You must have data scientists in your organization! Data’s the next oil! Data scientists will tell you what all this data means!! If you don’t want to be left behind, hire your experts now, before they’re all gone!!!
The hyperbole in the tech press about data scientists has exceeded a fever pitch, with the upshot being that there are a lot of young (and not so young) people with PhDs in data analytics that expect to be swept up to nosebleed salaries the moment that the ink on their diplomas dries. The reality, however, is considerably more muddled.
A data scientist, at the end of the day, is an applied mathematician. Their focus may be either in statistical analysis or in solving complex differential equations, typically through the use of specialized graphs called kernels. Most data scientists are also subject matter experts in a given domain, and the tools that they use may or may not be consistent from one domain to another. An economist, for instance, uses a very different set of notations than a biological researcher or an experimental physicist. Because of that, generalists, those who know the tools but not necessarily the domains, may very well not be what your organization needs if you are expecting subject matter expertise.
Analysts may or may not be data scientists. An analyst has domain expertise and the ability to both understand a situation at a strategic level and to make recommendations about how best to proceed in that area to maximize the goals of the organizations. They are, in essence, modelers, and such models can both make sense of past activity and, with predictive analytics, suggest future activity. However, this is all dependent upon having the data that’s needed when it’s needed, and upon having a clear set of objectives about what specifically needs to be modeled – and why.
This means that effective data management means going beyond the data in your databases, and building, through inferences, data – knowledge – about the world outside of the organization’s walls. It means strategic investment in solid data sources or the willingness to invest in data gathering operations, and it means keeping a much of that data contextual as possible. If your organization is not willing to make that investment, then don’t hire data scientists.
I do not believe the hype that if you are not a data-driven company that you will fail. Some, perhaps many organizations aren’t data-driven, not in any meaningful sense. This is not to say that if you are a data-driven organization you can get by without good analysts who are adept with the tools and understand their domain. It’s just important to recognize that a data scientist, or a whole department of them, is not going to transform your business into a data-driven one if the strategic will to do so isn’t there. Data science efforts usually fail not because of poor data science, but due to poor strategic management.
This is why we run Data Science Central, and why we are expanding its focus to consider the width and breadth of digital transformation in our society. Data Science Central is your community. It is a chance to learn from other practitioners, and a chance to communicate what you know to the data science community overall. I encourage you to submit original articles and to make your name known to the people that are going to be hiring in the coming year. As always let us know what you think.
When it comes to technology projects, 54 percent of businesses worldwide say digital transformation is their top priority.
Digital transformation has many advantages, including increased sales and stock values. However, financial gains take precedence over all other considerations, as these are the company’s primary objectives.
Companies like Target, Best Buy, and Hasbro were quick to recognize the value of digital technology and have leveraged its full potential. However, some businesses failed to implement the right digital transformation strategy, which eventually led them to shut down their operations.
According to Harvard Business Review, 52 percent of Fortune 500 firms from 2000 are no longer in business now. The reason behind? They couldn’t keep up with how the world was changing, and they couldn’t keep up with the latest tech trends.
Hence, I’ve listed some practical Do’s and Don’ts for new leaders to implement digital transformation in this article.
Do’s and Don’ts for making the journey towards digital transformation a success
Things new leader need to focus for persuasive digital transformation – Do’s
Map Out A Clear Strategy
To create a solid digital transformation plan roadmap, you must first conduct a thorough business analysis. Simultaneously, you can concentrate on your objectives and assess how they will impact your current business model. Determine the digital transformation vision and consider how it can enhance customer service and company culture.
Customer engagement, employee empowerment, operational optimization, and business model change should all be part of a successful digital transformation roadmap.
Evaluate Expense
As a leader, you can begin crunching numbers once everyone is on board. How much expense do you have to devote to transform your business digitally?
It’s essential to remember that digital transformation is a long-term project. It’s a futuristic approach to how you do business that will affect all aspects of your company.
On the other hand, the budget should be measured, keeping all of the business areas in mind that will benefit from digital transformation. By specifying goals and defining the scope of the process, you can implement the best digital transformation strategy.
Research and Incorporate New Technology
As a business leader, you must remain informed on emerging technology and trends to expand and meet consumer demands. Digital transformation services are not a one-time fix but rather a process that leads to a commitment to digital relevance. However, each business must be careful when introducing new technology, as not all of them can best fit its objectives.
An effective cognitive computing system will aid in digital transformation and place business on the road to being a successful digital enterprise.
“ According to Research and Markets – From 2019 to 2025, the digital transformation market is projected to rise at a CAGR of 23%, bringing the total market value to $3.3 trillion.”
Assess The Progress
Once you’ve started implanting your digital transformation journey, keep track of your progress or monitor essentials steps. Adopting the pinnacles of lean thinking will enable you to track your progress. This involves providing value to the end-user, cultivating a culture of continuous improvement, reducing redundancy through operations (e.g., via automation), and concentrating on people, process, and output iteration.
Points a leader should evade while implementing digital transformation – Don’ts
Overlook The Security
With more and more apps migrating to the cloud, it’s imperative to know what experts thought about cloud storage security. Surprisingly, one out of every five (22%) individuals believes that data kept in the cloud is not as secure as data saved locally. Furthermore, 30% of the participants believe that data is not currently stored securely within the organization.
Hence, during the digital transformation journey, a leader must closely watch the data security aspects. Don’t think of your network as a security barrier. Using the same network you used in 2010 isn’t going to help you. And don’t expect to find your ideal frameworks and diagnostics tools on the first try.
Investing In Unwanted Tech Or Tools
Don’t be over-enthused and purchase or invest in all the tools that come across during the process. Understand your business requirements and accordingly look for technology, tools that would help run the business operations smoothly. It’s also unnecessary to use a paid software or service, initially looking for free tools in the market. This will assist you in understanding the effectiveness, features of the software. As a leader, you must ensure that every process, tool, or strategy built is efficient, reliable, and scalable.
Stop Experimenting (once digital transformation is done, they stop)
Never stop experimenting with trying new strategies or technology. Several digital transformation agencies enable the business to move ahead by introducing cutting-edge tech solutions on a timely basis. It’s a fact that tech is ever-evolving; the new tech that was talk-of-the-town today will be labeled ‘old-school’ the next day. So don’t get satisfied or stop once you’ve started the journey towards digital transformation.
Let The Past Data Go To Waste
These technologies have the advantage of being data-driven from the start. The adoption of digital technologies is part of a more significant shift toward a data-driven organization in which information is shared across the ecosystem. Consider how production performance data could be valuable to both the line manager looking to increase efficiency and the engineers looking to ensure quality.
Because every interaction in the digital world from buying things to learning new food dish – is based on data. And data has become a critical pillar for digital transformation. This data allows you to set baselines and benchmarks for your digital transformation and serves as a valuable indicator of success.
Conclusion
Managers struggle to understand what digital transformation consulting means for them to pursue opportunities and prioritize efforts. It’s no wonder that many C-suite leaders anticipate major business disruption, significant new technology expenditures – a complete shift from physical to virtual channels, and the acquisition of tech start-ups.
Some businesses have succeeded in responding to the digital challenge by making significant changes to their distribution processes, production channels, or business models. In contrast, many others have done so by taking a more incremental approach that keeps the fundamental value proposition and supply chain largely untouched.
When everyone has access data analytics, they have the context to make the best decisionsfor their team, their department, and the company – in real time.
Facilitating decision making throughout the enterprise starts with commitment from the top
Many organizations today are accumulating data faster than they know what to do with it. But it’s not how much you have; it’s the quality and what you do with it that produces the real value. Businesses that have come to grips with the fact that acquiring data is the easy part are quickly realizing that transforming that data into insights, while challenging and at times risky, is well worth the effort. A data-led approach provides the foundation to a culture that infuses analytics throughout the business and beyond.
Yet, even as self-service data analytics have become standard practice, the impact of data has not been fully realized. According to a recent Harvard Business Review (HBR) survey, 89 percent of participants believe analyzed data is critical to their business innovation strategy. Results also noted data analytics as improving the customer experience and operational efficiency – but not leveraged to fuel innovation and new business opportunities. The takeaway? Too many organizations are not taking full advantage of data and analytics, creating competitive opportunity for those who dare to think differently.
Respondents in the HBR survey cited a lack of employee skills or training and inferior data quality as impediments to analyzed data utility. Training a diverse workforce in a broad range of roles and departments to use specialized BI technology is not easy. Convincing them to work outside their comfort zones can be downright impossible. But, to become ‘data-driven,’ everyone must participate.
Infusing existing workflows, processes, and applications with analytics is a seamless way to solve the participation challenge – increasing automation and resulting in a streamlined user experience with no complicated tools or technical training required. Embedding analytics within the employee’s standard workflow puts the information front and center, empowering timely decisions from within that same application. This ability to ‘stay within one’s comfort zone’ can boost analytics adoption, laying the strategic foundation to a data-driven culture.
In a data-driven culture, where every employee has access to the data they need within their own workflows, data analytics delivers significant benefit. This infusion of analytics offers insights and promotes strategic decision making on the spot. But to do this right, the C-suite must lead. Placing emphasis on data literacy and decision making throughout the organization is the precursor to infusing insights into each employee’s daily workflow.
While it is not necessary to become a data scientist to lead a data-centric organization, a fundamental knowledge of basic data principles is certainly invaluable to executives. These include an understanding of the insights required, recognition that clean data is valuable data, and the capacity to pinpoint data gaps. With this level of data know-how, leaders can reshape how decisions are made throughout the operation – and beyond (i.e., suppliers, partners, and customers). Establishing corporate priorities via consensus enables leadership to define how data will be leveraged and choose technology that drives adoption of the company’s data strategy. With harmony among the leadership team, goals are set, measured, evaluated, and adjusted. Metrics can encompass anything from revenue generation to improving customer experience and identifying demand for new products and services.
Technology, while necessary, can also impose limits to data visibility. Add the rapid amassing of more data, and the issue intensifies. Analytics infusion instead puts data and actionable intelligence in front of those who need it, when and where it’s needed. Sure, democratizing data is a bold move but leaders who understand the inherent value of a data-informed organization know the benefits outweigh the risks.
With an authentic data culture, leaders can introduce more efficient processes that guide innovation and new business opportunities. And while technology certainly plays a role, it’s the combination of culture and people supported by smarter processes – infused with analytics at the point of need – that powers strategic decision making at every level of the company.
With the advent of data-spitting technologies, the opportunities to collect information from within and outside the organization grow tremendously. From edge sensors and social media platforms to customer relationship management (CRM) and enterprise resource planning (ERP) software—every file is generated in different formats wherein each data stream is unique. The integrity of data used for business decisions, thus, becomes doubtful.
A majority of organizations are grappling with data quality challenges. AnIBMstudy states that poor quality data costs 3.1 trillion dollars per year in the US alone. It erodes a whopping 12% of the company’s revenue on average.
The reasons for gaps in data integrity are diverse—from abnormalities in terms of formatting and storage, poor acquisition methodologies to duplicates or incomplete records—everything under the sun has an impact on data. This highlights the importance of the data cleansing process for companies.
Data Cleansing—An Essential Business Mandate
Data-driven insights have been used as the major source of competitive differentiation for organizations. With COVID-19 gripping again, businesses are leveraging data-based insights to cope with the pandemic fallout. They are working on discerning new sources of growth while securing the company’s financial footing. This can be done efficiently when the data is clean and of assured quality.
Mentioned here are some of the industries where data cleaning plays a vital role:
1. Healthcare
Take the case in point: An organization comprising nearly 90 hospitals is committed to becoming the community resource to create insights, knowledge, and wisdom for the continuous improvement of healthcare in their area. To achieve this vision, the organization begins collecting information from its members and records it into a regional database. Through this collected data, the foundation can easily identify and address disparities related to health, diseases, gender, age, etc.—and transform this information into knowledge that can be put to use for the betterment of health programs in the community.
With good quality and clean data, this organization can make accurate analyses as well as offer evidence-based support to community programs, regional health partnerships, and various public health committees. This knowledge consequently translates into greater cost optimization, enhanced operational efficiencies, and reduced risks in healthcare.
Owing to the current pandemic landscape, governments, healthcare workers, and institutions are incorporating data-driven insights to make out of this situation and save people’s lives. Many providers are also refining their ACA readiness strategies to enhance the services offerings. Collaborating with reliabledata cleansing and migration companiesacts as an enabler in their quest of getting quality data without trading off its integrity. They leverage data quality tools that are HIPPA and FISMA compliant.
2. Manufacturing & Logistics
The companies dealing in the manufacturing and logistics vertical acknowledge the fact that inventory valuations depend on accurate data. Any type of anomalies, inconsistencies, or inaccuracies in the datasets can lead to delivery issues and an unsatisfied customer experience. Apart from this, configuring production machines and robots based on low-quality data leads to inefficient outcomes. However, associating with a reputed data cleansing company can help them efficiently streamline their processes, enhance bottom-line operations as well as gain big wins in productivity and profitability.
3. Banks & Financial Institutions
Incomplete or inaccurate data leads to regulatory breaches, sub-optimal trade strategies, and delayed decisions due to manual checks. Clean data translates into increased profitability and effective business for the banks and financial institutions. Not only do they gain confidence in their reports generated, but also get assured that their decision-making is supported by accurate information. They can easily stay compliant with the different data-related laws such as GDPR, CCPA, ADA, FCRA, etc.
Wrapping Up
The benefits of the data cleansing process are numerous for every business, irrespective of the industry verticals they deal in and these were just a few examples.Data cleansing servicesenables organizations to get hygienic data without trading its integrity. They can significantly optimize operational costs without jeopardizing the company’s growth. All that needs to be done is find the right data cleansing service provider!
Senior executives trained in accounting continue to struggle to understand how to determine the value of their data. The article “Why Your Company Doesn’t Measure The Value Of Its Data Assets” written by Doug Laney (by the way, why does the Forbes web site absolutely bury the reader in ads?) contains a telling comment from a senior accounting firm partner:
“… balance sheets and income statements which form the backbone of today’s accounting system now fail to capture significant sources of value in our economy. He said that the measurements we use don’t reflect all the ways that companies create value in the New Economy, and this lack of transparency results in undue market volatility and mere “guesstimates” by investors in valuing companies. Even the chairman of the AICPA stated that the accounting model is out of date and based on the assumption of profitability depending upon physical assets—an accounting model for the Industrial Age, not the Information Age.”
This paragraph reflects how a traditional accounting mindset, in the age of digital assets, is focused on the wrong valuation method – trying to represent value using an artificially-defined balance sheet that doesn’t capture how today’s companies are using data to create new sources of customer, product, and operational value.
Nothing says “We really don’t know how to quantify value in the digital age” better than Figure 1 where a significant percentage of the most valuable firms’ “value” is credited to nebulous intangible (non-physical) assets. And the discrepancy in the creation of value between traditional physical assets and intangible digital assets is growing exponentially.
Figure1: Increasing Percentage of Most Valuable Firms defined by Intangible Assets
To properly reflect the value of their digital assets, executives must embrace an economics mindset where the value of an assets is determined from the use of that asset. This is critical given the unique economic characteristics of digital assets – they never wear out, never deplete, can be used across an unlimited number of use cases at zero marginal cost, and they can appreciate, not depreciate, in value they more that they are used (if properly engineered).
I introduced the Data Monetization Roadmap in “Introducing the “4 Stages of Data Monetization” as a guide to help organizations in their data monetization journey. The roadmap emphasizes that the driver of data monetization is in the use or application of the data to create value. That is, the value of data isn’t in possession but in the application of the data to create new sources of customer, product, and operational value.
As organizations negotiate the Data Monetization Roadmap, they will encounter two critical inflection points:
Inflection Point #1 is where organizations transition from data as a cost to be minimized, to data as an economic asset to be monetized. I call this the “Prove and Expand Value” inflection point.
Inflection Point #2 is where organizations master the economics of data and analytics by creating composable, reusable, and continuously-learning and adapting digital assets that can scale the organization’s data monetization capabilities. I call this the “Scale Value” inflection point.
This pivot point is where the organization makes the transition from just capturing, storing, securing, and governing data to actually monetizing it. How do you get organizations to make that first pivot towards Data Monetization? How can one help the business stakeholders to connect to and envision where and how data and analytics can generate value (see Figure 2)?
Figure2: Data Monetization Roadmap Inflection Point #1
Navigating Inflection Point #1 requires close collaboration with business stakeholders to identify, validate, value, and prioritize the business and operational use cases where data and analytics can create new sources of value. The Big Data Strategy Document in Figure 3 provides a framework for that collaborative engagement process.
The Big Data Strategy Document decomposes an organization’s key business initiative into its supporting use cases, desired business outcomes, critical success factors against which progress and success will be measured, and key tasks or actions. The Big Data Strategy Document sets the stage for an envisioning exercise to help the business stakeholders brainstorm the areas of the business where data and analytics can drive meaningful and relevant business value. Yep, there is a lot of work that needs to be done before one ever puts science to the data.
So, now we’ve given the business stakeholders a taste of success in monetizing their data. Interest is building and others across the organization are asking for help in monetizing their data. Now it gets really fun!
The second inflection point occurs just as organizations are scaling their data and analytics success across the organization. More and more business units are coming to the data and analytics team for assistance with their top priority use cases. But remember:
“Organizations don’t fail due to a lack of use cases; they fail because they have too many.”
The volume of use case requests starts to overwhelm the limited data and analytics resources. And when the business units can’t get support in a timely enough manner, the business units get frustrated and seek outside solutions. And as these organizations go elsewhere for their data and analytic needs, some fatal developments occur:
Data Silos. These are data repositories that pop up outside the centralized data lake or data hub. And with the ease of procuring cloud capabilities (got a credit card anyone?), it is easy for impatient business units to set up their own data environments.
Shadow Data and Analytics Spend. The growing presence of software-as-a-service business solutions make it easy for impatient business units to just buy their solution from someone else. Consequently, money that could be invested to expand the organization’s data and analytics capabilities is now being siphoned off by one-off, point solutions that satisfy an immediate business need, but create longer term data and analytics debt.
Orphaned Analytics. Orphaned Analytics are one-off Machine Learning (ML) models written to address a specific business or operational problem, but never engineered for sharing, re-use, and continuous refinement. The ability to support and enhance these one-off ML models decays quickly as the data scientists who built the models get reassigned to other projects, or just leave the company.
The result: instead of creating data and analytics assets that can be easily shared, reused, and continuously refined, the organization has created data and analytics debt that drives up maintenance and support costs which quickly overwhelms the economic benefits of the data and analytic assets. Welcome to Inflection Point #2 (see Figure 4).
Figure4:Data Monetization Roadmap Inflection Point #2
What can organizations do to avoid the collapse of the economic value of data and analytics that can occur at inflection point #2?
Data Lake 3.0: Collaborative Value Creation Platform. Leading organizations are transitioning the data lake from a simple, cheaper (using the cloud) data repository to an agile, collaborative, holistic value creation platform that supports the sharing, reusing, and refinement of the organizations valuable data and analytic assets (see Figure 4).
Figure5: Data Lake 3.0: The Collaborative Value Creation Platform
Data Lake 3.0 employs intelligent catalogs to help the business units find the data they need for their use cases. The data lake also employs intelligent data pipelines to accelerate the ingestion of new data sources, and a multi-tiered data lake environment to support rapid data ingestion, transformation, exploration, development, and production. And eventually, these modern data lakes will transform into contextual knowledge centers that not only help the business units find the data, but also provide recommendations on other data sources (and analytic models) that might be useful for their given use case.
Data Monetization Governance Council. Another key to navigating Inflection Point #2 is the creation of a data monetization governance council with the teeth to mandate the sharing, reuse, and continuous refinement of the organization’s data and analytic assets. If data and analytics are truly economic assets, then the organization needs a governance organization with both “stick and carrot” authority for encouraging and enforcing the continuous cultivation of these critical 21st century economic assets (see Figure 5).
Figure6: Data Monetization Governance Council
The key to scaling the organization’s data monetization capabilities is to thwart data silos, shadow IT spend, and orphaned analytics that create a drag on the economic value of data and analytics. When the business and operational costs to find, reuse, and refine the data and analytic becomes greater than the cost to build your own from scratch, then that’s a failure of the Data Monetization Governance Council.
The Data Monetization Roadmap provides both a benchmark and a guide to help organizations with their data monetization journey. To successfully navigate the roadmap, organizations must be prepared to traverse two critical inflection points:
Inflection Point #1 is where organizations transition from data as a cost to be minimized, to data as an economic asset to be monetized; the “Prove and Expand Value” inflection point.
Inflection Point #2 is where organizations master the economics of data and analytics by creating composable, reusable, and continuously refining digital assets that can scale the organization’s data monetization capabilities; the “Scale Value” inflection point.
Carefully navigate these two inflection points enables organizations to fully exploit the game-changing economic characteristics of data and analytics assets – assets that never deplete, never wear out, can be used across an unlimited number of use cases at zero marginal cost, and can continuously-learn, adapt, and refine, resulting in assets that actually appreciate in value the more that they are used.
Yes, you could say that the Data Monetization Roadmap is the game plan for fully exploiting the Schmarzo Economic Digital Asset Valuation Theorem. But that’s just me and that Nobel Prize in Economics talking…
Nowadays, information is one of the most valuable resources at the disposal of companies. Data monetization is the process that allows valuable data within companies’ business operations to be turned into new revenue streams. According toGartner, integratingdata and analyticsinto key business roles is among the top trends for 2021.
The increasing need to get valuable insights from raw data opens many opportunities for data monetization vendors. According toMarketsandMarketsresearch, the global data monetization market was valued at $2.3 billion in 2020. It is growing rapidly and is set to reach $6.1 billion by 2025.
Direct and indirect data monetization approaches
IoT devices and other digital technologies allow businesses to gather a massive amount of data that offers insights into consumer demographics, preferred products, sales performance, etc. There are two main data monetization strategies: direct and indirect.
Indirect data monetizationinvolves leveraging these information insights within your business processes to predict demand, cut waste, segment customers and optimize price and supply chain, etc.
Direct data monetizationimplies exchanging data-derived insights for money or cryptocurrencies, directly turning them into income-generating assets. Direct data monetization is often part of long-term roadmaps, yet it always seems neglected, coming after everything else, including blockchain adoption, AI and hyper-automation in terms of priority.
Even though the market is expanding significantly, according to theBI Survey, only 25% of large organizations and 9% of small companies have actually launched data monetization initiatives. Companies are sitting on millions of dollars of potential revenue benefits from data monetization, and only a handful of them are actually making it a reality. Most of the time, the reasons why companies postpone monetization of their data are trivial, like the fact that they have never done it before.
Use cases for data monetization
There are many types of data that can be sold – from raw sensor data to insights obtained by analytics teams. Data that can be turned into a product differs greatly for each industry. For example, in insurance, customers’ claims histories are widely used for identifying fraud. In media and entertainment, anonymized customer data can be used to find behavioral patterns and target the right audience. Here’s a brief showcase with real-life examples of how companies monetize data products:
DTN. The agriculture company focuses on subscription-based services for the analysis and delivery of real-time data. It has created acloud-based data toolfor sharing information like field-level weather and commodity prices with agricultural businesses.
Vodafone. The mobile communications provider uses anonymized and aggregated mobile data to getinsightsabout users’ mobility patterns. The gathered information can then be leveraged by the tourism sector to understand both national and international tourists’ behavior, or by the real estate industry for site planning. In fact, data monetization has been a lucrative business for many telecommunication companies for years, including T-Mobile, Swisscom and others. Another example isNOS SPGS— Portugal’s biggest communications and entertainment group which monetizes anonymized phone data once a traveler enters the country and use its infrastructure.
“We are proving the power of data-driven phone record monetization as a new business model.” João Moreira, Head of Corporate and Public Administration at NOS SPGS
Uber. With the user’s permission, the ridesharing service can sell location data to food and retail industry players. Other companies can leverage this data to provide discounts and promotions personalized to the specific customer.
Michelin. The company’s main activity is manufacturing of tires; however, it also provides digital services and publishes maps and guides to help enrich trips and travels. In order to expand their offering for B2B customers and acquire new revenue streams the company sells both raw tire data (temperature, pressure, GPS, mileage) and insights to studydriving behavior.
Dunnhumby. This is an analytics company and subsidiary of Tesco. The majority of company profits comes from selling customer analytics insights, which helps retailers and brands to improve customer experiences. Nestle, Unilever, Metro, Danone are among many clients of Dunnhumby. The company’s annual revenue reached $444 million in 2019.
Monsanto. In 2012 Monsanto (part of Bayer since 2018) bought the Climate Corporation — a data analytics platform developed to help farmers improve their productivity using gathered insights. In 2020, the Climate Corporation revenue reached $100 million. Nowadays, the company uses FieldView a platform to turn data generated on a farm into additional income.
Preparing for data monetization
Before starting on the path to data monetization, pay attention to the following data monetization strategy components:
Licensing and unauthorized usage. You can ensure that your data cannot be resold legally by creating a proper license. But, it is quite a challenging task to identify those consumers that have breached the licensing term. Some data vendors find it one of the most difficult problems to address. Fortunately, there are many techniques for detecting unauthorized usage. Should you need any extra information on this topic, please let us know in the comments below and we’ll be happy to answer your questions.
Data privacy. Each data vendor must follow new data protection laws, like GDPR, that enforce the protection of personally identifiable information.
Competitive advantage. Selling the data that gives you an edge over your competitors is not a wise decision. Determining what data can and cannot be monetized is challenging but vital.
Marketing. Without a doubt, creating a marketing strategy for data monetization is pivotal. It includes consumer and market analysis, review of your competition, and, of course, marketing mix (product, place, promotion and price), among other things. Please keep in mind that there are also ways to avoid explicit pricing of assets.
Data quality. This is a vital component of any monetization strategy. It makes consumers trust their vendor, which can be the difference between having dozens of clients and having thousands of clients. And as with documentation, focusing on data quality results not only in a better customer experience but also in internal gains.
Using data marketplace vs your own infrastructure. There are different pros and cons of using both methods. For example, publishing your data to a marketplace makes it easy for marketplace users to discover and requires fewer infrastructure resources, while distributing it on your own grants you much more flexibility in terms of pricing options, etc. Marketplaces include specialized companies like Ocean Protocol and Datarade, as well as software vendors like Snowflake. Informatica has also jumped on the bandwagon of data monetization.
Scalability and availability. Make sure that your data is accessible to consumers all the time, and that it is distributed to all of them without any degradation in speed to provide a better user experience.
Documentation. The importance of thorough documentation must not be underestimated. Not only does it greatly benefit the consumer, but it also adds to the internal understanding of the data.
Key takeaways
Many companies have large amounts of unused data. And while these companies are sitting on a gold mine, very few of them decide to launch data monetization initiatives. Data that helps to develop and deliver new insights can identify new industry winners by boosting profits and creating internal value. Do not overlook your opportunity!
Integration of technology in different industries and business sectors has been obvious. The digital transformation streamlines various processes effortlessly. Although the manufacturing industry has been slow to embrace technology. But the right time is here for the production sector to improve decision making and performance from data analytics.
Industry 4.0 when combined with the power of the latest technological development of AI, advanced visualization and analytics, robotics and IoT powered devices can provide manufacturers with the potential to collect, store, visualize and utilize data in daily factory operations.
Through advanced business intelligence and analytics, plant managers can get recommendations about possible improvements.
The manufacturing industry is constantly looking for improved solutions to speed up production and automate large scale industrial processes. In this post, we wish to throw some light on power bi data analytics implementations and how they can bring a transformation for the manufacturing industry.
Why should everyone in manufacturing utilize the industry 4.0 revolution with Power BI?
Industry 4.0 involves a set of technologies for several processes. Even though the concept of industry 4.0 is a little hard, the manufacturing plants which use this solution have much faster production than companies still relying on conventional methods. Advanced data analytics and processes automation combined with connectivity can enhance production efficiency exponentially. Data analytics through power bi have helped plant managers to customize products, decrease time to time, increase efficiency and create a more sustainable and profitable business model optimized for service productivity.
For companies committed to streamline their supply chain, align operations, and crush production challenges, data analytics with power bi can help you achieve it all.
Before moving on to the benefits of power bi and analytics, let’s see some major data challenges of the manufacturing industry.
In manufacturing, there are so many processes and while the data is collected from several sources, it is presented inconsistently, making it difficult to read and draw insightful conclusions. Some companies gather data successfully but fail to comprehend and further utilize it.
Another challenge is the integration of data analytics with traditional manufacturing systems such as ERPs, production planning systems and others.
Even when the manufacturing department generates huge data, it fails to manage and coordinate the pace of storage management systems.
As the number of processes increases and the production grows, the complexity of data sore high which then need better visualization and analytics tools. Although it’s not the responsibility of the manufacturer to solve this issue, they must be aware of what a data analytics tool like power bi can do for complex data analysis.
An industrial data collection system with limited computing power might put the entire company at risk as there are underlying threats of cyber attacks, online leaks, unauthorised access, and other security issues.
We have discussed some of the primary data challenges, now let’s see how power bi can help mitigate these threats.
How data analytics solutions of Power BI can transform the manufacturing industry?
Detailed reports, graphs and charts in power bi can be explicitly used by companies to draw actionable insights for core manufacturing issues such as:
Which products bring the most customers and which yields lower profitability?
What are the weak links in the manufacturing process? For instance which raw material vendor can halt manufacturing the most?
How to perform shipment performance, transportation cost, and several other KPIs of the business?
The applications of power bi data analytics reports are many in the manufacturing industry. The primary aim for most managers is to improve productivity growth. Other important areas of inefficiency are supply chain management, prediction of sales and marketing expenses, calculation of equipment performance, and more.
Advanced data analytics can provide a high ROI with intuitive insights in these particular areas of a manufacturing plant. Also, the use of data analytics and visualization will help to create better revenue streams built around the product quality of a manufacturing company.
Benefits of power bi data analytics features:
Low operation costs
Imagine if employees had access to a custom dashboard to process an instant supply chain analysis or an enterprise-level custom dashboard of sales to monitor all the revenue sources on one screen. This is simply possible through power bi dashboard visualization features in seconds. Manufacturing managers and employees can read complex data in easy to read graphical formats. They access the ability to handle ad hoc queries and share insights about workflows too.
Such insightful analysis can reduce raw material waste, speed up funding for the most in-demand products, and improve the potential of a manufacturing unit.
Maintaining human resource and automation
For various manufactures, it is not simple to handle automated processes and warehouses as they have to decide about where to keep human labor for certain roles. Sometimes human supervision is necessary to maintain the quality of the product and its seamless production. Workforce analytics solutions in power bi can help manufactures evaluate ROI and employ staff along with automation where required.
Incredible management of supply chain
With the assistance of power bi, manufacturers can analyze supply chain logistics data regularly for timely product deliveries and ultimate services. Such detailed analysis of each batch shipment allows managers to monitor consignment costs, performances and settle contracts.
A supply chain management dashboard in power bi can help one measure repair costs, transportation expenses, equipment issues, and other KPIs efficiently. The data is presented in visually attractive formats to gain insights based on previous and real-time data to make informed business decisions.
Enhanced decision making
Data analytics and power bi make decision making very intuitive for managers and come up with unique solutions to rectify operational issues. Power bi processes millions of rows of data instantly and present in a visual format to help decision-makers catch inefficiencies and make efficient decisions for better organizational performance.
Data is the future
The organizations adopting data-driven manufacturing are actively dispersing both external and internal problems. Power bi data analytics is a strategic move to get speedy results and performance for your production business as well.
If you are ready to take a step towards a better future and create a lasting impact through data, talk to our power bi development company and start your data analytics voyage.
To pursue a successful data scientist career, one requires a deep understanding of this domain’s theoretical and practical aspects. But, there is one more important aspect of data science-related knowledge – the knowledge of writing a data scientist resume.
An impressive resume must target the companies to meet their requirements and understand the job market’s needs. Remember that a data scientist resume acts as the preliminary screening for your abilities and is the ticket for the next round of the interview process. And what is the first thing you need to ensure that your resume reflects your knowledge of data science? A data science certificate. Once you have it, create a nice resume, which is not only easy to read but also gives all the important information about you.
A typical data scientist resume must have:
Do not write a long detailed resume, as most employees spend just around 30 seconds as an average time while going through a resume. Just 1-2 page are enough
Never use fancy fonts and styles. Use simple fonts
Maximum usage of pointers is a good habit while writing a good resume
Don’t offer the same resume to every company. Your resume should match the company’s specific requirements
Provide URLs to your LinkedIn, Github repository, personal website, if any, and other social media profiles related to the domain
Stick with a chronological order format by first explaining your profile in 3-5 lines. Now proceed with your educational credentials, current job, work experience, projects, extracurricular information, and other related activities. Remember to keep the information section on top of the resume.
Avoid complicated lines, rather use simple words
Finally, proofread your resume to find any grammatical mistakes or any missing information you wanted to share
A good way of writing a resume is to imagine crafting a house. Just as real estate has a fixed area and a floor plan, the same is with your resume. You should make sure that all things fit neatly and within the space available.
Create Differentiated Areas –Once you have decided on the information to display in your resume, identify the right sections you have to place them. For example: determine the length of the introduction and profile summary and the place you want it to impress most
Resume Headlines –Here, you have to write your name and the job title you are applying for. Customizing a job title is a good choice as it depicts your skill levels to match the company’s requirements
Profile Summary –A data scientist’s resume may contain either an objective or a profile summary. Target the specific company and position of the job. Include your quality and skills and conclude with benefits you will bring to the company
Contact Information –This should be at the top of the page of your resume, below your objective statement. Apart from furnishing your name, phone number, email ID, etc., don’t add any personal information such as caste, marital status, etc. Add your website, LinkedIn, and Kaggle data scientist profile or other such platforms that demonstrate your data science capabilities
Education –It should be placed below the contact information. Include all major and minors information, including the year and the month of completion of degrees. List the highest and most relevant first and then list gradually down the order
Data Science Projects and Publications –In this section, write data science-related coursework and all academic projects with relevant background. Here you can also make up for the absence of rich professional experience
Experience –Include all part-time and full-time placements. Remember to list the most relevant experience above the list. Use bullet points to demonstrate your past activities that match your data science career pursuits
Skills –The most important part of a resume so make it stand out in your job application. Concentrate on indicating transferable skills, which show that you have mastered certain skill sets in your previous jobs
Leadership –You may include this as quality with the right experience. Demonstrate the skills and capabilities with bullet points so that they are clear for the reviews
Honors and awards –A stand-alone section with a brief description emphasizing your accomplishments in your academics and professional career
Certificates –Listing a data science certificate ensures an employer that you are well qualified for the position you have applied for. Update your resume with newly acquired certificates and keep upgrading your skill sets
To conclude, a good resume must be relevant to the job applied, showing your skills with appropriate education and certificates. And don’t forget to first gain a data science certificate!
Business analytics has moved from the sidelines to the forefront
AI-based technology has revolutionized the field
Real-world examples of BA success.
Gone are the days when a BA’s role was as a requirements note taker [1], or when data interpretation was the responsibility of a small team of programmers. In the last ten years, Business analytics has grown from a simple description of predictive and statistical tools to an umbrella term covering a complex spectrum of business intelligence and analytics. BA combines applications, skills, technologies, and processes to provide data-based insights for businesses. Big data is leveraged along with statistics to develop markets, evaluate customer behavior and optimize revenue streams.
A new generation of AI-based BI tools have resulted in sweeping changes to the entire data analytics process, enabling the creation of actionable insights from complex data. Companies that implement AI are certain to edge ahead of their competitors, improving performance and generating higher revenue.[2]
Where Business Analytics is Booming
Industries that are at the forefront of the business analytics revolution include:
Banking and Finance: Analytics aids in the detection of fraud, evaluation of credit risk and prediction of delinquency. For example, Mastercard business analysts built a cross-border ATM Fraud Rules Engine that resulted in a 65% Decrease in ATM Fraud [3].
Customer service: BA can help to reduce churn rate (customer loss) by using big data to route calls, maintain adequate staffing levels, and catch issues early on in the customer service process. One notable success was seen by broadband communications, services and solutions provider XO Communications, whoreduced churn rates by almost 50 percent with the assistance of IBM predictive analytics software [4].
Education: Data can be analyzed to predict student outcomes, analyze deficiencies in student learning and create a plan for improvement. Intervention can happen earlier, before a student has fallen too far behind [5]. For example, The University of Wolverhampton partnered with student software and services provider Tribal to develop learning analytics software. The tool predicts student success with 70 percent accuracy [6].
Farming: BA can help farmers boost yields, manage pests and crop diseases and limit pesticide use while maximizing per-acre production. For example, WinField United, the Land O’ Lakes seed and crop-protection division analyzes millions of data points from diverse sources to assist farmers with their goals [7].
Healthcare: Analytics can benefit patients by assessing risks and suggesting preventative care measures. It can also be used to maintain adequate staffing levels and track trends in a wide variety of areas including new technology, procedures to improve outcomes, or tracking of disease outbreaks. Electronic Health Records, which capture condition-specific information like clinical orders, clinical findings and laboratory results, have become a primary sources of information on the health and well-being of patients [8].
Marketing: BA can help to predict sales, maintain a budget, and analyze consumer behavior. Trends in consumer loyalty can be tracked, with different brand messages analyzed for effectiveness. As an example, behavioral targeting collects data on consumer browsing activities by placing digital tags in browsers. These tags track and aggregate consumer behavior, resulting in the serving of more relevant advertising [9].
Sports: Sports BA is booming, enabling sports business professionals strategize, promote a company’s financial performance, and maintain or improve a competitive advantage [10]. Business analytics has been used in Major League Baseball to show that starting pitchers lose effectiveness when they cycle through the batting lineup. This has resulted in a big increase in relief pitchers [11].
These are just a few examples to highlight the growing trends. As the science of business analytics continues to grow, it is on track to become “the world’s hottest market for advanced skills” [12]. As more and more businesses incorporate advanced data techniques and gain a competitive edge, the market for business analysis will continue to soar.
I plan to buy it and I recommend you do. This book provides a broad introduction to algorithms for decision making under uncertainty.
The book takes an agent based approach
An agent is an entity that acts based on observations of its environment. Agents
may be physical entities, like humans or robots, or they may be nonphysical entities,
such as decision support systems that are implemented entirely in software.
The interaction between the agent and the environment follows an observe-act cycle or loop.
The agent at time t receives an observation of the environment
Observations are often incomplete or noisy;
Based in the inputs, the agent then chooses an action at through some decision process.
This action, such as sounding an alert, may have a nondeterministic effect on the environment.
The book focusses on agents that interact intelligently to achieve their objectives over time.
Given the past sequence of observations and knowledge about the environment, the agent must choose an action at that best achieves its objectives in the presence of various sources of uncertainty including:
outcome uncertainty, where the effects of our actions are uncertain,
model uncertainty, where our model of the problem is uncertain, 3. state uncertainty, where the true state of the environment is uncertain, and
interaction uncertainty, where the behavior of the other agents interacting in the environment is uncertain.
The book is organized around these four sources of uncertainty.
Making decisions in the presence of uncertainty is central to the field of artificial intelligence
