becomes very large, as shown in the left part of figure 4.

Figure 5

If n = 4 (corresponding to three planets in section 1 since the first term is constant here), a well defined hole appears, although it does not encompass the origin (see figure 5). Proving the existence of a non-vanishing hole encompassing the origin, regardless of how large t goes and regardless of s in ]0.5, 1[, when n is infinite, would prove the Riemann hypothesis. 

Note the resemblance between the left parts of figure 3 and 4. This could suggest two possible paths to proving the Riemann Hypothesis:

• Approximating the orbit of figure 4 by a an orbit like that of figure 3, and obtain a bound on the approximation error. If the bound is small enough, it will result in a smaller hole in figure 4, but possibly still large enough to encompass the origin.
• Find a topological mapping between the orbits of figure 3 and 4: one that preserves the existence of the hole, and preserves the fact that the hole encompasses the origin. 

 3. Exercises

Here are a few questions for further exploration. They are related to section 1.

• In section 1, all the planets are aligned when t = 0. Can this still happen again in the future if n = 3? What if n = 4? Assume that the orbit of the centroid is non periodic, and n is the number of planets.
• What are the conditions necessary and sufficient to make the orbit of the centroid non periodic?
• At the initial condition (t = 0), is the centroid always inside the limit domain of oscillations (the right part on each figure, colored in blue)? Or can the orbit permanently drift away from its location at t = 0, depending on the Ak‘s and Bk‘s?
• Find an orbit that has no hole. 
• Make a video, showing the planets moving around the star, as well as the orbital movement of the centroid of the planets. Make it interactive (like an API), allowing the users to input some parameters.
• Can you compute the shape of the hole is n = 3, and prove its existence?
• Try to categorize all possible orbits when n = 3 or n = 4.

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About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). He recently opened Paris Restaurant, in Anacortes. You can access Vincent’s articles and books, here.

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Using Automation in AI with Recent Enterprise Tools

A Classic Data Science Project and approach looks like this:

Data Science (DS) and Machine Learning (ML) are the spines of today’s data-driven business decision-making.

From a human viewpoint, ML often consists of multiple phases: from gathering requirements and datasets to deploying a model, and to support human decision-making—we refer to these stages together as DS/ML Lifecycle. There are also various personas in the DS/ML team and these personas must coordinate across the lifecycle: stakeholders set requirements, data scientists define a plan, and data engineers and ML engineers support with data cleaning and model building. Later, stakeholders verify the model, and domain experts use model inferences in decision making, and so on. Throughout the lifecycle, refinements may be performed at various stages, as needed. It is such a complex and time-consuming activity that there are not enough DS/ML professionals to fill the job demands, and as much as 80% of their time is spent on low-level activities such as tweaking data or trying out various algorithmic options and model tuning. These two challenges — the dearth of data scientists, and time-consuming low-level activities — have stimulated AI researchers and system builders to explore an automated solution for DS/ML work: Automated Data Science (AutoML). Several AutoML algorithms and systems have been built to automate the various stages of the DS/ML lifecycle. For example, the ETL (extract/transform/load) task has been applied to the data readiness, pre-processing & cleaning stage, and has attracted research attention. Another heavily investigated stage is feature engineering, for which many new techniques have been developed such as deep feature synthesis, one-button machine, reinforcement learning-based exploration, and historical pattern learning.

However, such work often targets only a single stage of the DS/ML lifecycle. For example, AutoWEKA can automate the model building and training stage by automatically searching for the optimal algorithm and hyperparameter settings, but it offers no support for examining the training data quality, which is a critical step before the training starts. In recent years, a growing number of companies and research organizations have started to invest in driving automation across the full end-to-end AutoML system. For example, Google released its version of AutoML in 2018. Startups like H2O and Data Robot both introduced products. There are also Auto-sklearn and TPOT from the open-source community. Most of these systems aim to support end-to-end DS/ML automation. Dataiku became a leader in Enterprise AI tool which is giving us an all total new view too. There are many other platforms coming up.

Current capabilities are focused on the model building and data analysis stages, while little automation is offered for the human-labor-intensive and time-consuming data preparation or model runtime monitoring stages. Moreover, these works currently lack an analysis from the users’ perspective: Who are the potential users of envisioned full automation functionalities? Are they satisfied with the AutoML’s performance, if they have used it? Can they get what they want and trust the resulting models?

At the end of this article, we will see how Dataiku & other AI solutions or platforms can give us many solutions.

Data Science Team and Data Science Lifecycle:

Data science and machine learning are complex practices that require a team with interdisciplinary background and skills. For example, the team often includes stakeholders who have deep domain knowledge and own the problem; it also must have DS/ML professionals who can actively work with data and write code. Due to the interdisciplinary and complex nature of the DS/ML work, teams need to closely collaborate across different job roles, and the success of such collaboration directly impacts the DS/ML project’s final output model performance.

Data Science Automated:

 It often starts with the phases of requirement gathering & problem formulation, followed by data cleaning and engineering, model training and selection, model tuning and ensembles, and finally deployment and monitoring. Automated Data Science (AutoML) is the endeavor of automating each stage of this process separately or jointly. The Data cleaning stage focuses on improving data quality. It involves an array of tasks such as missing value imputation, duplicate removal, noise correction, invalid values, and other data collection errors. AlphaClean and HoloClean provide representative examples of automated data cleaning. Automation can be achieved through approaches like reinforcement learning, trial and error methodology, historical pattern learning, and more recently through knowledge graphs. The Hyperparameter selection stage is used to fine-tune a model or the sequence of steps in a model pipeline. Several automation strategies have been proposed, including grid search, random search, evolutionary algorithms, and finally sequential model-based optimization methods. AutoML has witnessed considerable progress in recent years, in research as well as application in commercial products. Various AutoML research efforts have moved beyond the automation on one specific step. Joint optimization, a type of Bayesian-Optimization-based algorithms, enables AutoML to automate multiple tasks together. For example, AutoWEKA , Auto-sklearn , and TPOT all automate the model selection, hyperparameter optimization, and assembling steps of the data science pipeline. The result coming out of such AutoML system is called a “model pipeline”. A model pipeline is not only about the model algorithm; it emphasizes the various data manipulation actions (e.g., filling in missing value) before the model algorithm is selected, and the multiple model improvement actions (e.g., optimize the best values for model’s hyperparameters) after the model algorithm is selected.

Amongst these advanced AutoML systems, Auto-sklearn and Auto-WEKA are two open-source efforts. Both use the sequential-parameter-optimization algorithm. This optimization approach generates model pipelines by selecting a combination of model algorithms, data pre-processors, and feature transformers. Their system architectures are both based on the same general-purpose-algorithm-configuration framework, SMAC (Sequential Model-based Optimization for General Algorithm Configuration). In applying SMAC, Auto-sklearn and Auto-WEKA translate the model selection problem into a configuration problem, where the selection of the algorithm itself is modeled as a configuration.

Auto-sklearn supports warm-starting the configuration search by trying to generalize configuration settings across data sets based on historic performance information. Leverage historical information to build a recommender system that can navigate the historical information more efficiently. This approach is effective in determining a pipeline but is also limited because it can only select from a pre-defined and limited set of pre-existing pipelines. To enable AutoML to dynamically generate pipelines instead of only selecting pre-existing pipelines were inspired by AlphaGo Zero and its pipeline generation algorithm. modeled as a single-player game. So the pipeline is built iteratively by selecting a set of actions (insertion, deletion, replacement) and a set of pipeline components (e.g., logarithmic transformation of a specific predictor or” feature”). Extend this idea to use a reinforcement learning approach, so that their final pipeline outcome is an ensemble of multiple sub-optimal pipelines, but that final pipeline has a state-of-the-art model performance when compared to other approaches. Model ensembles have become a mainstay in ML with all recent Kaggle competition-winning teams relying on them. Many AutoML systems generate a final output model pipeline as an ensemble of multiple model algorithms instead of a single algorithm. More specifically, the ensemble algorithm includes:

1) Ensemble selection, which is a greedy-search-based algorithm that starts with an empty set of models, incrementally adds a model to the working set and selects that model if such addition results in improving the predictive performance of the ensemble

2) And, genetic programming algorithm, which does not create an ensemble of multiple model algorithms, but it can compose derived model algorithms. An advanced version of the genetic programming algorithm that uses multi-objective genetic programming to evolve a set of accurate and diverse models via introducing bias into the fitness function accordingly.

Data Science Characters, Their Current and Favoured Levels of Automation, and Different Stages of Lifespan:

In this section, let’s take a closer look at data science workers’ current level of automation, and what their preferred level of automation would be in the future. The current and preferred levels of automation is also associated with different stages of the DS lifecycle. It is observed that there is a clear gap between the levels of automation in their current DS work practices and preferred automation in the future. Most respondents reported that their current work is at automation L0, which is “No automation, human performs the task”. Some participants reported L1 or even L2 levels of automation (i.e., “human-directed automation” and “system-suggested human-directed” respectively) in their current work practice, and these automation activities happened often in the more technical stages of the DS lifecycle (e.g., data pre-processing, feature engineering, modeling building, and model deployment). These findings echo the existing trend that AutoML system development and algorithm research work focus much more on the technical stages of the lifecycle. However, these degrees of automation are far less than what the respondents desired: participants reported that they prefer at least L1 automation across all stages, with the only exceptions in requirement gathering and model verification where a number of participants still prefer L0. The median across all the stages is L2 – human-guided automation.

In some of the stages, when asked about future preferences, a few respondents indicated that they want full automation (L4) over other automation levels. The Model deployment stage had the highest full automation preference, but it was still not the top choice of people. On average across stages, full automation was only preferred by 14% of respondents. Human-directed automation (L2) was preferred by most respondents (42%), while system-directed automation (L3) was the second preference (22%). This suggests that users of AutoML would always like to be informed and have control of the system to some degree. A full end-to-end automated DS lifecycle was not what people wanted. End-to-end AutoML systems should always have human-in-the-loop. There seems to be a trend in the results of the preferred levels of automation: in general, the desired levels of automation increases along with the lifecycle stages moving from less technical ones (e.g., requirement gathering) into the more technical ones (e.g., model building). L2 (System-suggested human-directed automation), L3 (system-directed automation), and L4 (full automation) are the levels when the human shifts some control and decision power to the system, and the AutoML system starts to take agency. And L2, L3, and L4 together took the majority vote of each stage. In summary, these results suggest that people definitely welcome more automation to help with their DS/ML projects, and there is a huge gap between what they use today and what they want tomorrow. However, people also do not want over-automated systems in human-centered tasks (i.e., requirement gathering, model verification, and decision optimization).

To have an in-depth examination of people’s preferred levels of automation, finer-grained level of automation preferences across different stages, and across different roles. It is worth noting that participants across all roles agreed that requirement gathering should remain a relatively manual process. Data scientists, both expert and citizen scientists, tend to be cautious about automation. Only a few of them expressed interest in fully automating (L4) feature engineering, model building, model verification, and runtime monitoring. For example, in model verification, they prefer system-suggested and human-directed (L3) and system-directed automation (L2) over too little automation (L1/L0) or too much automation (L4).

AI-Ops had a more conservative perspective toward automation than other roles, they only have a majority preference of full automation (L4) in the model deployment stage, some on data acquisition and data pre-processing, but for the rest stages, they would strongly prefer to have human involvement. Above all, there is a clear consensus among different roles that model deployment, feature engineering, and model building are the places where practitioners want higher levels of automation. This suggests an opportunity for researchers and system builders to prioritize automation work on these stages. On the other hand, all roles agree that less automation is desired in requirements gathering and decision optimization stages, this may be due to the fact these stages are currently labor-intensive human efforts, and it is difficult for our participants to even imagine how the automation would look like in these stages in the future.

Data Governance & Why?

Data governance is not a new idea – as long as data has been collected, companies have needed some level of policy and oversight for their management. Yet it largely stayed in the context, as businesses weren’t using data at a scale that required data governance to be top of mind. In the last few years, and certainly in the face of 2020’s tumultuous turn of events, data governance has shot to the forefront of discussions both in the media and in the boardroom as businesses take their first steps towards Enterprise AI. Recent increased government involvement in data privacy (e.g. GDPR and CCPA) has no doubt played a part, as have magnified focuses on AI risks and model maintenance in the face of the rapid development of machine learning. Companies are starting to realize that data governance has never really been established in a way to handle the massive shift toward democratized machine learning required in the age of AI. And that with AI comes new governance requirements. Today, the democratization of data science across the enterprise and tools that put data into the hands of the many and not just the elite few (like data scientists or even analysts) means that companies are using more data in more ways than ever before. And that’s super valuable; in fact, the businesses that have seen the most success in using data to drive the business take this approach. 

But it also gifts new challenges – mainly that businesses’ IT organizations are not able to handle the demands of data democratization, which has created a sort of power struggle between the two sides that slows down overall progression to Enterprise AI. A fundamental shift and organizational change into a new type of data governance, one that enables data use while also protecting the business from risk, is the answer to this challenge and the topic of this section.

Most enterprises today identify data governance as a very important part of their data strategy, but often, it’s because poor data governance is risky. And that’s not a bad reason to prioritize it; after all, complying with regulations and avoiding bad actors or security concerns is critical.   However, governance programs aren’t just beneficial because they keep the company safe – their effects are much wider: 

Governance isn’t about just keeping the company safe; data and AI governance are essential components to bringing the company up to today’s standards, turning data and AI systems into a fundamental organizational asset. As we’ll see in the next section, this includes wider use of data and democratization across the company.

AI Governance and Machine Learning Model Management?

Data governance traditionally includes the policies, roles, standards, and metrics to continuously improve the use of information that ultimately enables a company to achieve its business goals. Data governance ensures the quality and security of an organization’s data by clearly defining who is responsible for what data as well as what actions they can take (using what methods).

With the rise of data science, machine learning, and AI, the opportunities for leveraging the mass amounts of data at the company’s disposal have exploded, and it’s tempting to think that existing data governance strategies are sufficient to sustain this increased activity. Surely, it’s possible to get data to data scientists and analysts as quickly as possible via a data lake, and they can wrangle it to the needs of the business?

But this thinking is flawed; in fact, the need for data governance is greater than ever as organizations worldwide make more decisions with more data.  Companies without effective governance and quality controls at the top are effectively kicking the can down the road, so to speak, for the analysts, data scientists, and business users to deal with — repeatedly, and in inconsistent ways. This ultimately leads to a lack of trust at every stage of the data pipeline. If people across an organization do not trust the data, they can’t possibly confidently and accurately make the right decisions

IT organizations historically have addressed and been ultimately responsible for data governance. But as businesses move into the age of data democratization (where stewardship, access, and data ownership become larger questions), those  IT teams have often been put in the position incorrectly of also taking responsibility for information governance pieces that should really be owned by business teams. Because the skill sets for each of these governance components are different. Those responsible for data governance will have expertise in data architecture, privacy, integration, and modeling. However, those on the information governance side should be business experts — they know:

• What the data is?
• Where does the data come from?
• Is the source of the data correct?
• How valid this data is?
• How and why the data is valuable to the business?
• How the data can be used in different business contexts?
• What data security level is there?
• Has the business validated this data?
• Is it enough data?
• How the data ultimately should be used, which in turn, is the crux of a good Responsible AI strategy?

In brief, data governance needs to be teamwork between IT and business stakeholders.

Shifting from Traditional Data Governance to Data Science & AI Governance Model:

An old-style data governance program oversees a range of activities, including data security, reference and master data management, data quality, data architecture, and metadata management. Now with the growing adoption of data science, machine learning, and AI, there are new components that should also sit under the data governance. These are namely machine learning model management and Responsible AI governance. Just as the use of data is governed by a data governance program, the development and use of machine learning models in production require clear, unambiguous policies, roles, standards, and metrics. A robust machine learning model management program would aim to answer questions such as:

• Who is responsible for the performance and maintenance of production machine learning models?
• How are machine learning models updated and/or refreshed to account for model drift (deterioration in the model’s performance)?
• What performance metrics are measured when developing and selecting models, and what level of performance is acceptable to the business?
• How are models monitored over time to detect model deterioration or unexpected, anomalous data and predictions?
• How are models audited, and are they explainable to those outside of the team developing them?

It’s worth noting that machine learning model management will play an especially important role in AI governance strategies in 2020 and beyond as businesses leverage Enterprise AI to both recovers from and develop systems to better adapt to future economic change.

Responsible AI Governance and Keys to Defining a Successful AI Governance Strategy:

The second new aspect for a modern governance strategy is the oversight and policies around Responsible AI. While it has certainly been at the center of media attention as well as public debate, Responsible AI has also at the same time been somewhat overlooked when it comes to incorporating it concretely as part of governance programs.

Perhaps because data science is referred to as just that — a science — there is a perception among some that AI is intrinsically objective;  that is, that its recommendations, forecasts, or any other output of a machine learning model isn’t subject to individuals’ biases. If this were the case, then the question of responsibility would be irrelevant to AI – an algorithm would simply be an indisputable representation of reality.

This misconception is extremely dangerous not only because it is false, but also because it tends to create a false sense of comfort, diluting team and individual responsibility when it comes to AI projects. Governance around Responsible AI should aim to address this misconception, answering questions such as:

• What data is being chosen to train models, and does this data have a pre-existing bias in and of itself?
• What are the protected characteristics that should be omitted from the model training process (such as ethnicity, gender, age, religion, etc.)?
• How do we account for and mitigate model bias and unfairness against certain groups?
• How do we respect the data privacy of our customers, employees, users, and citizens?
• How long can we legitimately retain data beyond its original intended use?
• Are the means by which we collect and store data in line not only with regulatory standards but with our own company’s standards?

Following key Methods & Ethics to follow to define AI Governance Strategy:

A Top-Down and Bottom-Up Plan:

Every AI governance program needs executive sponsorship. Without strong support from leadership, it is unlikely a company will make the right changes (which — full transparency — are often difficult changes) to improve data security, quality, and management. At the same time, individual teams must take collective responsibility for the data they manage and the analysis they produce. There needs to be a culture of continuous improvement and ownership of data issues. This bottom-up approach can only be achieved in tandem with top-down communications and recognition of teams that have made real improvements and can serve as an example to the rest of the organization.

Stability Between Governance and Enablement:

Governance shouldn’t be a blocker to innovation; rather, it should enable and support innovation. That means in many cases, teams need to make distinctions between proofs-of-concept, self-service data initiatives, and industrialized data products, as well as the government, needs surrounding each. Space needs to be given for exploration and experimentation, but teams also need to make a clear decision about when self-service projects or proofs-of-concept should have the funding, testing, and assurance to become an industrialized, operationalized solution

Excellence at its Heart:

In many companies, data products produced by data science and business intelligence teams have not had the same commitment to quality as traditional software development (through movements such as extreme programming and software craftsmanship). In many ways, this arose because five to ten years ago, data science was still a relatively new discipline, and practitioners were mostly working in experimental environments, not pushing to production.  So, while data science used to be the wild west, today, its adoption and importance have grown so much that standards of quality applied to software development need to be reapplied. Not only does the quality of the data itself matter now more than ever, but also data products need to have the same high standards of quality — through code review, testing, and continuous integration/continuous development (CI/CD)  that traditional software does if the insights are to be trusted and adopted by the business at scale.

Model Organization:

As machine learning and deep learning models become more widespread in the decisions made across industries, model management is becoming a key factor in any AI Governance strategy. This is especially true today as the economic climate shifts, causing massive changes in underlying data and models that degrade or drift more quickly. Continuous monitoring, model refreshes, and testing are needed to ensure the performance of models meets the needs of the business. To this end, MLOps is an attempt to take the best of DevOps processes from software development and apply them to data science. 

Transparency and Accountable AI:

Even if, per the third component, data scientists write tidy code and adhere to high-quality standards, they are still giving away a certain level of control to complex algorithms. In other words, it’s not just about the quality of data or code, but making sure that models do what they’re intended to do.  There is growing scrutiny on decisions made by machine learning models, and rightly so.  Models are making decisions that impact many people’s lives every day, so understanding the implications of the decisions they make and making the models explainable is essential (both for the people impacted and the companies producing them). Open source toolkits such as Aequitas3, developed by the University of Chicago, make it simpler for machine learning developers, analysts, and policymakers to understand the types of bias that machine learning models bring.

 Data & AI Governance Weaknesses:

Data and AI governance aren’t easy; as mentioned in the introduction, these programs require coordination, discipline, and organizational change, all of which become even more challenging the larger the enterprise. What’s more, their success is a question not just of successful processes, but a transformation of people and technology as well. That is why despite the clear importance and tangible benefit of having an effective AI governance program, there are several pitfalls that organizations can fall into along the way that might hamper efforts:

A governance program without senior sponsorship means policies without “teeth,” so to speak. Data scientists, analysts, and business people will often revert to the status quo if there isn’t top-down castigation when data governance policies aren’t adhered to and recognition for when positive steps are taken to improve data governance.

If there isn’t a culture of ownership and commitment to improving the use and exploitation of data throughout the organization, it is very difficult for a data governance strategy to be effective. As the saying goes, “Culture eats strategy for breakfast.” Part of the answer often comes back to senior sponsorship as well as communication and tooling

A lack of clear and widespread communication around data governance policies, standards, roles, and metrics can lead to a data governance program being ineffective. If employees aren’t aware or educated around what the policies and standards are, then how can they do their best to implement them? 

Training and education are hugely important pieces of good data and AI governance.  It not only ensures that everyone is aware of policies but also can help explain practically why governance matters. Whether through webinars, e-learning, online documentation, mass emails, or videos, initial and continuing education should be a piece of the puzzle.

A centralized, controlled environment from which all data work happens makes data and AI governance infinitely simpler. Data science, machine learning, and AI platforms   can be a basis for this environment, and essential features include at a minimum contextualized documentation, a clear delineation between projects, task organization, change management, rollback, monitoring, and enterprise-level security

 Which solutions or platforms are Trending for Enterprise AI or specialist data scientists to make life easier in this area?

Not too hot, not too cold, but just right – these are the platforms that achieve a mix between being loved by techies and non-techies alike. This middle ground offers a strong focus on citizen data science users and heavy integration with programming languages, allowing for flexibility and in-platform collaboration between people who can code, and people who can’t. These platforms making life easy for data scientists in terms of executing a whole lengthy Data science projects steps and doing automation in the works.

Azure Machine Learning:

Microsoft is well known for seamlessly integrating their product offerings with each other, making Azure Machine Learning an attractive option for users who are already working in an existing Azure stack.  Azure Machine Learning’s main offering is the ability to build predictive models in-browser using a point-and-click GUI. Though the ability to write code directly in the platform is not available, specialized data scientists will be excited by Microsoft’s Python integration. The Azure ML library for Python allows users to normalize and transform data in Python themselves using familiar syntax, and call Azure Machine Learning models as needed using loops. Not only this, but Azure Machine Learning also integrates with existing Python ML packages (including scikit-learn, TensorFlow and PyTorch). For users familiar with these tools, distributed cloud resources can be used to productions results at scale, just like any other experiment. As of the writing of this article, Azure Machine Learning also offers an SDK for R in a public preview (i.e. non-productionisable) mode, which is expected to improve over time.

H2O Driverless AI:

H2O Driverless AI is the main commercial enterprise offering of the company H2O.ai, offering automated AI with some pretty in-depth algorithms, including advanced features like natural language processing. A strong focus on model interpretability gives users multiple options for visualizing algorithms in charts, decision trees, and flowcharts. H2O.ai is already well-known in the industry for its fully open-source ML platform H2O, which can be accessed as a package through existing languages like Python and R, or in notebook format. H2O Driverless AI and H2O currently exist as fairly separate products, though it is potential for these to be further integrated in the future. Partnerships with multiple cloud infrastructure providers (including AWS, Microsoft, Google Cloud, and Snowflake) make H2o Driverless AI a product to watch in the coming years.

DataRobot:

DataRobot offers a tool that is intended to empower business users to build predictive models through a streamlined point-and-click GUI. The tool focuses very heavily on model explainability, by generating flowcharts for data normalization and automated visuals for assessing model outcomes. These out-of-the-box visuals include important exploratory charts like ROC curves, confusion matrices, and feature impact charts. DataRobot’s end-to-end capabilities were significantly bolstered by the company’s acquisition of Paxata (a data preparation platform) in December 2019, which has since been integrated with the DataRobot predictive platform. The company also boasts some big-name partnerships, including Qlik, Tableau, Looker, Snowflake, AWS, and Alteryx. DataRobot does offer Python and R packages, which allow many of the service’s predictive features to be called through code, though the ability to directly write code in the DataRobot platform and collaborate with citizen data scientist users is not currently available (as of the writing of this article). DataRobot’s new MLOps service also provides the ability to deploy independent models written in Python/R (in addition to models developed in DataRobot), as part of a robust operations platform that includes deployment tests, integrated source control, and the ability to track model drift over time.

RapidMiner:

RapidMiner Studio is a drag & drop GUI-based tool for building predictive analytics solutions, with a free version providing analysis of up to 10,000 rows. In-database querying and processing are available through the GUI, but programmers/analysts also have the option to query in SQL code. The ETL process is handled by Turbo Prep, which offers a point & clicks data preparation (as well as direct export to .qvx, for users who want to import results into Qlik). The cool thing about RapidMiner is the integration with Python & R modules, available as supported extensions in the RapidMiner Marketplace, through which coders & non-coders can both collaborate on the same project. For coders working on a local Python instance, the RapidMiner library in Python also allows for the administration of projects and resources of a RapidMiner instance. For cloud-based scaling of models, RapidMiner also allows containerization using Docker and Kubernetes.

Alteryx:

An existing big player in the ETL tool market, Alteryx is used to build data transformation workflows in a GUI, replacing the need to write SQL code. Alteryx has significantly stepped up its game in recent years with its integrated data science offering, allowing users to build predictive models using their drag-and-drop “no-code” approach. The ability to visualize and troubleshoot results at every step of the operation is a huge plus, and users familiar with SQL should transition easily to the logical flowchart style of the ETL, removing the need for complex nested scripts. Alteryx has a fantastic online community with plenty of resources, and direct integration with both Python and R through out-of-the-box tools. The Python tool includes common data science packages such as pandas, scikit-learn, matplotlib, numpy, and others which will be familiar to the Python enthusiasts of this world.

Dataiku:

Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data eorts via collaborative, elastic, and responsible AI, all at enterprise scale. Hundreds of companies use Dataiku to underpin their essential business operations and ensure they stay relevant in a changing world. One quick look at the Dataiku website will make it immediately clear that this is a platform for everyone in the data space. Dataiku offers both a visual UI and a code-based platform for ML model development, along with a host of features that make Dataiku a highly sustainable platform in production. Data scientists will be delighted with not only the Python & R integration, but the flexibility in being able to code either using the embedded code editor, or their favorite IDE like Jupyter notebooks or RStudio. The Dataiku DSS (Data Science Studio) is available as an HTTP REST API, allowing users to manage models, pipelines, and automation externally. Data analysts will be excited by the multitude of plugins available – including PowerBI, Looker, Qlik. qvx export, Dropbox, Excel, Google Sheets, Google Drive, Google Cloud, OneDrive, SharePoint, Confluence, and many more. Automatic feature engineering, generation, and selection, in combination with the visual UI for model development, allows ML to sit firmly within the reach of these citizen data scientists.

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DSC Weekly Digest 17 August 2021

As data systems become more complex (and far-reaching), so too does the way that we build applications. On the one hand, enterprise data no longer just means the databases that a company owns, but increasingly refers to broad models where data is shared among multiple departments, is defined by subject matter experts, and is referenced not only by software programs but complex machine learning models.

The day where a software developer could arbitrarily create their own model to do one task very specifically seems to be slipping away in favor of standardized models that then need to be transformed into a final form before use. Extract, transform, load (ETL) has now given way to extract, load, transform (ELT). There’s even been a shift in best practices in the last couple of decades, with the idea that you want to move core data around as little as possible and rely instead upon increasingly sophisticated queries and transformation pipelines.

At the same time, the notion is growing that the database, in whatever incarnation it takes, is always somewhat local to the application domain. The edge is gaining in intelligence and memory, indeed, most databases are moving towards in-memory stores, and caching is evolving right along with them.

The future increasingly is about the query. For areas like machine learning, the query ultimately comes down to making models so that they are not only explainable, but tunable as well. The query response is becoming less and less about single the answer, and more about creating whole simulations.

At the same time, the hottest databases are increasingly graph databases that allow for inferencing, the surfacing of knowledge through the subtle interplay of known facts. Bayesian analysis (in various forms and flavors) has become a powerful tool for predicting the most likely scenarios, with queries here having to straddle the line between utility and meaningfulness. What happens when you combine the two? I expect this will be one of the hottest areas of development in the coming years.

SQL won’t be going away – the tabular data paradigm is still one of the easiest ways to aggregate data – but the world is more than just tables. A machine learning model, at the end of the day, is simply an index, albeit one where the keys are often complex objects, and the results are as well. A knowledge graph takes advantage of robust interconnections between the various things in the world and is able to harness that complexity, rather than get bogged down by it.

It is this that makes data science so interesting. For so long, we’ve been focused primarily on getting the right answers. Yet in the future, it’s likely that the real value of the evolution of data science is learning how to ask the right questions.

In media res,

Kurt Cagle
Community Editor,
Data Science Central

To subscribe to the DSC Newsletter, go to Data Science Central and become a member today. It’s free! 

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Top Use Cases of Natural Language Processing in Healthcare

Better access to data-driven technology as procured by healthcare organisations can enhance healthcare and expand business endorsements. But, it is not simple for the company enterprise systems to utilise the many gigabytes of health and web data. But, not to worry, the drivers of NLP in healthcare are a feasible part of the remedy.

What is NLP in Healthcare? 

The NLP illustrates the manners in which artificial intelligence policies gather and assess unstructured data from the language of humans to extract patterns, get the meaning and thus compose feedback. This is helping the healthcare industry to make the best use of unstructured data. This technology facilitates providers to automate the managerial job, invest more time in taking care of the patients, and enrich the patient’s experience using real-time data.

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You will be reading more in this article about the most effective uses and role of NLP in healthcare corporations, including benchmarking patient experience, review administration and sentiment analysis, dictation and the implications of EMR, and lastly the predictive analytics. 

14 Best Use Cases of NLP in Healthcare

Let us have a look at the 14 use cases associated with Natural Language Processing in Healthcare:

    1. Clinical Documentation

The NLP’s clinical documentation helps free clinicians from the laborious physical systems of EHRs and permits them to invest more time in the patient; this is how NLP can help doctors. Both speech-to-text dictation and formulated data entry have been a blessing. The Nuance and M*Modal consists of technology that functions in team and speech recognition technologies for getting structured data at the point of care and formalised vocabularies for future use

The NLP technologies bring out relevant data from speech recognition equipment which will considerably modify analytical data used to run VBC and PHM efforts. This has better outcomes for the clinicians. In upcoming times, it will apply NLP tools to various public data sets and social media to determine Social Determinants of Health (SDOH) and the usefulness of wellness-based policies.

    2. Speech Recognition

NLP has matured its use case in speech recognition over the years by allowing clinicians to transcribe notes for useful EHR data entry. Front-end speech recognition eliminates the task of physicians to dictate notes instead of having to sit at a point of care, while back-end technology works to detect and correct any errors in the transcription before passing it on for human proofing. 

The market is almost saturated with speech recognition technologies, but a few startups are disrupting the space with deep learning algorithms in mining applications, uncovering more extensive possibilities.

    3. Computer-Assisted Coding (CAC)

CAC captures data of procedures and treatments to grasp each possible code to maximise claims. It is one of the most popular uses of NLP, but unfortunately, its adoption rate is just 30%. It has enriched the speed of coding but fell short at accuracy.

    4. Data Mining Research

The integration of data mining in healthcare systems allows organizations to reduce the levels of subjectivity in decision-making and provide useful medical know-how. Once started, data mining can become a cyclic technology for knowledge discovery, which can help any HCO create a good business strategy to deliver better care to patients.

    5. Automated Registry Reporting

An NLP use case is to extract values as needed by each use case. Many health IT systems are burdened by regulatory reporting when measures such as ejection fraction are not stored as discrete values. For automated reporting, health systems will have to identify when an ejection fraction is documented as part of a note, and also save each value in a form that can be utilized by the organization’s analytics platform for automated registry reporting.

    6. Clinical Decision Support

The presence of NLP in Healthcare will strengthen clinical decision support. Nonetheless, solutions are formulated to bolster clinical decisions more acutely. There are some areas of processes, which require better strategies of supervision, e.g., medical errors. 

According to a report, recent research has indicated the beneficial use of NLP for computerised infection detection. Some leading vendors are M*Modal and IBM Watson Health for NLP-powered CDS. In addition, with the help of Isabel Healthcare, NLP is aiding clinicians in diagnosis and symptom checking. 

    7. Clinical Trial Matching

Using NLP and machines in healthcare for recognising patients for a clinical trial is a significant use case. Some companies are striving to answer the challenges in this area using Natural Language Processing in Healthcare engines for trial matching. With the latest growth, NLP can automate trial matching and make it a seamless procedure. 

One of the use cases of clinical trial matching is IBM Watson Health and Inspirata, which have devoted enormous resources to utilise NLP while supporting oncology trials.

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    8. Prior Authorization

Analysis has demonstrated that payer prior authorisation requirements on medical personnel are just increasing. These demands increase practice overhead and holdup care delivery. The problem of whether payers will approve and enact compensation might not be around after a while, thanks to NLP. IBM Watson and Anthem are already up with an NLP module used by the payer’s network for deducing prior authorisation promptly.

    9. AI Chatbots and Virtual Scribe

Although no such solution exists presently, the chances are high that speech recognition apps would help humans modify clinical documentation. The perfect device for this will be something like Amazon’s Alexa or Google’s Assistant. Microsoft and Google have tied up for the pursuit of this particular objective. Well, thus, it is safe to determine that Amazon and IBM will follow suit. 

Chatbots or Virtual Private assistants exist in a wide range in the current digital world, and the healthcare industry is not out of this. Presently, these assistants can capture symptoms and triage patients to the most suitable provider. New startups formulating chatbots comprise BRIGHT.MD, which has generated Smart Exam, “a virtual physician assistant” that utilises conversational NLP to gather personal health data and compare the information to evidence-based guidelines along with diagnostic suggestions for the provider.  

Another “virtual therapist” started by Woebot connects patients through Facebook messenger. According to a trial, it has gained success in lowering anxiety and depression in 82% of the college students who joined in.

    10. Risk Adjustment and Hierarchical Condition Categories

Hierarchical Condition Category coding, a risk adjustment model, was initially designed to predict the future care costs for patients. In value-based payment models, HCC coding will become increasingly prevalent. HCC relies on ICD-10 coding to assign risk scores to each patient. Natural language processing can help assign patients a risk factor and use their score to predict the costs of healthcare.

    11. Computational Phenotyping

In many ways, the NLP is altering clinical trial matching; it even had the possible chances to help clinicians with the complicatedness of phenotyping patients for examination. For example, NLP will permit phenotypes to be defined by the patients’ current conditions instead of the knowledge of professionals.

To assess speech patterns, it may use NLP that could validate to have diagnostic potential when it comes to neurocognitive damages, for example, Alzheimer’s, dementia, or other cardiovascular or psychological disorders. Many new companies are ensuing around this case, including BeyondVerbal, which united with Mayo Clinic for recognising vocal biomarkers for coronary artery disorders. In addition, Winterlight Labs is discovering unique linguistic patterns in the language of Alzheimer’s patients.

    12. Review Management & Sentiment Analysis

 NLP can also help healthcare organisations manage online reviews. It can gather and evaluate thousands of reviews on healthcare each day on 3rd party listings. In addition, NLP finds out PHI or Protected Health Information, profanity or further data related to HIPPA compliance. It can even rapidly examine human sentiments along with the context of their usage. 

Some systems can even monitor the voice of the customer in reviews; this helps the physician get a knowledge of how patients speak about their care and can better articulate with the use of shared vocabulary. Similarly, NLP can track customers’ attitudes by understanding positive and negative terms within the review. 

    13. Dictation and EMR Implications

On average, EMR lists between 50 and 150 MB per million records, whereas the average clinical note record is almost 150 times extensive. For this, many physicians are shifting from handwritten notes to voice notes that NLP systems can quickly analyse and add to EMR systems. By doing this, the physicians can commit more time to the quality of care. 

Much of the clinical notes are in amorphous form, but NLP can automatically examine those. In addition, it can extract details from diagnostic reports and physicians’ letters, ensuring that each critical information has been uploaded to the patient’s health profile. 

    14. Root Cause Analysis

Another exciting benefit of NLP is how predictive analysis can give the solution to prevalent health problems. Applied to NLP, vast caches of digital medical records can assist in recognising subsets of geographic regions, racial groups, or other various population sectors which confront different types of health discrepancies. The current administrative database cannot analyse socio-cultural impacts on health at such a large scale, but NLP has given way to additional exploration. 

In the same way, NLP systems are used to assess unstructured response and know the root cause of patients’ difficulties or poor outcomes.

This article was originally published here

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Using Automated Builds in ModelOps

In this installment of the ModelOps Blog Series, we will transition from what it takes to build AI models to the process of deploying into production. Think of this as the on ramp for extracting value from your AI investments—moving your model out of the lab and into an environment where it can provide new insights for your organization or add value to customers.

Front and center is the concept of continuous integration (CI) and continuous deployment (CD). This methodology can be applied to automate the process of releasing AI models in a reproducible and reliable manner. Get ready to walk away with everything you need to know in order to leverage containers to formalize and manage AI models within your organization.

The starting point for the deployment process is a source-control, versioned AI model. Need a refresher on how to get to the starting point? Review the previous blogs in this series which cover how to produce a model with responsibly sourced data and software development best-practices around model training and versioning.

Data Preparation: Putting the Right Process in Place for AI
Model Training: Our Favorite Tools in the Shed
Model Versioning. Reduce Friction. Create Stability. Automate.

Living in a containerized world

For ModelOps, containers are a standard way to package AI models to leverage in production. In essence, a container is a running software application comprised of the minimum requirements necessary to run the application, including an operating system, application source code, system dependencies, programming language libraries, and runtime. Containers are comprised of static container images that outline each resource and instruction required to bring the application to life within the container.

Your organization might already embrace containers or microservices in more traditional software and DevOps settings. But did you know containers can also be applied to the packaging and distribution of AI models for data science teams? That’s good news for leaders investing in the development of AI models because it means that models—and their difficult to install dependencies—can be packaged up into containers that can run anywhere. Upskilling and familiarizing your data science team with container technology will empower them to easily package their own AI models and participate in a robust CI/CD process—which can reduce your timeline to realize return on your AI investments.

Extending the notion of an AI model

Modzy extends the container concept to power AI models running in production. AI models are deployed through an open, standardized template that encourages developers to expose the functionality of their AI model while ensuring it can run anywhere (see example.) Keeping the focus on production deployment, a single set of best practices can be put into place. Without standardization, model developers often work in disparate development environments creating challenges with reproducing or handing off models from the research team to the production team.

Standardizing the process for how models are packaged ensures data scientists don’t need deep expertise in either software engineering or DevOps. However, they can reap the benefits of these disciplines. Data scientists can focus on developing new models to solve important problems instead of hacking together patchwork solutions every time a model is ready for deployment.

Ideally, you want a suite of standard templates for popular machine learning frameworks such as TensorFlow and PyTorch, giving data scientists the flexibility to use their tools of choice. This is a capstone to the process of model training described in Model Training: Our Favorite Tools in the Shed. Developers can make individualized decisions during the development of each model without compromising a streamlined process for model development and release.

Leveraging CI for automated builds

A CI/CD process that takes source code for a freshly developed AI model and automatically produces a containerized version of that model is the gold standard for build automation practices. Establishing such a process means that deployment is fully reproducible with no manually curated steps that could introduce error and consume valuable developer time. Modern CI frameworks such as Jenkins, CircleCI, or GitHub Actions are essential tools in the CI/CD pipeline. They keep your team’s development velocity high by allowing your data scientists to focus on developing their models instead of solving complicated deployment nuances—translating directly to an accelerated completion.

Modzy’s approach combines continuous integration best practices with containerization to build container images for models. By automating the build process, model versioning best practices are deployed to the models ensuring each model is traceable to a specific version of secure, tested code. (Check out where this was highlighted in the Model Versioning: Reduce Friction. Create Stability. Automate blog. Once a model developer checks in their code to version control, the AI model image is built, scanned, and tested making it ready for any hand-off or deployment. This simple, convenient process makes automated builds something developers will seek out, rather than a burdensome business practice.

Empowering teams of data scientists and machine learning engineers through robust practices of CI and containerization will serve to bridge the gap between AI development and deployment at scale.

Visit modzy.com to learn more.

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Growing Role Of AI Chatbots In Healthcare Sector

Since technology has started disrupting the healthcare sector, innovations are included to follow a patient-driven approach. Recently, AI chatbots have been the new hue in the market and have caught the attention of health experts. As a vital part of healthcare IT solutions, AI chatbots work faster and more effectively to resolve patients’ queries than traditional calling systems. In this article, we have presented all the benefits of including AI chatbots in the healthcare system. So, without further ado, let’s begin!

Challenges in Healthcare

The Healthcare industry needs official reports of patients, and thus, user privacy is always at stake. Moreover, since they know they are talking to an intelligent and smart chatbot, patients find it challenging to build their trust and share their personal information with an online chatbot.

Thus, different data safety methods must be implemented to stay ahead of cybercrimes that steal user’s private data and follow best practices for reliable AI implementation. If you have a healthcare platform, business owners always look at implementing suitable data safety measures to strengthen their platform’s resistance to cybersecurity.

AI Chatbots As Part Of Healthcare IT Solutions

AI Chatbots are mainly the software that is developed using machine learning algorithms, such as NLP. Therefore, they are super helpful in engaging a conversation with any user for fulfilling the sole aim of providing excellent real-time patient assistance

.

All the medical assistants in the healthcare industry are switching to AI-enabled tools that impart superb assistance at low costs. Suppose you tend to use any healthcare app or visit some medical website and find a conversation with any medical expert who sounds human. In that case, it is an intelligent AI chatbot catering to your specific needs.

All the patients like to speak to real qualified doctors or medical specialists, and AI chatbots can achieve this. The best part is that many chatbots that comprise complex self-learning algorithms are known for maintaining a comprehensive human-like conversation and assist perfectly.

Some Prominent Use Cases Of AI Chatbots In Healthcare

Given below are all the top use cases of AI chatbots in the future healthcare industry.

1. Boost Patient Engagement

Since chatbots are intelligent, they are helpful in providing accurate suggestions according to the specific interest of the patient. Not just this, they both keep a regular touch with both health officials as well as patients. Thus, they serve as the bridge between patients and health professionals to offer simplified consulting.

2. Top-Notch Customer Service

AI Chatbots were launched to redefine the customer service arena in all industries. For example, in healthcare, these chatbots are pretty helpful in scheduling appointments, sharing feedback, issuing reminders, and noting the information regarding refilling prescription medications.

3. Offer updates to patients

Patients who are looking forward to getting surgery can easily connect with the chatbot to help in the preparation of the surgery. Once the appointment is booked, the chatbot confirms the appointment through a confirmation mail or text message.

Even during and after the surgery, different details regarding the surgery are shared with the patient’s family. It will also help in sharing educational materials about the surgery.

4. Voice assistance

AI-enabled chatbots are not just text-based assistants; they also assist through their voice. All the health-related guidance and results are communicated via chatbots. Many times, textual conversations may become a little difficult to discuss a patient’s problems. Thus, to understand and render the best support with customized solutions, AI chatbots are extensively used to solve patient’s issues of any size. If you get to experience this service, you will fall for such an innovative and advanced way of connecting both the patient and the medical representative.

5. Informative Chatbots

Some chatbots are also present to proffer excellent information on different medical issues. For example, automated details on other conditions get popped up quickly on all health-centric apps. A plethora of medical websites use these types of chatbots to render quick information on different topics efficiently. For example, if you want to use customer support to know about breast cancer, the chatbots will provide information based on top links on the search engine.

Conclusion

AI chatbots hold a bright future in the healthcare domain. Along with other healthcare IT solutions, AI chatbots will further revolutionize and create a strong hole in this realm. Don’t worry; these chatbots do not delve deeper into providing medical aid; however, they are emerging as a reliable medium to boost patient’s engagement with their respective health providers.

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The Benefits of Remote Work for Knowledge Workers in 2022

Image Credit: remote work, credit Unsplash

Time for a Change of Scenery on the Job?

Nothing has been quite so transformative during the last two years as the way we work. The future of work and our mobility as knowledge workers has been augmented by digital transformation. Ready to have the best of both worlds? Well now you can. 

This article will explore many of those familiar clichés of the benefits of remote work for the knowledge worker. Many forget it’s not just for the individual programmer, software engineer or back-end knowledge worker, but also is better for organizations, firms, and their industrial psychology. 

The pandemic brought us many challenges including lockdowns, physical separation, uncertainty, loneliness, a shift in how we spend our time and relate, even with family and, for some of the lucky few of us, remote work. So what are the benefits of remote work? Do the benefits outweigh the potential mental health and salary costs? While this is an immensely personal weighing, society continues to churn from the office in the ‘great resignation’. 

Remote work certainly seems tethered with the future of work for various knowledge workers including those in programming, datascience, machine learning and so forth. 

Heading into the second half of 2021 and into 2022, how will remote work feel as the new normal and what benefits will we no longer be able to live without. Do you know someone in the office of in management who is skeptical of the benefits for the firm or for workers? Share with them this article. 

More Life-Work Balance: More Quality Shared Time

I have been enjoying work from home. It has allowed me more time with the family. No commute in the morning, no commute in the evening. Still put the hours in, but my breaks consist of family time. When I was in the office, I was lucky to get an hour with the family on a weekday. Enjoy the new house. Have fun exploring CO, so much to do there. We miss it. Hoping we stay remote, but as of now, that is planned to mostly end in September for me. – Richard Litsky.

More time with family is the single most reported benefit of more hybrid work or remote work arrangements. This can lead to healthier family units, marked improvements in spousal relationships and more social support when facing the ups and downs of life.

Digital Nomad Lifestyle: More Housing Opportunities

The pandemic really has shown that remote working is a viable option in so many ways including having the ability to buy a place of your own, still have an awesome career and maintain a healthy work life balance. – Ben Forrest.

For knowledge workers and those typically in data science, programming, software engineering, artificial intelligence and technology, remote work has given people the flexibility to work from anywhere in the country, presenting new opportunities they might not otherwise have had. 

This has led to many knowledge workers buying a home during the pandemic, moving and finding new ways to live where they want, instead of working where they have to work in a designated office. Some companies have even abandoned physical offices altogether.

Less Commute Stress

In studies that say WFM is more productive, commute time is often mentioned as one of the key variables. However, for mental health, commute stress can also be significant for many workers who go to the office. Realistically however, for many remote workers less commute stress may be off-set by technological loneliness, especially among young workers who are single.

Improved mental health is often cited as a reason for a preference for remote work. However not everyone enjoys or tolerates zoom meetings, not in the same way as in-person meetings.

Moving to a More Rural or Scenic Setting

Many remote workers choose to leave large cities and live in more comfortable, scenic and pleasant surroundings.

It seems remote working has more perks than just avoiding a bad commute. On a recent backpacking trip in Oregon and California, I met a woman in a little town called Etna. She used to work in Buffalo, NY, but she moved to California to be near family and continues to support a large IT services company remotely. There’s no way she could have a job like that in a rural town without working from her home.… – Bonnie Nicholls.

Money Savings

With hybrid work or remote work employees report spending less on commuting and on more professional business attire than they would if they were going to the office every single day of the week. Also fewer coffees and other daily expenses mean savings that can add up and be spent on family, entertainment or office supplies at home instead. However this can also be offset by some employers actually reducing salary expenses if employees choose to go full on remote work. This is common if you move from Silicon Valley to somewhere else where the cost of living is cheaper.

More Time With Pets

While more time with loved ones can take many forms, for some Millennials or GenZ who do not have a family yet this means more time with pets which can boost mental health and productivity. Working remotely means you can recharge with micro breaks at home in a way you wouldn’t necessarily do with ear phones on in an open-space office (blast from the past!).

Significant Benefits for Companies Too

While it’s very easy to spot the benefits for the individual employee, what might be the benefits for a company? Some of these include:

• Less time spent in meetings and needless office chatter
• Higher overall productivity (this has been backed up by numerous studies)
• Better engagement
• Higher employee retention
• Profitability – not having to rent an office is a huge saving.

More Organizational Decentralization (sometimes called Location Independence)

Businesses also function differently when they use remote workers with many reporting better team bonds and less hierarchy.

However location independence is actually having a broader range of potential jobs that aren’t tied to a single geography. This relates to a greater democratization of opportunity and geographically decentralized workforce.

GenZ really prefer the freelancer life and some will not hold down 9 to 5 jobs like we used to, while some of them might even country hop often in a digitally nomadic lifestyle. Of course all of this presumes that you have skills that are translatable to remote work and preferably that you are a knowledge worker that software engineering or data science afford.

Improved Diversity and Inclusion

There’s some preliminary data that suggests remote teams are more inclusive, in that they embrace minorities better and promote more equality in the “workplace”.

Due to location independence, roles can be filled with more expanded criteria. For instance by hiring people from different socioeconomic, geographic, and cultural backgrounds and with different perspectives better teams and products end up being created.

This is a benefit of remote work that could truly impact society in a major way, but more studies are required to back up the initial data.

More Freedom for Employees

Remote work offers workers more freedom that they appreciate so firms that adopt hybrid or remote work conditions may see significantly higher employee retention and higher morale.

Employees report having more freedom to customize their life-work balance the way they want, including making their own schedules. For knowledge work in software development, data science, AI and software engineering this may be an essential requirement, given the diversity among their employees.

This freedom extends to introverts who might be sensitized to the noise and constant interruptions of a physical office. More than anything this perceived freedom implies the company trusts the employee to do their best, even with limited supervision. This kind of trust can mean a pay it forwards reciprocity comes into effect where the remote worker becomes a more positive influence on his team and its productivity.

Better Collaborative Technology

Before the pandemic remote work was something for freelancers and the self-employed. However with remote work becoming the mainstream for knowledge workers, the technology took a sudden leap that empowers better communication, collaboration and support for employees.

The Zoom and Microsoft Teams era among many other tools suggests that these collaboration software services also make for a better support system. The collaboration at a distance approach can mean meetings are shorter, check-ins are more personal and flexibility is higher for all the surprises that life can bring.

That the tech is more collaborative makes remote work easier than it did in the past. Every process has been streamlined, and the way teams function has also been improved with new kinds of digital workforce data. As the hybrid workforce becomes the new normal, this collaborative technology is set to become exponentially better.

Greater Ability to Handle Multiple Projects

Remote work and WFM also allows your side-gigs to prosper or working for several clients at once if you are a freelancer. Remote work enables you to multitask in different ways and as a knowledge worker that could mean your DIY investing, your side-gig, your personal projects and your actual work role.

Remote work gives you the ability to be more productive because you can be yourself in your own customized office space. This ultimately means likely making more money due to the ability to include side projects in your daily activities.

Remote work in this way allows you to fulfill your human potential in a manner that you might not get when stuffed in an office all day long.

Making Friends in the Digital Workforce

Microsoft is creating LinkedIn news promoting remote work and helping knowledge workers navigate it, here and here. With Microsoft Teams and their suite of software tools, it’s almost like a product placement. In the ‘AI for good” work making friends at work could also, frankly, be easier. It will no longer be about who you are physically most close to in a physical office setting.

Companies understand in a remote work environment the importance of Slack and making space for social chat. A bonded team is a team that understands itself better and can be more productive. As a result firms are finding out what works to create a friendlier culture for a digital workforce.

During the pandemic when mental health issues were rising, workers created or organized weekly virtual hangouts, which they took offline as people got vaccinated. Some met one-on-one or attended company-wide off-site events like baseball games. As we adapted to lockdowns, we had to find ways to socialize in a way that helped us cope with the various adjustments.

Starting a remote job can be hard, but many people have found new ways to forge work friendships during the pandemic. There’s no longer a proverbial water cooler to generate casual encounters, and some younger workers have never had a physical office at all. But they’ve overcome the awkwardness of the digital chat box to initiate meaningful, if often distanced, friendships. – Krithika Varagur.

For those that don’t like office politics or water-cooler chit chat, remote work actually feels like a blessing. For extraverts, digital software tools make it easier to communicate.

Less Peer-Influenced Overtime Work

While remote workers initially tend to blur the lines between home life and work, as they get used to the new routine they may work less peer influenced overtime. This actually promotes emotional well-being. With some studies suggesting a 4-day work week is most productive, forced overtime is not something knowledge workers should be doing much of.

While some firms, startups and technology companies might expect a significant amount of overtime as part of the job, remote workers have more flexibility with regards to this.

Less forced overtime means improved work-life balance. Many remote workers actually do too much overtime which reduces their job satisfaction. Keeping to a fixed routine helps.

Better Access to Jobs

Remote work can help those who are underemployed or retired bycv 2350/*, working in an easier environment. Some retired knowledge workers get bored and want to continue to actively contribute to society and remote work can afford them that opportunity.

Others who are disabled, those who are caregivers, those who are suffering an illness and others in difficult mental health periods have more flexibility in working remotely. Remote work overall improves accessibility to jobs and professional opportunities.

Sustainable Living Choice

Remote work means less commuting and as such may represent a smaller carbon footprint for your family and you as an individual. Digital transformation technologies might allow major companies to attain carbon neutrality faster.

So by choosing a lifestyle of remote work, you aren’t just saving time and perhaps money, but having a positive environmental impact in your small way. For the U.S. a whopping 7.8 billion vehicle miles aren’t traveled each year for those who work at least part time from home, 3 million tons of greenhouse gases (GHG) are avoided, and oil savings reach $980 million.

So the behavior modification of remote work can have small sustainable environmental impacts. It can also contribute to less traffic congestion, less noise pollution and thus less air pollution in your local region, if remote work becomes a more collective choice in how we approach the future of work as a society.

Sensory Tenderness

For introverts who prefer to work in total silence and in peace and tranquility, working from home has the obvious benefit of a more customized and quiet work space. Busy offices, interruptions, noise, wasteful meetings and crowded conditions create stress that reduces work focus and general feelings of well-being.

From a perspective of too much stimulation, remote work can create a more ideal balance between the physical environment and the preferences of the employee to work in an atmosphere that’s more conducive for them to give it their best, think creatively and come up with novel solutions to work problems.

Greater Access to Talent Pools

For SMBs and firms in general, remote work allows for greater access to a talented pool of workers, no matter where they live in the country of operations or the entire world at large. For particular positions employers want to find the right skills but also the right cultural fit for their organization.

As such, remote work allows HR to improve the talent level of their employees thus benefiting the company and the industry as a whole. For organizations, having an extended reach of talent can make the difference in becoming profitable compared to just scraping by.

Improved Productivity

It’s hard to argue against being more productive. An overwhelming majority of studies demonstrate how remote work allows an organization and the individual to be more productive. As companies adopt remote work for knowledge workers, especially their white collar talent and software engineers, key metrics of productivity and efficiency go up and constructs related to innovation go up.

One of the biggest lessons of the pandemic has been how remote work equals more productivity which might hypothetically eventually lead developed countries to adopt a 4-day work week.

More Immunity from the Great Resignation

With the economic recovery after the pandemic, many employees want to change jobs. Those organizations that are the most pro remote work will retain their best talent. The data might in the end show how remote work means less job hopping for young talent, a key demographic competitive companies need to retain to excel in the future.

Remote work in the future might be one of the necessary requirements to create a successful company culture. With remote work and a more automated HR with AI, companies can become more agile and cost efficient in terms of recruitment and retention of their talent.

Remote work will also allow new data to be used by AI systems that assist in recruiting and on-boarding of employees. This data will ultimately improve the success rate of HR and hiring (talent acquisition) for a company. Remote work will thus likely accelerate how some aspects of HR become automated with chatbots and advanced AI systems.

Improved Internal Communications

As remote work becomes normalized the software systems around collaboration and communication improve and can be tweaked to suit an organization’s needs. This improved communion leads to increased teamwork, loyalty, job satisfaction and productivity. That might be why remote work in the end leads to more productivity in teams and individual performance.

Technology in this way can reduce office politics while augmenting the key metrics of communication with more data on the kinds of communication that matter the most. Remote work is thus an example of how digital transformation software and more knowledge workers going remote might improve a company’s long term prospects.

Conclusion on Adoption of Remote Work

The advent of remote work might lead to a cascade of changes for knowledge workers in a future work revolution never seen before. The arrival of the corporate metaverse might mean significant changes in how we work and navigate work-life balance as a generational shift.

Knowledge workers are in 2021 insisting on having the ability to work hybrid WFM or in some cases take jobs across the country as remote workers. Digital transformation associated with remote work will also create new data science and programming jobs all around the world and change the future of digital collaboration itself. 

A large majority of knowledge workers do not want to go back to the office much more than one or two times a week, if at all. Therefore it’s somewhat likely that the benefits of remote work make it a sustainable trends, both for the individual worker and for businesses. 

Your Turn on Remote Work?

What do you think? What has been your experience so far with remote work in a world in partial or recurrent lockdowns? Is it something you want implemented in the future for your next job or the future of work itself? Will you insist on remote work as a condition for applying for a position in your field?

Share a comment below. 

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The Growing Importance of Data and AI Literacy – Part 1

This is the first part of a 2-part series on the growing importance of teaching Data and AI literacy to our students.  This will be included in a module I am teaching at Menlo College but wanted to share the blog to help validate the content before presenting to my students.

Wow, what an interesting dilemma. Apple plans to introduce new iPhone software that uses artificial intelligence (AI) to churn through the vast collection of photos that people have taken with their iPhones to detect and report child sexual abuse.  See the Wall Street article “Apple Plans to Have iPhones Detect Child Pornography, Fueling Priva…” for more details on Apple’s plan.

Apple has a strong history of working to protect its customers’ privacy.  It’s iPhone is basically uncrackable which has put it at odds with the US government. For example, the US Attorney General asked Apple to crack their encrypted phones after a December 2019 attack by a Saudi aviation student that killed three people at a Florida Navy base. The Justice Department in 2016 pushed Apple to create a software update that would break the privacy protections of the iPhone to gain access to a phone linked to a dead gunman responsible for a 2015 terrorist attack in San Bernardino, Calif. Time and again, Apple has refused to build tools that break its iPhone’s encryption, saying such software would undermine user privacy.

In fact, Apple has a new commercial where they promote their focus on consumer privacy (Figure 1).

Figure 1:  Apple iPhone Privacy Commercial

Now, stopping child pornography is certainly be a top society priority, but at what cost to privacy. This is one of those topics where the answer is not black or white. A number of questions arise including:

• How much personal privacy is one willing to give up trying to halt this abhorrent behavior?
• How much do we trust the organization (Apple in this case) in their use of the data to stop child pornography?
• How much do we trust that the results of the analysis won’t get into unethical players’ hands and used for nefarious purposes?

And let’s be sure that we have thoroughly vetted the costs associated with the AI model’s False Positives (accusing an innocent person of child pornography) and False Negatives (missing people who are guilty of child pornography), a topic that I’ll cover in more detail in Part 2.

Data literacy starts by understanding what data is.  Data is defined as the facts and statistics collected to describe an entity (height, weight, age, location, origin, etc.) or an event (purchase, sales call, manufacturing, logistics, maintenance, marketing campaign, social media post, call center transaction, etc.). See Figure 2.

Figure 2:  Visible and Hidden Data from Grocery Store Transaction

But not all data is readily visible to the consumer.  For example, from the Point-of-Sales (POS) transaction on the right side of Figure 2, there is data for which the consumer may not be aware that is also captured and/or derived when the POS transaction is merged with the customer loyalty data.

It is the combination of visible and hidden data that organizations (grocery stores in this example) use to identify customer behavioral and performance propensities (an inclination or natural tendency to behave in a particular way) such as:

• What products do you prefer? And which ones do you buy in combination?
• When and where do you prefer to shop?
• How frequently do you use coupons and for what products?
• How much does price impact your buying behaviors?
• To what marketing treatments do you tend to respond?
• Do the combinations of products indicate your life stage?
• Do you change your purchase patterns based upon holidays and seasons?

The sorts of customer, product, and operational insights (predicted behavioral and performance propensities) are only limited by the availability of granular, labeled, consumer data and the analysts’ curiosity.

Now there is nothing illegal about the blending of consumer purchase data with other data sources to uncover and codify those consumer insights (preferences, patters, trends, relationships, tendencies, inclinations, associations, etc.).  The data these organizations collect is not illegal because you as a consumer have signed away your exclusive right to this engagement data.

There is a growing market of companies that are buying, aggregating, and reselling your personal data.  There are at least 121 companies such as Nielsen, Acxiom, Experian, Equifax and CoreLogic whose business model is focused on purchasing, curating, packaging, and selling of your personal data.  Unfortunately, most folks have no idea how much data these data aggregators are gathering about YOU (Figure 3)!

Figure 3: Source: “Here are the data brokers quietly buying and selling your personal information”

Yes, the level of information that a company like Acxiom captures on you and me is staggering.  But it is not illegal. You (sometimes unknowingly) agree to share your personal data when you sign up for credit cards and loyalty cards or register for “free” websites, newsletters, podcasts, and mobile apps.

Companies then combine this third-party data with their collection of your personal data (captured through purchases, returns, marketing campaign responses, emails, call center conversations, warranty cards, websites, social media posts, etc.) to create a more complete view of your interests, tendencies, preferences, inclinations, relationships, and associations.

One needs to be aware of nefarious organizations who are capturing data that is not protected by privacy laws. For example, iHandy Ltd distributes the “Brightest Flashlight LED” Android app with over 10 million installs.  Unfortunately for consumers, iHandy Ltd is headquartered in Beijing, China where the consumer privacy laws are very lax compared to privacy laws in America, Europe, Australia, and Japan (Figure 4).

Figure 4: iHandy Ltd Brightest Flashlight LED Android App

But wait, there’s more.  A home digital assistant like Amazon Alexa or Google Assistant and their always-on listening capabilities are listening and capturing EVERYTHING that is being said in your home… ALL THE TIME! 

And if you thought that conversational data was private, guess again!  Recently, a judge ordered Amazon to hand over recordings from an Echo smart speaker from a home where a double murder occurred.  Authorities hope the recordings can provide information that could put the murderer behind bars. If Amazon hands over the private data of its users to law enforcement, it will also be the latest incident to raise serious questions about how much data technology and social medai companies collect about their customers with and without their knowledge, how that data can be used, and what it means for your personal privacy.

Yes, the world envisioned by the movie “Eagle Eye”, with its nefarious, always-listening, AI-powered ARIIA is more real than one might think or wish.  And remember that digital media (and the cloud) have long memories.  Once you post something, expect that it will be in the digital ecosystem F-O-R-E-V-E-R.

All this effort to capture, align, buy, and integrate all of your personal data is done so that these organizations can more easily influence and manipulate you.  Yes, influence and manipulate you. 

Companies such as Facebook, Google, Amazon, and countless others leverage your personal propensities, that is, the predicted behavioral propensities gleaned from the aggregation of your personal data, to sell advertising and influence your behaviors.  Figure 5 shows how Google leverages your “free” search requests to create a market for advertisers willing to pay to place their products and messages at the top of your search results.

Figure 5: Here’s How Google Shares, Monetizes, and Exploits Your Data

All of your personal data helped Google achieve $147 billion in digital media revenue in 2020.  Not a bad financial return for a “free” customer service.

What can one do to protect their data? The first step is awareness of where and how organizations are capturing and exploiting your personal data for their own monetization purposes. Be aware of what data you are sharing via the apps on your phone, the customer loyalty programs to which you belong, and your engagement data on websites and social media.  But even then, there will be questionable organizations who will skirt the privacy laws to capture more of your personal data for their own nefarious acts (spam, phishing, identity theft, ransomware, and more).

In Part 2 of this series, will dive into the next aspect of this critical data literacy conversation – AI Literacy and AI Ethics.

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Has Working at Home Actually Led to Longer Hours?

Image Source: Pexels

When the Covid-19 pandemic first hit, and a vast majority of the population was given the ‘stay at home’ order by Prime Minister Boris Johnson. At the beginning and on the surface, the transition to enforced working from home which begun last year initially (and on its surface) allowed for much-needed flexibility for professional workers who were able to work from home during the COVID-19 crisis and shown that a remote workforce can continue working productively. However, the quieter demons and negative aspects of the experience such as loneliness and low mental health, lack of collaboration, and overall burnout have emerged and continue to emerge as the ‘work from home’ saga goes on.

“The concept of the ‘office’ has been recently shown to actually be a healthy one, which was until recently, fairly unknown. The ‘9 to 5’ working day and separate environment for work allows for a work-life balance,” says Adam Nelson, a writer from OXEssays and UKWritings. The separate environment allows for a division between work life and home life that has not been viable during the Covid-19 pandemic. Studies have shown that working from home over the last year has led to longer hours, longer meetings, and spending more time using online and social media communication channels.

In a Los Angeles survey conducted this year, over sixty percent of professionals now working at home due to Covid-19 confessed that they do at least some work almost every weekend. In addition to this, 45 percent of Los Angeles remote workers say they now work more hours during the week than prior to the pandemic. This survey also discovered that parents who now worked remotely were actually more likely to work weekends and longer days than those surveyed without children.

“Though the thought of remote work was expected to provide flexibility to workers during the pandemic, it has in reality made disconnecting from work almost impossible,” explains Ian Jackson, an HR manager at Studydemic and Via Writing. The downside to an entire company’s operation being able to function online and through communication channels such as Zoom and Google Meets, is that the only way to completely avoid work is to turn your phone off (something that rarely happens in our social media charged day and age).

NordVPN Teams, a New York VPN company has noted that, “Remote working has led to a two and a half hour increase in each average working day”. For example, in the UK, employees that would usually leave their jobs at 5 or 6 o’clock at the latest are now logging off their online platforms around 8pm. Remote working has not only led to longer working days, but it has also produced shorter lunch breaks, and a sharp spike in working hours on so-called ‘family holidays’.

Over half of UK employees report working more than the hours that they are technically paid for during remote working, and a staggering 74% report stress, fatigue, or burnout during the so called ‘flexible and relaxing’ work-from-home period. Working at home has led to longer hours, with some employees even allocating their previous commuting time to work as well. Another recent research study discovered that remote workers were undertaking one entire month’s more work per year, compared with before the pandemic.

So, does this mean the death of the office? While work can now be conducted entirely at home, this does not mean it will be productive in the long term. Yes, remote workers are now working longer and more for their employers currently; but the fatigue and burnout reported by current home workers will eventually lead to a massive downfall in productivity. As well as this, the creative exchange of ideas and lack of effective work-life balance has meant that working at home has come at both great personal and professional cost. Working from home this past year has been continuously proven to lead to longer hours, and it will continue to be detrimental to both personal and professional health and development unless we do our best to return to our offices as soon as it is safely possible.

About the Author:

Emily Henry is a professional article writer at Best assignment writing services and Essay Help Services who enjoys being involved in multiple all over the world. She is a work-from-home mother; and she enjoys traveling the world, reading and researching management topics and attending business training courses. She also contributes her writing skills at StudentWritingServices.

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The Most Costly Big Data Mistakes You Should Avoid

Marketers are now using data-driven analysis and approaches to utilize accurate research data for their marketing campaigns. Not every data and its digging strategy is relevant and accurate enough to be adopted by a company. The question is how to avoid unauthentic data driving techniques and data blunders. 

“Data is the new oil for IT industry”

What does big data comprise?

IMG Source: Researchgate.net

• Volume
• Velocity
• Variety
• Veracity

These four V interprets data quality in terms of its quantity, certainty, and categories. 

What is the data-driven marketplace and what importance does it hold? 

Researching and collecting data for the targeted customers and audience can help in providing accurate services and information. The brand can better understand what its customers’ demands and expectations are. This helps in driving better leads, conversion leads, and successful campaigns.

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – By Geoffrey Moore, an American Management Consultant and Author

How can big data mistakes damage the company’s marketing profile?

Wrong data-driven methods can result in a poor response from the target audience with reduced engagements. The marketing team will predict erroneous insights that will damage the prospects of marketing campaigns. A survey was conducted in 2013 for big data evolution, where 81% of the companies mentioned big data strategy as their top five priorities and 55% of them reported failure and implementing big data objectives.

Here’s a golden guide on how to identify and avoid significant data mistakes to keep your marketing game running on fleek.

Ignoring quality-driven data

Researching and accumulating data is not the only requirement as quality overshadows quantity when it comes to customer service. Making the data qualitative in terms of its relevance, confidentiality, and accuracy. The data should be sorted, organized, and cross-checked in all the protocols of data quality control.

“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.” – By Angela Ahrendts

The fallacious data analytics can develop ineffective insights for marketing campaigns, damaging the perspective and motivation. Infiltrated data can drain the marketing budget into waste by not fulfilling the objective. Take the following precautions: 

• Follow the taxonomy governance 
• Avoid using meta tags
• Focus on versions 
• Scan data on regular basis to find potential threats

It’s vital for businesses to plan their marketing budget based on quality and accurate data. Otherwise, they risk missing out on opportunities to grow and expand. 

Arranging your database into sub-datasets

The concept of organizing data in a single database in making marketing decisions can help in removing inconsistency. A large data set can average out errors. Small datasets can escalate the chances of inconsistencies in marketing campaigns and can ruin marketing decisions.

To get more accurate insights and to boost the effectiveness of a marketing campaign, the data analyst must focus on building data as relevant and precise as possible.

Source: Ingrammicro.com

From government to energy sectors, every sector is investing to build its big data market. The financial sector has reported owning $6.4B of market value in 2015. 

Data with no marketing goal 

Researching and analyzing data with no intention is baseless and a waste of money and effort. Data digging has always to be done under some criteria to meet the requirements of target customers. With no specific analysis intention about data, it would definitely result in ambiguity, inaccuracy, and irrelevant insights. The marketing and execution department of the organization focuses on analyzing from a 360 perspective about framing a committed strategy with its all execution phases. 

It’s important to analyze metrics to save the budget and efforts of data analysts. In parallel, it’s vital to track the performance of those metrics to see their worth and effect on the marketing campaigns.

The sample data statistics highlight what significance this one-minute data collection shows. The analysis shows 2.5 million Facebook posts were made in 1 minute, showing massive engagement. 72 hours of YouTube videos were uploaded. 

How to surmount this? 

Before gearing up the analyzing operation, jot down the goals related to data selection and marketing benchmarks. Through this technique, you can use all the resources to find the optimum data set.

Weak or no data architecture plan 

Quality, confidentiality, quantity, relevancy, and accuracy make up a solid data set. A database with no framework and plan is like a balloon full of data but no rope tied with it. A structure-less data set can result in discrepancies and ambiguities in performing analysis actions. It will become a menace to store, retrieve, and save the data. 

“Data is a precious thing and will last longer than the systems themselves.” – By Tim Berners-Lee, Inventor of the World Wide Web

Enterprises these days have employees and contractors doing data entry work from home because the scale of these entries is too vast. This unorganized data can suffer from constraints and potential threats. The solution lies in building a Data Architecture Plan with a smooth concrete of storage and retrieving. Data-driven enterprises must automate the process of turning signal intelligence into a decision or action, and the way to do this is by creating automated processes powered by AIOps using Robotic Data Automation(RDA). There are many online and offline storage tools such as cloud and edge computing, coupled with low-latency tools.

Improper data visualization 

Presentation and proper showcasing of information and raw data come under the art of data visualization. Apart from organizing and storing your database, it’s equally important to make it presentable and visible. It comes under the skills and responsibilities of marketers and data analysts. 

A database that fails to deliver the required information has lost the effort and sense of data presentation. Your Data Architecture plan also comprises data allocation and presentation which allows easy conveying of information. 

The data should be organized in sections to make it understandable for the audience. The caliber and requirements of the audience should be considered while designing the presentation of data. Haphazardly organized data can result in visual displeasure. You can use software design tools to creatively save and arrange your data with the help of infographics and visuals.

A team lacking the major analytics skills

Most of the data science and mining companies compromise on improving their teams’ skills and expertise according to the upgrade in technology. There are predictive maintenance tools like those that sensors collect to accumulate massive databases in a single space. All the data analysts must be called for regular analysis training to update them with essential tools for developing accurate data insights. Even if you’ve employed freelancers who were looking for typing jobs from home, you need to arm them with appropriate skills. Most companies and marketers confuse big data by measuring their technical strength of handling it such as storage and computing devices. Whereas, they should be focusing on effective big data initiatives. Once they decide on business strategies for big data, they can allocate supportive technology with it. 

“No great marketing decisions have ever been made on qualitative data.” – By John Sculley, CEO of Apple Inc

Zero collaboration between data analysis and business development team

Once your company’s data is set to be cooked for marketing, finance, and business strategies, a lack of a business development sector will bring in no value in the data. A proactive BI team will invest in building its data in terms of its thoughtful resources. They explore and work on driving importance from the collected data for the organization. A Bi team is dedicated to working for the managing, execution, acquisition, and quality-driven utilization of data. Big data requires different forms of treatment of data isolation, management, and authentication which requires some operational procedures. 

Comprehend your data set trends 

Data collection, analysis, and response show a significant trend for data analysts to understand and work accordingly. When tracking and analyzing the data set, there are various connections in the data that connect different parameters. You can see them as trends or see the interlinks between them. 

These trends are not always trustworthy but can help marketers to some extent in some phenomena. The best way to find misleading trends is to look from where the trend comes in. The cause of the trend will signify the credibility of the specific trend. 

The bottom line

Massive data analysis and research is a significant sub-department of data marketing strategy. Considering its significance in mind, it has to be error-free as it has a major contribution in developing marketing campaigns and customer-related approaches. To design a flawless data mining procedure for your company, focus on its quality, architecture, and database. As mentioned above, say NO to small data sets as it invites massive inconsistencies. 

We can’t argue more on how data has impacted our lives as digital data storage has overpassed 40 zettabytes by the end of 2020. With 70% of the population with their own mobile phone, each individual is contributing some useful data stats to the company, brand, or government. It’s wise enough to say in 2021 that your data commands and control. No one and none of the organizations can control the flow and effect of data. With thousands of copies of a single piece of data all over the world, it’s impossible to control privacy and copyrights.

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