Machine Learning, AI, and Prescriptive Model Analytics Redefines Competition

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These buzz words are overused, even bordering on cliche. However, given the right IT partner and platform, they define real capabilities with tremendous competitive potential. They are at the epicenter of data, analytics, and supply-chain innovation. Whether you’re predicting a consumer purchase, an election victory, or the insured property damage of a 100-year storm, the aforementioned disciplines play an increasing, if not essential, role. Through careful investment in prescriptive model analytic platforms, organizations are now able to gain insights from data sets so large and complex that they could not have been processed just a few years ago. As a result, increasingly more decision-makers are able to complement internal, historical records with big data (both owned and non-proprietary) for additional regression analyses and vastly improved forecast accuracy.

Other organizations are using machine learning algorithms to decode unstructured data, recognize patterns and nuances in human expression, improve language translation, and redefine human-to-computer interaction.

On the prescriptive model analytics front, data-mature organizations have for years been applying optimization models, Monte Carlo simulations, and other forms of analyses to test business decisions and optimize policies and plans for inventory, distribution, pricing, and much more.

People, process, technology

Whether to advance capabilities around big data, machine learning, or prescriptive analytics, smart organizations are pulling ahead of the competition by investing in advanced-analytic technology. Those who do it best usually accompany their investment with the following essential ingredients:

  • Defined, measurable objectives
  • Management buy-in
  • New business processes
  • Effective workforce communications

Complex problems, intuitive solutions

Processes aside, decision-makers are often most challenged in their ability to define optimal demand plans, safety-stock levels, order schedules, and other operational variables. Each of these plans or policies is a set of crucial decisions with potentially drastic ramifications. But with the proper use of advanced analytics, organizations can turn the wildly chaotic data that surround these challenges into information and insights that enable better outcomes. Indeed, enhanced predictive accuracy and prescriptive-analytic decision making have great potential to improve customer satisfaction, profitability, and cash-flow performance.

It’s important to note here that management decision makers need not become experts in advanced-analytic capabilities (big data, machine learning, predictive analytics, prescriptive analytics) to reap substantial value from them. On the contrary, best-in-class analytics applications are designed to make expert-level algorithmic and data technology accessible to non-technical business users. The end goal is to help people make optimal decisions by letting the software do what it does best; crush dizzyingly complex math problems so that human decision makers can see clear choices.

Prescriptive model analytics: the next layer

As more and more data course through our networks, best-in-class applications are beginning to learn from previously undetectable patterns and nuances in the data; and they are becoming increasingly better predictors of the future. They’re not just predictors though, they’re also prescribers, able to present a virtually infinite range of potential outcomes and probabilities based on a virtually infinite range of unique inputs and scenarios.

Better yet, all of this is happening while we human decision maker are mulling strategy, or performing tasks. This is the power of advanced analytics. Done correctly, the software does the heavy lifting of transforming complex data into usable information for decision making. It’s the perfect marriage of information technology, and human domain knowledge and know-how.

Yesterday to tomorrow

Leading organizations have been exploiting advanced-analytic technologies for years, namely predictive and prescriptive analytics. We’ve long captured historical events, transactions, and other records to extrapolate into expected futures – using a wide variety of statistical techniques. The difference now is in the advancement of big data technology, which lets us capture and process much larger, and potentially non-homogenous (or non-normalized), data sets.

In my opinion, the capturing and processing of big data will soon become a standard best practice for maximizing returns on data. I’ll save a deeper dive into big data for another blog entry. For now, rest assured that Vanguard Software solutions remain at the forefront of both big data and prescriptive model analytics. Our clients generate millions of forecasts in a fraction of the time it takes with lesser technologies.

With respect to artificial intelligence (AI), machine learning is among the more useful technologies for providing measurable value to current organizations. Vanguard Software clients have made good on our machine learning algorithms for some time now, though we plan to innovate further. We will continue to push new techniques in deep learning, advance our use of neural networks, and optimize and enhance our use of machine learning algorithms.

At the end of the day, all of these technologies have one thing in common; they provide business value to users, organizations, and increasingly multilateral supply-chain networks. With the right partner and platform, business cooperatives gain the power to process huge datasets, run exhaustive simulations, predict myriad futures, and execute best-possible courses of actions. We are close to business nirvana!