Inflation is not one of the more usual topics one thinks of when talking about artificial intelligence, data science and machine learning. Yes, economic modeling is increasingly done using these technologies and certainly the field is ripe for exploration given the evolution of AI tools, but all too often there is a chasm between the technical and the political/economic that people are uncomfortable jumping so it’s worth understanding how one impacts the other.
Inflation is oft-misunderstood because it isn’t really a “thing” per se. Rather, inflation occurs when the price of a particular good or service increases relative to either its own past price or the price of other goods or services. Ordinarily, inflation tends to rise by 1-2% a year, primarily as a reflection of an increase in money supply as the population grows. This is fairly benign inflation as wages should go up at roughly the same level as commodity costs.
Where problems arise is when commodity or service inflation rises faster than wage inflation (labor costs). This commodity/wage inflation ratio is typically fairly elastic – commodities can increase in price for some time before workers can no longer afford even basic goods and services, which in turn usually result in businesses failing and recessions occurring. As the economy recovers, wages tend to get renegotiated, especially when companies cannot hire enough workers at the old price points and have to raise hourly wages.
However, things are different this time around. For starters, the pandemic was global in nature, which meant that employment plummeted globally as well, and a large number of companies disappeared nearly overnight. This means that there are far more people who are now renegotiating contracts with employers in a period of very high demand. Additionally, because so many employees that did survive had to work from home, they saw first-hand that they did not need to work in an office and could in fact do all of their work nearly as well as (or in many cases better than) they could when working from an office.
They also discovered the art of the side hustle – creating virtual storefronts, becoming virtual celebrities that live off Google ad revenues from their media ventures, writing content for multiple clients, and otherwise taking advantage of the Internet to become producers rather than just consumers. This was work that met their personal needs, gave them a stake in their own products, and increasingly took them outside of the 9/5 walls of the corporation. Put another way, employers are no longer competing just against other companies, but increasingly with their potential employee’s own gigs.
One other wild card is the roles that AI and machine learning play in this equation. Commodity prices are rising in part because of supply chain disruptions, but those supply chain disruptions have to do primarily with a lack of people at critical points in a system where employers have been trying to eke out every potential performance gain they could through automation.
Automation causes wages to fall because you need fewer people to do things the automated process replaces. It can make processing commodities somewhat faster as well, but you are still limited by the laws of physics there (indeed, most performance improvements in commodity processing have come about because of improved material sciences understanding, not automation per se). Improved data analysis allows you to better eke out some performance gains, but increasingly it will be the skills and talents of the people who work for you (or increasingly with you) that will determine whether you succeed or fail as a business.
AI is not a magic panacea. It is a powerful tool to help understand the niche that your organization fills and it can expand the capabilities of the people that you do employ, but ironically we may now be entering a protracted period where the gains that came from automation are balanced out by the need for qualified, well-trained, creative and intelligent workers who increasingly are able to use the same power of that automation for their own endeavors. This should be a sobering thought for everyone, but especially those who expect things to return to how it was pre-pandemic.
These issues and more are covered in this week’s digest. 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.
Spend any time at all in the machine learning space, and pretty soon you will encounter the term “feature”. It’s a term that may seem self-evident at first, but it very quickly descends into a level of murkiness that can leave most laypeople (and even many programmers) confused, especially when you hear examples of machine learning systems that involve millions or even billions of features.
If you take a look at a spreadsheet, you can think of a feature as being roughly analogous to a column of data, along with the metadata that describes that column. This means that each cell in that column (which corresponds to a given “record”) becomes one item in an array, not including any header labels for that column. The feature could have potentially thousands of values, but they are all values of the same type and semantics.
However, there are two additional requirements that act on features. The first is that any two features should be independent – that is to say, the values of one feature should in general not be directly dependent upon the same indexed values of another feature. In practice, however, identifying truly unique features can often prove to be far more complex than may be obvious on the surface, and the best that can be hoped for is that there is, at worst, only minimal correlation between two features.
The second aspect of feature values is that they need to be normalized – that is to say, they have to be converted into a value between zero and one inclusive. The reason for this is that such normalized values can be plugged into matrix calculations in a way that other forms of data can’t. For straight numeric data, this is usually is sample as finding the minimum and maximum values of a feature, then interpolating to find where a specific value is within that set. For ordered ranges (such as the degree to which you liked or disliked a movie, on a scale of 1 to 5), the same kind of interpolation can be done. As an example, if you liked the movie but didn’t love it (4 out of 5), this would be interpolated as (5-4)/(5-1) = 3/4 = 0.75, and the feature for (Loved the Movie) when asked of 10 people might then look like:
Other types of data present more problematic conversions. For instance, enumerated sets can be converted in a similar fashion, but if there’s no intrinsic ordering, assigning a numeric value doesn’t make as much sense. This is why enumerated features are often decomposed into multiple like/dislike type questions. For instance, rather than trying to describe the genre of a movie, a feature-set might be modeled as multiple range questions:
On a scale of 1 to 5, was the movie more serious or funny?
On a scale of 1 to 5, was the movie more realistic or more fantastic?
On a scale of 1 to 5, was the movie more romantic or more action oriented?
A feature set then is able to describe a genre by taking each score (normalized) and using it to identify a point in an n-dimensional space. This might sound a bit intimidating, but another way of thinking about it is that you have three (or n) dials (as in a sound mixer board) that can go from 0 to 10. Certain combinations of these dial settings can get you closer to or farther from a given effect (Princess Bride might have a “funny” of 8, a “fantasy” of 8 and an “action oriented” of 4). Shrek might have something around these same scores, meaning if they were described as comedic fantasy romance and you liked Princess Bride, you stand a good chance of liking Shrek.
Collectively, if you have several such features with the same row identifiers (a table, in essence), this is known as a feature vector. The more rows (items) that a given feature has, the more that you’ll see statistical patterns such as clustering, where several points are close to one another in at least some subset of the possible features. This can be an indication of similarity, which is how classifiers can work to say that two objects fall into the same category.
However, there’s also a caveat involved here. Not all features have equal impact. For instance, it’s perfectly possible to have a feature be the cost of popcorn. Now, it’s unlikely that the cost of popcorn has any impact whatsoever on the genre of a movie. Put another way, the weight, or significance, of that particular feature is very low. When building a model, then, one of the things that needs to be determined is, given a set of features, what weights associated with those features should be applied to get the most accurate model.
This is basically how (many) machine learning algorithms work. The feature values are known ahead of time for a training set. A machine-learning algorithm uses a set of neurons (common connections) between a starting set of weights, testing the weights against the expected values in order to identify a gradient (slope) and from that recalibrate the weights to find where to move next. Once this new vector is determined, the process is repeated until a local minimum value or stable orbit is found. These points of stability represent clusters of information, or classifications, based upon the incoming labels
Assuming that new data has the same statistical characteristics as the test data, the weighted values determine a computational model. Multiply the new feature values by the corresponding weights (using matrix multiplication here) and you can then backtrack to find the most appropriate labels. In other words, the learning data identifies the model (the set of features and their weights) for a given classification, while the test data uses that model to classify or predict new content.
There are variations on a theme. Withsupervised learning, the classifications are provided a priori, and the algorithm essentially acts as an index into the features that make up a given classification. Withunsupervised learning, on the other hand, the clustering comes before the labeling of the categories, so that a human being at some point would have to associate a previously unknown cluster to a category. As to what those categories (or labels) are, they could be anything – lines or shapes in a visual grid that render to a car or a truck, genre preferences, words likely to follow after other words, even (with a large enough dataset such as is used by Google’s GPT-3, whole passages or descriptions constructed from a skeletal structures of features and (most importantly) patterns.
Indeed, machine learning is actually a misnomer (most of the time). These are pattern recognition algorithms. They become learning algorithms when they become re-entrant – when at least some of the data that is produced (inferred) by the model gets reincorporated into the model even as new information is fed into it. This is essentially howreinforcement learningtakes place, in which new data (stimuli) causes the model to dynamically change, retaining and refining new inferences while “forgetting” older, less relevant content. This does, to a certain extent, mimic the way that animals’ brains work.
Now is a good point to take a step back. I’ve deliberately kept math mostly out of the discussion because, while not that complex, the math is complex enough that it can often obscure rather than elucidate the issues. It should first be noted that creating a compelling model requires a lot of data, and the reality that most organizations face is that they don’t have that much truly complex data. Feature engineering, where you identify the features and the transforms necessary to normalize them, can be a time-consuming task, and one that can only be simplified if the data itself falls into certain types.
Additionally, the need to normalize quite frequently causes contextual loss, especially when the feature in question is a key to another structure. This can create a combinatoric explosion of features that can be better modeled as a graph. This becomes especially a problem because the more features you have, the more likely your features are no longer independent. Consequently, the more likely the model is likely to become non-linear in specific regimes.
One way of thinking about linearity is to consider a two-dimensional surface within a three-dimensional space. If a function is linear, it will be continuous everywhere (such as rippling waves in a pond). If you freeze those waves then draw a line in any direction across them, there will be no points where the line will break and restart at a different level. However, once your vectors are no longer independent, you can have areas that are discontinuous, such as a whirlpool. that flows all the way to the bottom of the pond. Non-linear modeling is far harder because the mathematics moves towards generating fractals, and the ability to model goes right out the window.
This is the realm of deep learning, and even then only so long as you stay in the shallows. Significantly, re-entrancy seems to be a key marker for non-linearity, because non-linear systems create quasi-patterns or levels of abstraction. Reinforcement learning shows signs of this, and it is likely that in order for data scientists to actually develop artificial general intelligence (AGI) systems, we have to allow for “magical” emergent behaviors that are impossible to truly explain. There may also be the hesitant smile of Kurt Goedel at work here, because this expression of mathematics may in fact NOT be explainable, an artifact of Goedel’s Incompleteness theorem.
It is likely that the future of machine learning ultimately will revolve around the ability to reduce feature complexity by modeling inferential relationships via graphs and graph queries.These too are pattern matching algorithms, and they are both much lighter weight and far less computationally intense than attempting to “solve” even linear partial differential equations in ten-thousand dimensions. This does not reduce the value of machine learning, but we need to recognize with these machine-learning toolsets that we are in effect creating on the fly databases with lousy indexing technology.
One final thought: as with any form of modeling, if you ask the wrong questions, then it does not really matter how well the technology works.
It isn’t every day that you are blessed with app ideas to make money. But when you are, the worst thing you can do is launch it without the right resources and knowledge.
To build a mobile app for startup is more than just getting a team of tech-savvy people to make a product that appears as a tile on your phone. It is about developing your idea and prepping that idea for the market.
If your app idea has potential, but you dont know how to code, what legal stuff to take care of, or even secure funding to execute all of it; here is a brief custom mobile app development for startups guide on things to take into consideration.
Will Anyone Pay for Your Idea
When you come up with an app idea you may feel that it is the most brilliant idea in the world, and it probably is but do you know to what scale?
One of the primary reasons businesses fail is because nobody needed their product in the market they launched. Imagine finding out everything you built and invested time, money, and effort into was useless.
Therefore the very first thing you must do is a market analysis for how viable your product/ application is. Will it fly or flop?
Finding out that a product has great potential and staggering demand in a market is the green light you need to put things into motion.
What is Your Competition Doing
While you are researching a market need for your product keep up the habit and do a competitive landscape analysis. This is super insightful and a time for you to absorb details. Learn what they did that worked for them, and what they did that ended up as massive fails. It’s like second-hand valuable experience on how to run a business built on an app like yours.
More often than not market analysis also brings to light market opportunities that your competition is neglecting and is up for grabs.
Finding Your Brand
Most people make the mistake of using a business’s logo interchangeably with its brand. Building a powerful brand image is perhaps the best way to survive in a cutthroat market for the long term.
To build one, you must figure out what differentiates your application from others. Once you figure that out, the next step is to make sure that you have a uniform design so that every time a potential customer sees your promotional material they associate it with your business and everything it stand for.
Do You Have a Plan?
A solid app business plan will take you places. By projecting and outlining everything up to 5 years post launch, you are in a better position to stick to your goals. Moreover as a young startup a plan also offers you milestones with set deadlines that you must work for. They help you gauge the performance of your business and breed confidence in your decision making.
Money, Money, Money
To find an app idea investor can be time consuming and stressful but is not an impossible feat. If you completed all the right steps, there are bound to be several investors who see how your product is headed for greatness.
A great way to accumulate and summarize everything you have learned from market research is to share highlights in a pitch deck. A pitch deck is also how you can show off your existing structure, your chosen business plan, and your capability as an entrepreneur and a leader. All of these things can make or break a startup and potential investors want to know these things.
The remote working triggered by COVID-19 is leading to a series of interesting and positive business outcomes. An area where we are witnessing considerable impact is the practice of DevOps or the art of accelerating and improving technology delivery and operations processes. At the heart of DevOps, it is all about culture, people and technologies coming together.[i] The practice seeks to drive collaboration between development and operations teams, bringing them closer right from conceptualization, planning, design, deployment to operations. With COVID-19 imposing remote working, DevOps is witnessing a significant increase in adoption. Cloud has become critical for decentralized DevOps teams, providing a new set of challenges that need to be addressed.
The challenges are significant. For organizations that do not (yet) have a complete cloud-native DevOps program, Freddy Mahhumane, the Head of DevOps for South Africa’s financial services leader, the Absa Group, has some advice: “Don’t just pick AWS or Azure or Google Cloud and migrate. If an organization is in early stage of cloud adoption, come up with a hybrid strategy that keeps some processes running in house and on premises while you learn in the cloud environment.” This gives the required confidence to Development and Operation teams. But that is increasingly turning into a luxury with COVID-19 around. Working remotely in cloud environment has become necessary—and will soon become the default mode.
Fortunately, the DevOps culture talks about working remotely from anywhere, anytime -hardcore practitioners should be able to walk into an Internet café anywhere in the world and work. And now, after a few months of distributed working enforced by the pandemic, DevOps leaders are finding ways to solve the challenges of collaboration, processes management, delivery and more. However, cloud adoption for systems lowers the risk by several magnitudes. It allows them to access the required infrastructure when compared to accessing it over an enterprise VPN or via secure end points that need expensive licenses. This, naturally, raises questions around security.
Part of the answer lies in the practice of DevSecOps that seeks to maintain compliance and security requirements. But the focus of security, as called out by Mahhumane, has traditionally been on hardware and application security. He too feels distributed/remote working requires organizations to also focus on the social aspects of security. For example, an employee working from home social engineering may share data or innovation ideas with friends or family. So, it is very essential to understand the social side of the security as well. There could be many more challenges. DevSecOps was born to secure our systems, processes and the places where we are producing information with enterprise infrastructure. Physical security of data is assured. Now, DevSecOps has to deal with remote and isolated team members in environments that may be difficult to monitor. What is required is DevSecOps with an additional layer of controls.
This, in the long-term, is a welcome trend. It shifts attention to security right to the front of the SDLC and will cause more robust security practices. For banks and financial services this is a healthy development. They need to deliver digital products quickly to stay ahead of competition and drive innovation. Better security practices will ease their anxieties over potential breaches of compliance and regulatory requirements while enabling product development at pace.
Leaders who prioritize security strategy will find that they can drive their large distributed ecosystems of IT vendors to deliver better. To enable this, Mahhumane advises organizations to develop a security team along with strong strategy within the organization to support vendors. The team’s goals would be to help vendors meet organizational goals. He also suggests that DevOps leaders should understand their infrastructure thoroughly. For example, a data center plays the role of hosting, and understanding the processes of a data center can help create better delivery.
Over the last several years, DevOps has shown that it is an evolutionary process, not a big-bang event. However, currently it is being forced to evolve more rapidly than it has done. Processes used for years for versioning, testing, monitoring and securing, must undergo rapid change. DevOps leaders need to question their existing practices so new ones may quickly evolve for an era of remote working.
Co-authors
Freddy Mahhumane, Head of DevOps, ABSA
Ashok MVN, Associate Partner, ITC Infotech
[i] There is no clear, single definition of DevOps. As DevOps depends on company culture, the definitions show subtle differences when articulated by different people.
Big data analytics are used in the quite classy analytic techniques on tremendously big varied data groups that include organized, and unorganized data from many separate bases, and all over the various sizes from terabytes to exabytes.
Data pertaining to a massive amount of data in both the organized and unstructured formats may be referred to as big data. As cloud computing is becoming a common feature of many different companies all across the globe, this has become an essential tool for companies of every size. When it comes to one particular firm, big data has an extremely wide array of possibilities. Information technology is rising in popularity over the last decade, as organizations of all sizes understand its vital relevance.
By making use of knowledge that can be obtained via the use of big data, enterprises may use this information to make high-impact business choices that can give their company an edge over its competitors. Our ability to surpass our opponents in every area makes us a market leader.
While it may seem straightforward, a big data analysis method requires a number of sophisticated procedures that include scrutinizing enormous and various arrays of data. to get a better understanding of and emphasize recent landscape, structures, associations, hidden linkages, inclinations, and other prominent discoveries and data found in a given collection of data.
While there contain many components of Big Data analysis that may get used by small companies to proactively create data kind planning, there are also numerous methods that may be utilized by small firms to create simple decisions.
Whether it requires a third party to develop a solution or if it is essential to provide a dedicated staff with the required tools to handle such data, this will depend on the demands of the organization. A well-defined strategy for including fundamental classes unique to the platforms and frameworks a company employs to help its employees generate data-driven outcomes should be part of any preparation strategy.
There are a lot of big data in every aspect of our lives. What continuously is rising is the requirement to gather and retain all raw information and numbers, regardless of the current magnitude, acceptable to ensure nothing is overlooked. This leads to the generation of a vast amount of data in nearly every discipline. The need for a big amount of data and statistics to drive current company practices and overtake rivals is a top priority for IT professionals right now because of the vital part it plays in generating options, developing new approaches, and getting ahead of rivals. In the analytics of big data, there is a significant need for specialists who benefit to procure and inspect each of the ballistics data which is stored. This holds various chances for those who fill these roles.
enterprise-wide big data solutions Australia issues faced by customers are solved by Australia-based firms to assist their businesses to realize their digital potential. Among the big data business solutions, we provide our big data strategy, real-time big data analytics, machine learning, digital transformation management, and analytics solutions. As a result, since they think that any organization can become a data-driven company, it helps you to put in place a complete long-term strategy and shine the focus on big data analytics services.
Big data analytics is significant by disrupting many sectors
Organizations may utilize big data analytics to make use of their data and utilize it to uncover new possibilities. As a result, a firm will be better about its operational decisions, more efficient at running its operations, and will have a greater return on investment and better-off consumers. A Marketing Analytics platform is built for a crowdfunding platform in order to generate improved campaign performance.
To a rising number of businesses, big data is no longer a choice but an unavoidable development, as both the volume of structured and unstructured data is increasing fast, along with a vast network of IoT devices that collect and process it.
The significant prospects for corporate expansion provided by big data exist for every sector, including:
IT infrastructures that use data-driven systems enable firms to automate time-consuming tasks such as data collecting and processing.
Over time, the usage of big data has led to new possibilities and trends which may be utilized to modify goods and services to meet the demands of end-users or to improve operational efficiency.
Data-based decision-making: Machines learn on huge data, which serves as the cornerstone of predictive analytics software, and therefore allows computers to act proactively with educated conclusions.
In terms of cost reduction, big data insights may be utilized to optimize corporate operations, eliminating superfluous expenditures while simultaneously boosting productivity.
Conclusion
Since all of the aforementioned factors have been introduced, it is no wonder that the significance of data as a corporate priority continues to expand.
The use of data to operate a company is becoming the norm in the fast-paced, technology-driven society we live in today. If you aren’t using data to lead your organization into the future, you’re almost certainly destined to become a company of the past!
While advanced analytics has made it simpler to expand your organization with data, unfortunately, the recent breakthroughs in data analysis and visualization have led to a significant reduction in the need for more data. We’ve included a guide for researching your own business data above so you can get critical insights to further your organization’s progress.
In a world that is becoming increasingly reliant on technology, data science plays an extremely important role in various sectors.Data scienceis the umbrella term that captures several fields, such as statistics, scientific methods, and data analysis. The goal of all these practices is to extract value from data. This data could be collected from the Internet, smartphones, sensors, consumers and many other sources. In short, data science is all around us; that’s part of what makes it such an exciting field.
Even now that we know that data science can be found all around us, you may still be wondering what industries are making the most of this fascinating technology? Let’s take a look at three industries that are putting data science to use in amazing ways.
According to the January 2021 Global Climate Report, January 2021 ranked as the seventh warmest January in the world’s 142-year records. It also marked the 45th consecutive January with temperatures over the 20th-century average. This indicates that climate change remains a prevalent issue.
It’s a particularly concerning issue for environmental public health professionals, who are designated to protect people from health and safety threats posed by their environments. This includes epidemiologists, environmental specialists, bioengineers, and air pollution analysts to name a few.
Public health experts use data sciencein a number of ways to facilitate their work. For instance, an environmental specialist would collect data such as air and water pollution levels, tree ring data, and more. They would then analyze that data to inform themselves and the public of environmental health concerns. Bioengineers, on the other hand, use big data on the environment to influence the engineering of various medical devices, including prosthetics and artificial organs.
Lending Industry
Have you ever taken out a loan or used a credit card? For most of us, the answer is yes, meaning that we’ve inherently benefited from the data science technologies leveraged by this industry.
Data science in the lending industryis incredibly important. In order to grant you (or any applicant) a loan, the lending agency must conduct arisk analysisto assess how likely it is that you’ll pay back the loan on time. They do this by collecting your data and analyzing it to assess your financial standing and level of desirability as a candidate. Some of this data could include your name, your past borrowing history, your loan repayment history, and your credit score.
Marketing Industry
In order to market to your target demographic, data is key. You need to know who you’re marketing to, what they like, what they do, how old they are, and more. This is why data science is so crucial and highly utilized in various forms of marketing.
Consideraffiliate marketingas an example. Affiliate marketing is a process in which an affiliate gets a commission in exchange for marketing another person’s or brand’s products. Data science is vital in this process. Data analytics are needed to increase return on investment (ROI), find new opportunities in foreign regions, and discover new resources to optimize campaigns.
Clearly, data science is a multifaceted field with uses across many industries. Environmental public health, credit lending, and marketing are just a few examples of sectors that leverage these incredible technologies.
In today’s time, digitalization is everywhere. Every sector is leveraging the benefits of digitalization, and the agriculture field is not behind when it comes to using the latest technologies and tools for enhanced productivity and efficiency. Today, people looking for an efficientfarm management solutioncan easily find a number of custom-made crop management applications. These applications help users manage tillage, seeding spraying, fertilization, irrigation, harvesting, and various other farming tasks.
An agriculture process management or farm management software can enhance and manage farm operations and production activities in a proficient manner. Various activities that can be managed easily using this software solution include:
Data storage
Record management
Monitoring and analyzing farming activities such as crop rotation, pest control, fertilizer/water saturation, seeding/harvesting, etc.
Streamlining production and work schedules
The best thing about afarm management solutionis that it can be customized to meet precise farm requirements. The customized solution allows users to manage and organize all their farm-related information in a centralized location. With the help of anagriculture process tracking solution, farmers can easily organize every aspect of farm management to augment productivity and farm operations. In addition, the solution helps them to keep a real-time check over their crops, workers, farm activities, tools & equipment, etc.
The software facilitates tracking real-time data about the farming activities, thus allowing users to make the best possible decisions based on real-time data. They can easily analyze the cost of production and other important activities and make decisions accordingly. Using farm management software, users can also monitor crop health, track rainfall, allocate resources efficiently, manage all kinds of field activities with ease, and generate real-time reports as and when required.
Features of Agriculture Process Tracking Application
Some of the key features of agriculture process tracking application include:
Real-Time Farm Monitoring:With the help of a farm management software solution, users can easily monitor every farm-related activity in real-time. Real-time monitoring helps detect any glitch in an early stage and can be rectified as soon as possible without creating any mess.
Analytics & Reports:The application software comes with the ability to generate a number of reports in real-time. This further helps to eliminate delays in the auditing process and makes tracking the farm’s progress a quick and flawless task.
Comprehensive Traceability:The feature allows users to create detailed traceability reports related to all the farming activities to improve risk management and reduce any unfortunate incidents.
Task Scheduling:The task scheduling feature allows users to perform every task on time without missing out or duplicating a particular task. Farmers and other related users can set their schedules as per their key requirements by selecting their template choice from a number of available templates.
Record-Keeping Abilities:The application allows users to keep a detailed record of all the farm-related activities as they happen systematically. Records of employees, irrigation, harvesting, and other production practices can be stored with ease, and the users can access these records as and when required.
Farmers Survey Data:The software can also be used for the survey purpose to get the right feedback from the farmers related to any farming activity, process, or tools. Based on the data gathered from the survey, informed decisions can be made to enhance the efficiency and profitability of the farming sector.
Benefits of Farm Management Software Solution
Some of the key benefits of using farm management software solution application include:
Better Planning and Tracking of Farm Activities:The solution allows users to plan and track farm activities more efficiently and better. With the help of this software, users can plan various activities, including when and how to carry out crop activities, the best suitable time for fertilizers, appropriate pest control measures, and others. It also allows users to track various activities in real-time, thus helps them make the best decisions related to farm operations.
Cost Savings:Agriculture process management software helps users to reduce various input and labor costs to a great extent. By integrating the operational and business data, the software helps in improving the efficiency, thereby helping in enhancing the ROI (Return on Investment).
Better Risk Manage:There are several types of risks that the agriculture sector has to deal with, including weather conditions, market demand, diseases, etc. With the help of a farm management software solution that consists of various features like weather forecasting, users can easily take the best possible measures to avoid or at least reduce the risk to a great extent. The software helps determine the weather condition, pest/disease occurrence, the health of the soil, and much more and accordingly alerts farmers about any impending risks to take proactive measures on time.
With all the features mentioned above and benefits, customized farm management software is ideal for people associated with the agriculture sector. It helps in generating the best strategies and methods to keep an agricultural farm productive and profitable.
Conclusion:
To conclude, it would be correct to say that with the help of smart innovations likeagriculture process tracking solutions, the farming business can be made more efficient, convenient, predictable, and profitable.
In today’s time, digitalization is everywhere. Every sector is leveraging the benefits of digitalization, and the agriculture field is not behind when it comes to using the latest technologies and tools for enhanced productivity and efficiency. Today, people looking for an efficientfarm management solutioncan easily find a number of custom-made crop management applications. These applications help users manage tillage, seeding spraying, fertilization, irrigation, harvesting, and various other farming tasks.
An agriculture process management or farm management software can enhance and manage farm operations and production activities in a proficient manner. Various activities that can be managed easily using this software solution include:
Data storage
Record management
Monitoring and analyzing farming activities such as crop rotation, pest control, fertilizer/water saturation, seeding/harvesting, etc.
Streamlining production and work schedules
The best thing about afarm management solutionis that it can be customized to meet precise farm requirements. The customized solution allows users to manage and organize all their farm-related information in a centralized location. With the help of anagriculture process tracking solution, farmers can easily organize every aspect of farm management to augment productivity and farm operations. In addition, the solution helps them to keep a real-time check over their crops, workers, farm activities, tools & equipment, etc.
The software facilitates tracking real-time data about the farming activities, thus allowing users to make the best possible decisions based on real-time data. They can easily analyze the cost of production and other important activities and make decisions accordingly. Using farm management software, users can also monitor crop health, track rainfall, allocate resources efficiently, manage all kinds of field activities with ease, and generate real-time reports as and when required.
Features of Agriculture Process Tracking Application
Some of the key features of agriculture process tracking application include:
Real-Time Farm Monitoring:With the help of a farm management software solution, users can easily monitor every farm-related activity in real-time. Real-time monitoring helps detect any glitch in an early stage and can be rectified as soon as possible without creating any mess.
Analytics & Reports:The application software comes with the ability to generate a number of reports in real-time. This further helps to eliminate delays in the auditing process and makes tracking the farm’s progress a quick and flawless task.
Comprehensive Traceability:The feature allows users to create detailed traceability reports related to all the farming activities to improve risk management and reduce any unfortunate incidents.
Task Scheduling:The task scheduling feature allows users to perform every task on time without missing out or duplicating a particular task. Farmers and other related users can set their schedules as per their key requirements by selecting their template choice from a number of available templates.
Record-Keeping Abilities:The application allows users to keep a detailed record of all the farm-related activities as they happen systematically. Records of employees, irrigation, harvesting, and other production practices can be stored with ease, and the users can access these records as and when required.
Farmers Survey Data:The software can also be used for the survey purpose to get the right feedback from the farmers related to any farming activity, process, or tools. Based on the data gathered from the survey, informed decisions can be made to enhance the efficiency and profitability of the farming sector.
Benefits of Farm Management Software Solution
Some of the key benefits of using farm management software solution application include:
Better Planning and Tracking of Farm Activities:The solution allows users to plan and track farm activities more efficiently and better. With the help of this software, users can plan various activities, including when and how to carry out crop activities, the best suitable time for fertilizers, appropriate pest control measures, and others. It also allows users to track various activities in real-time, thus helps them make the best decisions related to farm operations.
Cost Savings:Agriculture process management software helps users to reduce various input and labor costs to a great extent. By integrating the operational and business data, the software helps in improving the efficiency, thereby helping in enhancing the ROI (Return on Investment).
Better Risk Manage:There are several types of risks that the agriculture sector has to deal with, including weather conditions, market demand, diseases, etc. With the help of a farm management software solution that consists of various features like weather forecasting, users can easily take the best possible measures to avoid or at least reduce the risk to a great extent. The software helps determine the weather condition, pest/disease occurrence, the health of the soil, and much more and accordingly alerts farmers about any impending risks to take proactive measures on time.
With all the features mentioned above and benefits, customized farm management software is ideal for people associated with the agriculture sector. It helps in generating the best strategies and methods to keep an agricultural farm productive and profitable.
Conclusion:
To conclude, it would be correct to say that with the help of smart innovations likeagriculture process tracking solutions, the farming business can be made more efficient, convenient, predictable, and profitable.
In the blog “What Movies Can Teach Us About Prospering in an AI World – Part 1”, I laid out the challenge that us humans will face surviving in a world dominated by AI. I discussed the different learning techniques – such as machine learning, deep learning, reinforcement learning, and transfer learning – that are available to AI to accelerate its learning. If success in the future is purely defined by how fast one can learn, then us humans are truly doomed.
But are we really doomed? Nah, I think us humans still have a few tricks up our sleeves, but we need to reframe the value that humans will bring to a world of AI.
“Big” is one of my favorite movies, and Tom Hanks is one of my favorite actors (though I still want Harrison Ford to play me in my movie). There is lots to like about the movie, but the scene that really sticks with me is when the “adults” are brainstorming about creating a toy that looks like a building but transforms into a robot. Review that scene here…
Josh (Tom Hanks): “I don’t get it.”
Mr. MacMillan (the boss played by Robert Loggia): “What don’t you get, Josh?”
Josh (Tom Hanks): “There’s a million robots that turn into something. This is a building that turns into a robot. What’s fun about playing with a building? That’s not any fun.”
Yes! Us humans aren’t doomed to the dumpster. We still have a role to play in a world driven by AI. But that means we need to channel our inner Tom Hanks (Josh) and be willing to raise our hands when something just doesn’t make sense.
Let’s say a person is applying for a car loan where today those decisions are more and more being made by an AI model. The AI loan application model rejects the applicant because of the applicant’s spotty income history, student loan struggles, and sporadic work history. The AI model assumes that the applicant’s past behaviors is indicative of future outcomes because that is all the AI model has to work with!
Now, a (human) loan officer intercedes on the application process. The loan officer asks the applicant some questions about their intentions for the loan. The applicant explains that they want to buy a nicer car because they want to become an Uber / Lyft / DoorDash driver to generate a more predictable income stream. The loan officer decides to give the loan applicant the loan.
Now, this is a scenario that the AI loan model hasn’t seen in the data, and consequently couldn’t easily extrapolate to ask those questions to make a more informed decision. While the “safe” choice would have been to reject the loan application, the bigger picture is that if organizations can’t evolve to contemplate these edge cases, over time, they risk shrinking their total addressable market (see Figure 2).
So, what’s the key to embracing our inner Tom Hanks? Here are my top 10 reasons (David Letterman anyone?) for the reason why humans will excel in a world of AI, if we learn to embrace the fact that AI will force humans to become more human.
Not sure why 10 is some magically number (Number of fingers on your hand? Number of frames in bowlin’? Bo Derek?), but here is my list of the top 10 human behaviors that can overcome that massive learning advantage that AI models have over us:
1) Thoroughly Align Goals. Invest the time upfront to thoroughly align and vet goals across a holistic and diverse set of stakeholders. Be sure that everyone is clear on what is trying to be achieved and why it’s important to each stakeholder (a key part of my “Thinking Like a Data Scientist” process). Bring together the different stakeholders – internal and external – who either impact or are impacted by the goals. Brainstorm (and prioritize) the metrics and KPIs against which the team will measure progress and success. Leverage “future visioning” exercises to help all stakeholders to imagine what success looks like…to them.
2) Embrace Diversity of Perspectives. AI models learn by identifying and codifying patterns, trends, and relationships buried in the data. Diversity and outliers are not the friends of an AI model because they can skew the analytic results. And that’s an area where humans can truly excel (if we can learn to overcome our own confirmation biases). Think holistically about the variables and metrics against which you want the AI model to optimize, and not just the financial metrics, but include customer, operational, environmental, societal, and diversity metrics as well. Diversity may be the human secret weapon. Diverse perspectives can create friction and friction can lead to synergizing new ideas. There cannot be innovation without friction. So be inclusive and welcoming of different perspectives.
3) Brainstorm What Could Go Wrong. Empower the naysayers. Bring together folks who support as well as folks who do not support the goals. Brainstorm all the possible ways that things can go wrong. All ideas are worthy of consideration. Invest the time to understand and quantify the costs of the models being wrong. Don’t allow Groupthink, which is the practice of making decisions as a group in a way that discourages creativity or individual responsibility. For a history lesson on Groupthink, check out the Bay of Pigs fiasco.
4) Empower to Challenge Conventional Thinking. Empower everyone to think for themselves and question AI authority (channel your inner Timothy Leary). Embrace the power of “I don’t get it?”. Empower your team members to raise their hands and stand up to challenge the thinking that’s on the table. Embrace an operating model of “Disagree and commit” where folks are allowed to disagree while a decision is being made, but that once that decision is made, everybody must commit to full-on execution of that decision. Be aware of and root out Passive Aggressive behaviors amongst the “Disagree and Commit” crowd. No Pollyannish mentality here.
5) Collaborative Intelligence. Embrace the power of well-aligned, collaborative, diverse teams to bend, break, and blend traditional thinking and approaches into something new and more powerful. Become the master at collaboration. Uncover everyone’s unique assets and synergize the collaboration across those unique assets. Yes, each of us is special (Mister Rogers). Collaborate across different perspectives and experiences to create something greater than the sum of the parts. Resistance is not futile!
6) Empathize. Be more human by being more understanding. Seek first to understand before trying to be understood (Thanks Stephen Covey). Seek to intimately understand your customers. And broaden definition of customers to include all those who you seek to serve, which should include your work colleagues. Embrace empathy, walk in the shoes of others, stand up for what’s right, and truly care about others. Design Thinking provides some marvelous tools if one has the right mindset to truly seek to empathize with their customers.
7) Stay Curious. Innovation is driven by Curiosity. Embrace your inner 5-year-old. Try to understand why things work the way that they do. Don’t be afraid to take apart that radio. Leverage your natural curiosity and turn curiosity into creativity (envision, create, try, fail, learn, re-create, and try again) and turn creativity into innovation. Remember, the base of the word “creativity” is “create”, so don’t be afraid to create or build schtuff. And even if that schtuff doesn’t work, use that as motivation to fuel even more curiosity and create even more schtuff. Build baby, Build!
8) Embrace Organizational Improv. Identify and leverage everyone’s unique “assets” to achieve team and organizational agility. Prepare everyone to lead because at different times in organizational improv, everyone will have to lead. Execute like the US Women’s Olympic Soccer team or a great jazz quartet where everyone is prepared to take their shot or play their riff in sync with the rest of the team. Think expanding team swirls not limiting organizational boxes. Transition from a mindset of compromise to a mindset of abundance where everyone can win, and drive team execution from settling on the “Least Worst” to transforming to “Best Best” decisions.
9) Prepare to Unlearn. Don’t be held captive by your outdated mental models. Don’t be that person who falls back on “That’s the way that we’ve always done it.” Don’t be that guy. The world and capabilities are continuously changing, so challenging your conventional models may be the only way to stay current and valuable. Besides, you can’t climb a ladder if you aren’t willing to let go of the rung below you.
10) Find Your Spiritual Foundation. Ethics must be the foundation for our efforts to become more human. Forgiveness, generosity, caring, compassion. Think about the critical difference between “Do no harm” versus “Do good.” And if you’ve forgotten the difference, re-read the Fable of the Good Samaritan. Be more righteous and sincerely care about others, build for a better future, answer to a higher power. The best textbooks for being more human? The Bible, Torah, Shruti, Koran, or whatever your religious foundation. And finally, when in doubt about the right actions to take, apply the “Mom Test” – that is, what would your mom think if you were to explain your action to your mom (and hopefully your mom isn’t Ma Parker).
AI is going to force us humans to focus on nurturing the creativity and innovation skills that distinctly make us humans and differentiate us from the analytical machines. Innovation and creativity are the human ability and the willingness to be curious, ask provocative and challenging questions (like Tom Hanks in the movie “Big”), embrace diverse ideas and perspectives, blend these different ideas and perspectives into a new perspective (frame), and explore, test, fail, learn, test again, fail again, and learn again in applicability of the new blended perspective to real-world challenges.
Agile Data Governance is the process of improving data assets by iteratively capturing knowledge as data producers and consumers work together so that everyone can benefit. It adapts the deeply proven best practices of Agile and Open software development to data and analytics. It begins with the identification of a business problem, following by the gathering of stakeholders who are aware of the issue and are working to address it.
Agile data governance focuses on self-service analytics and seeks to provide support much closer to the point where data is consumed. It is supported by tools that assist in the delivery of data knowledge to data users.
Importance of Agile Data Governance:
Data breadlines: At the data producer’s threshold, there are bottlenecks. While serving one spontaneous data request after another, data consumers can’t keep up. Consumers are dissatisfied with the time it takes to receive what they want. Projects using analytics quickly devolve into lengthy email chains. Data consumers, data producers, and domain experts iterate collaboratively using agile principles to create reusable assets that reduce the frequency of ad-hoc requests. New spontaneous requests will be saved alongside cataloged data assets and analyzed so that the other person can identify and use them before approaching data producers for assistance.
Data silos: Agile Data Governance enables data consumers to obtain and iterate on data assets in a direct and clear way. This minimizes the chance of emailed spreadsheets. Furthermore, information assets will be well-documented, allowing more users to access, understand, and use them.
Data brawls: People would lose faith in data work if it is not transparent. After months of work, people come up with new versions of the same analysis. They quarrel over data sources, small ones, and even project objectives. Transparency in Agile Data Governance means that correction and peer review occur as the analysis progresses. This results in a common understanding that may be incorporated into company glossaries.
Data obscurity: In many organizations, those who try to understand the availability and usage of data assets encounter partial answers, inefficiencies, and perplexing processes. Documentation is primarily a problem, and disconnected tools that aren’t designed for agile processes make it a job and an afterthought. Agile Data Governance allows you to document your work while doing it. This near-real-time documentation raises awareness of what data exists, what it means, and how to use it all around the world.
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