Data without Process is Meaningless!

When you hear the terms “big data” or “analytics” what comes to mind?

Do you think of technical experts pushing exabytes of data through an algorithm? Perhaps you think of marketing experts attempting to get answers about your company’s customers.

No matter which scenario you think of, it is important to recognize that big data and analytics are most useful when their associated processes are in place and observed.

 In fact, big data and analytics are useless without process.

Why? Because quite simply, Data without process is meaningless!

The process that is used to acquire or create data is in fact vitally significant. While this has always been true, it has not been on the radar for most businesspeople. The passage of the Sarbanes-Oxley Act of 2002” [aka “Public Company Accounting Reform and Investor Protection Act" (in the Senate) and "Corporate and Auditing Accountability and Responsibility Act" (in the House)] is really the first overtly public acknowledgment that the process of how an organization gets data is financially as important as the data itself. For the first time in business history, companies and their public accounting firms were required to document and audit, not simply financial results, but also the processes by which they arrived at those results.

In a completely different area, software developers, web designers, and marketers are beginning to appreciate that better customer retention arises out of a deep awareness of the customer’s journey or “experience” — process.

Our business journey (our process) is every bit as important as our destination, i.e. (our goal or desired outcome).

Therefore, in looking at big data and analytics through the prism of business process, what does the focus need to be?

The business process community over the last ten years has begun shifting its focus from an inward view of process streamlining to an outward view that encompasses successful customer outcomes. With this shift of thinking in mind, how does the journey (process) of big data influence the outcome (destination)?

Start with data quality, which is a key component of big data and analytics. Without excellent data quality business managers cannot ask good questions about the data or make decisions that help move your company forward on the competitive landscape. In other words, without a solid business process that ensures quality data is collected, stored, and made available, managers cannot trust that the data they are using to make important decisions is valid.

Moving to big data (and assuming that data quality is now a non-issue) the concern becomes about data extraction and transformation processes from various, and frequently disparate, data sources into single repository. Once more, if those processes are not robust, business managers are unable to ask reliable business questions.

However, let us assume for the moment that data quality, along with extraction and transformation processes have been successfully addressed and are reliable.

Analytical manipulation of quality, transformed data also involves a variety of processes, mostly around mathematical formulas. Without documented, validated processes to ensure that the correct data from the right sources is being analyzed, managers will arrive at inaccurate decisions that negatively impact business.

At every step along the big data-analytics journey, process is at the crux and essential to business success. For data quality the foundation of the process is about trust. As you travel down the path to big data the heart of the process is about the questions that managers need to ask. By the time your journey arrives at the point of analyzing data the central focus of the process is about decision making.

Summary

Your journey, i.e. your processes, in big data and analytics is as important as your, or your manager’s, destination, i.e. accurate decisions. If you cannot describe your processes precisely you cannot defend the accuracy of data or rely upon them to yield desirable outcomes by way of enabling positive decision making.

Make certain that your data quality processes inspire trust so that management has confidence in the data that is transformed and used for asking questions, and making truly accurate and valuable business decisions.