There are many confusing definitions for Business Intelligence (BI) and predictive analytics in books, magazines and blogs. It’s often hard to decipher how they differ, and the vendors selling one or both often don’t make it any easier to distinguish between these two key business decision-making practices.
In developing materials for my Data & Analytics consulting practice, I have a created a simple comparative model to help my clients understand how business intelligence differs from predictive analytics.
Both BI and predictive analytics rely on data to drive decision making, but that’s where most of the similarity ends.
- With BI, raw data is processed into information for consumption by people. Your organization’s key decision makers slog through the information, slicing and dicing their way through data cubes and various dimensions of the data, e.g., Product, Client, Region, Quarter for sales data.
- With predictive analytics, however, raw data is processed into “cleaned data” for consumption by algorithms. The volume and complexity of the data is more than our grey matter can digest.
- With BI, your people review and analyze the information presented, hoping to gain some as yet unrealized insight to solve a business problem, which often includes some guesswork, assumption-making, and reliance on subjective experience.
- With predictive analytics, algorithms detect complex patterns and create a model that IS the insight, powerfully illuminating logical pathways forward to address your key business concerns, and importantly, doing so free from the inherent limitations of even the best human thinking.
- With BI, people make decisions on what to do with their insight, choosing what they believe may be the best option, but often without strong, tangible evidence that they are correct.
- With predictive analytics, the model tells you the best decision to make based on the data presented, providing your organization with strong and entirely fact-based solutions.
- With both BI and predictive analytics, your organization’s decision makers take action according to the decision made, differing only in the pathway to that action. That pathway, however, is the critical difference between the two business decision-making practices.
So in summary, BI leaves all the analysis, subsequent insight and decision-making for people to manage, often in a time-consuming, laborious process, with all the inherent risk associated with human fallibility in logic and reasoning. In contrast, predictive analytics efficiently and effortlessly analyzes your data, objectively generates powerful insight, and provides accurate direction that can guide your business decision makers forward with more confidence in the likelihood of successful outcomes.
There are, of course, many other differences and details that are not considered in this model, but I intended the model to be simple and provide a foundation for understanding the specific key differences between the processes of business intelligence and predictive analytics. So the next time you consider the question of how BI and predictive analytics differ, you can keep this simple comparative model in mind.
On a final note, while the process of predictive analytics is fairly straightforward, the skills, experience and tools required to apply algorithms to data with a goal of creating predictive models are significantly different from those that most organizations and their staff currently have. So even if your organization functions well in business intelligence, and is well-resourced with people and tools generally needed to create cubes, dashboards and reports from a BI system, you will likely need to procure outside help once you are ready to employ predictive analytics and take your business decision-making practices to the next level.
Interested in exploring how the power of predictive analytics could help solve your key business concerns? To learn more, send me an email at firstname.lastname@example.org.