By Greg Silverman, Jun 10, 2020
Nearly a decade ago, Gartner released their Analytic Ascendancy Model, defining the value different methods of analytics bring to businesses. This model served as inspiration for analysts and business decision-makers alike striving to get more from their data to guide strategic actions.
While data analysis has been around for more than half a century to help businesses make sense of the response to their decisions, the past decade witnessed the most dramatic change in analytic capabilities yet. Much of this is chalked up to the advancements in technology, especially in business applications. Organizations have made it their mission to climb the data ladder presented by Gartner, but only recently has the peak of forecasting been achieved for everyday business users needing to turn their insights into data-driven decisions.
Let’s look back on how a decade of decisions has evolved with new technologies and developments that are making accurate foresight a reality.
The beginning of data science as a practice has its roots in the 1960s. As commercial computers began arriving on the business scene, mathematicians saw how their knowledge of statistics and the storage capacity and automation computers were capable of could benefit organizations needing to make sense of large stores of data.
While computers have long been used to help businesses solve complicated problems, it was not until the turn of the century when Gartner’s model began to take shape. Breakthroughs in consumer research - such as mass surveys through different channels and new ways to track online behavior - made it easier for businesses to understand their target market. Not only was this data abundant, but it was also high-quality - typically direct from the consumer.
This source of rich information helped organizations build their stores of descriptive data and subsequently run analyses to identify what happened in the market. Furthermore, diagnostic analytics were used to determine why things happened the way they did. While this approach to analysis was helpful in identifying trends in consumer behavior, it was never capable of forecasting the market’s reaction to decisions in the moment.
Descriptive and diagnostic analytics are the first half of Gartner’s model. They set the foundation for forecasting by emphasizing the importance of consumer data and an understanding of the customer journey. It also inspired the creation of marketing mix modeling that provided some insight into how their actions affected the market outcome.
However, businesses soon became reliant on this method and began using their models as a predictive tool. Diagnostic analysis only helps businesses understand why something happened in the past, not how it could happen in the future. While misguided in their approach, modeling of this nature led the way for the next generation of analytics.
Nearly another decade later, from 2008-2009, further advancements in technology and the urgency businesses felt to remain competitive saw the development of predictive analytics. Machine learning and tools like Excel that had their roots in descriptive analytics helped make answering “what will happen next” a reality.
Instead of asking questions like “What happened when we changed our marketing strategy?” predictive analytics empowers teams to use their data on hand and other business intelligence tools to ask, “What will happen if we change our marketing strategy?” With this information, businesses shift their investment decisions and capitalize on actions that are forward-thinking rather than dwelling on the past.
This easily could have been where businesses stopped; however, with the release of Gartner’s model, organizations saw how the next level of data analysis further optimizes actions. After all, predictions were not always accurate, and businesses realized that while they had insight into the future, they did not know which actions would make it a reality. With this, the peak of forecasting was identified. While difficult to achieve, prescriptive analytics brings the most value to businesses.
It’s easy to say that hindsight is 20/20 when the results of a decision aren’t as predicted. However, looking back at what didn’t go according to plan does very little to make better plans of action in the moment. In the year 2020, we should aim to make the foresight just as accurate.
Foresight is the ability to predict, the act of looking forward, or the art of what will happen next. Though given multiple definitions, foresight allows businesses to take strategic action in response to predicted events. Predictive analytics, while beneficial, fails to provide business users with a plan of action on how to achieve their predictions and is why prescriptive analytics outranks it on Gartner’s model.
As the height of forecasting, prescriptive analytics not only gives precision on the future, but also provides businesses with the recommendations for how to make it happen. It uses data and simulation to understand a fluid market and consumer behavior in ways the human mind cannot comprehend. Only in the past decade has prescriptive analytics been developed and refined to deliver a self-service tool that businesses utilize in-house.
While handing over the reins to prescriptive analytics and allowing AI and machine learning to guide business actions may sound intimidating, it’s actually commonplace in our everyday lives. Consider every time you board a plane, turn on your GPS, or go to your doctor. Your pilot, phone, and health care professional are all following or giving you directions based on algorithms and data. Not only does this form of prescription give instructions, it gives the best possible instructions - the safest way to land a plane, the fastest way to your destination and the swiftest road to recovery.
We embrace prescriptive analytics and applaud how it makes our everyday lives easier, so why aren’t businesses using it to the benefit of their organization?
In the past decade, we have experienced an insurmountable change in the way society and business operate. Only prescriptive analytics has the ability to understand the complexities of evolving consumer behavior and market trends, so businesses are able to take insight from their data and turn it into strategic action poised for success.