Making Smart Strategic Decisions from Reliable intelligence

“If you torture the data long enough, it will confess [to anything].” Ronald Coase

The goal here is to highlight the range of statistical and visualisation tools that are available to analysts to glean insights from data. A secondary goal is to urge analysts to use the appropriate tools and, where necessary, to learn more sophisticated tools that can provide a deeper level of understanding of the problem at hand.

What is the difference between Data, Information and Knowledge? Data is the raw, unprocessed facts about the world. Information is captured, processed data, while knowledge is a set of mental models and beliefs about the world built from information over time.

The Goal of the Analyst: Transform data assets into competitive insights, that will drive business decisions and actions using people, processes and technologies.

Data Collection

In his book, DJ Patil remarks: It’s easy to pretend that you’re data driven. But if you get into the mindset to collect and measure everything you can, and think about what the data you’ve collected means, you’ll be ahead of most of the organizations that claim to be data driven.

Collect and measure everything that you can. You never know what you might need, you often only have one chance to collect the data, and you’ll kick yourself later when you need it and it is no longer accessible. The more data that you collect, the greater the chance that you have to model and understand the users’ behaviour (as in the checkout example) and, importantly, their context—context is king.

That is, the more that an organization understands about the individual users, their tastes, intentions, and desires, the more it can improve the user experience through personalization, recommendation, or more fine-grained services that reach down the “long tail.”

The Cost of Data & Return on Investment

As “big data” becomes the cure-all for many business optimization decisions, it is increasingly important for managers to be able to evaluate their data-driven decisions and justify the investments made in acquiring and using data. Without the tools to make such evaluations, big data is more of a faith-based initiative than a scientific practice.

A data-driven organization should think carefully about the value of its data. Primary focus should be on its core data, where any downtime may have a real impact. It should consider deleting old, irrelevant data (this is easier said than done); but if nothing else, it should consider moving it to the cheapest suitable medium, such as on local servers or cloud based storage.