There are two ways to go about generating or implementing predictive analytics: purely on the basis of your data (with no prior knowledge of what you’re after) or with a proposed business goal that the data may or may not support. You don’t have to choose one or the other; the two approaches can be complementary. Each has its advantages and disadvantages.

Both approaches to predictive analytics have their limitations; keep risk management in mind as you cross-examine their results. Which approach do you find to be both promising of good results and relatively safe?

Combining both types of analysis empowers your business and enables you to expand your understanding, insight, and awareness of your business and your customers. It makes your decision process smarter and subsequently more profitable.

How to generate data-driven predictive analytics

If you’re basing your analysis purely on existing data, you can use internal data — accumulated by your company over the years — or external data (often purchased from a source outside your company) that is relevant to your line of business.

To make sense of that data, you can employ data-mining tools to overcome both its complexity and size; reveal some patterns you were not aware of; uncover some associations and links within your data; and use your findings to generate new categorizations, new insights and new understanding.

Data-driven analysis can even reveal a gem or two that can radically improve your business — all of which gives this approach an element of surprise that feeds on curiosity and builds anticipation.

Data-driven analysis is best suited for large datasets because it’s hard for human beings to wrap their minds around huge amounts of data. Data-mining tools and visualization techniques help you get a closer look and cut the overwhelming mass of data down to size. Keep these general principles in mind:

  • The more complete your data is, the better the outcome of data-driven analytics. If you have extensive data that has key information to the variables you’re measuring, and spans an extended period of time, you’re guaranteed to discover something new about your business.

  • Data-driven analytics is neutral because no prior knowledge about the data is necessary and you’re not after a specific goal in particular, but analyzing the data for the sake of it.

  • The nature of this analysis is broad and it does not concern itself with a specific search or validation of a preconceived idea. This approach to analytics can be viewed as sort of random and broad data mining.

  • If you conduct such data analysis, and if you learn something about your business from the analysis, you’ll still need to decide whether the results you’re getting are worth implementing or acting upon.

  • Relying solely on data-driven analytics adds some risk to the resulting business decisions. You can, however, limit that risk by incorporating some of the realism that characterizes user-driven analytics.

    When real-world data proves (or at least supports) the correctness of your original ideas, then the appropriate decision is practically already made. When an informed hunch is validated by the data, the whole analysis shows itself as driven by strategic ideas that were worth pursuing and verifying.

How to generate user-driven predictive analytics

The user-driven approach to predictive analytics starts with you (or your managers) conceiving of ideas and then taking refuge in your data to see whether those ideas have merit, would stand testing, and are supported by the data.

The test data can be a very small subset of your total business data; it’s something you define and choose as you deem relevant for testing your ideas.

The process of picking the right datasets and designing accurate testing methods — in fact, the whole process from inception to adoption — has to be guided by careful consideration and meticulous planning.

User-driven analytics requires not only strategic thinking but also enough in-depth knowledge of the business domain to back up the strategizing. Vision and intuition can be very helpful here; you’re looking for how the data lends specific support to ideas you deemed important and strategic. This approach to predictive analytics is defined by the scope of the ideas you’re probing. Decision-making becomes easier when the data supports your ideas.

The process of probing your ideas may not be as straightforward as analyzing whole datasets. It can also be affected by your bias to prove the correctness of your initial assumptions.

Here is a comparison of data-driven and user-driven data.

Characteristics Data-Driven User-Driven
Business Knowledge Needed No prior knowledge In-depth domain knowledge
Analysis and Tools Used Broad use of data-mining tools Specific design for analysis and testing
Big Data Suited for large-scale data Applied on smaller datasets
Analysis Scope Open scope Limited scope
Analysis Conclusion Needs verification of results Easier adoption of analysis results
Data Pattern Uncovers patterns and associations May miss hidden patterns and associations