The following is an extract from Eric Wilson’s new book, Predictive Analytics For Business Forecasting & Planning, written by Eric Wilson CPF. It is your guidebook to the predictive analytics revolution. Get your copy.
Most of what is discussed around predictive analytics is in terms of a new way to forecast where you are not just looking at your internal sales history, but also you are bringing in more data, different drivers, and other external variables to improve your forecast.
We have focused on using predictive analytics to help in discovering “Why do things happen?” and the benefits of translating this into “What could happen if…?” With advances in technology, we now have advanced models and methods that can better enable predictive analytics.
The Demand Planner or predictive analytics professional blends forecasting and business intelligence. They merge techniques and methods including machine learning to support the business’s needs. A common misconception is that machine learning, business forecasting, advanced business intelligence, and all things predictive analytics are synonymous.
The diversity of opinion reflects the fluidity of how we understand the defining language of the field. Business forecasting is the process to extract information and provide insights. Machine learning is a subset or application of AI and is more of an approach than a process. Business intelligence is the different types of analytics and outputs.
Where they overlap is the intersection of process, approach, and insights of predictive analytics.
Business Intelligence or BI focuses on infrastructure and output. The use of the term business
intelligence can be traced to the mid- to late-1860s, but it would be a century later before consultant. Howard Dresner was credited with coining the term. His definition was, “Business Intelligence: An umbrella term that covers architectures, databases, analytical tools, applications, and methodologies used for applying data analysis techniques to support business decision-making.” Despite this definition, historically, when people thought of business intelligence, most considered it as a fancy way of talking about data reporting. It has always been much bigger than just a dashboard and, through the years, people have begun to better understand the breadth and uses of BI to inform data-driven business decisions.
Fig b | Infographic depicting intersection of AI, BI, and business forecasting
Predictive analytics in BI has become a natural and needed progression of decision-making capabilities and insights. Where most of BI focused on visualization of data and descriptive type analytics, with predictive analytics we are asking more what could happen or even what we can make happen as an organization. Predictive analytics helps in presenting actionable information to help executives, managers, and other corporate end-users to make informed business decisions. Overall, predictive analytics can help discover why things happen and use this knowledge to reveal what could happen in future.
Business Forecasting: The process of using analytics, data, insights, and experience to make predictions and answer questions for various business needs. It is a process of breaking something down into its constituent elements to understand the whole and make predictions. Where BI is about the tools and representation, business forecasting is the analysis and procedures.
Predictive analytics in business forecasting has become a more advanced process that encompasses more and different types of data, more forward-looking causal type models, and more advanced algorithms and technology. It uses several tools, data mining methodologies, forecasting methods, analytical models (including machine learning approaches), and descriptive and predictive variables to analyze historical and current data, assess risk and opportunities, and make predictions.
Instead of just historical sales, we are trying to better understand the factors or the likely purchase behavior of the buyer. Predictive analytics is a new way to forecast where you are not just looking at your internal sales history, but you are bringing in more data, different drivers, and other external variables to improve your forecast.
Machine Learning involves different approaches and methodologies. It is a subset of AI and is a
collection of different techniques, methods, modeling, and programming that allow systems to learn automatically.10 Machine Learning: An algorithm or technique that enables systems to be “trained” and to learn patterns from inputs and subsequently recalibrate from experience without being explicitly programmed. Unlike other approaches, these techniques and algorithms strive to learn as they are presented with new data and can forecast and mine data independently.
For predictive analytics, machine learning has opened new opportunities and provides more
advanced methods for it to use. Predictive analytics in machine learning is a category of approaches to achieve better forecasts, improved intelligence, automation of processes, and a path to AI. Some new advanced models and methods can be incorporated to further enable predictive analytics.
At the intersection of advanced business forecasting, mature business intelligence, and some machine learning techniques, is predictive analytics. Predictive Analytics: A process and strategy that uses a variety of advanced statistical algorithms to detect patterns and conditions that may occur in the future for insights into what will happen.
Predictive analytics used to be out of reach for most organizations. However, recent advances
in professional skills, increased data, and new technologies, including machine learning and AI
techniques, have made it much more accessible. Predictive analytics utilizes many advanced business and planning processes to provide more information with less latency and improved efficiency. This is not just about advanced analytics outputs and business intelligence; it also offers more mature organizations a view of what and why things occur. Finally, while predictive analytics may use some machine learning techniques, it is only a portion of the planner’s toolbox, along with other statistical and data mining techniques.
This article is an extract from the book Predictive Analytics For Business Forecasting & Planning, written by Eric Wilson CPF. It is your guidebook to the predictive analytics revolution. Get your copy.