If you’ve spent any time in the Big Data, analytics or BI space, you’ve probably heard the analogy, “data is the new oil.” The phrase is meant to communicate the idea that data can be incredibly valuable but only if used in the right way. Simply possessing oil, or data in this case, means little to your business — it’s what you do with it that counts.
Accordingly, there has been a slew of a smart data discovery tools introduced to the enterprise in an effort to monetize data. Gartner defines smart data discovery as “a next-generation data discovery capability that makes insights from advanced analytics accessible to business users or citizen data scientists.”
Essentially, these tools make it easy for the average user to find and understand the insights hidden in their data. Assorted tools offer a range of benefits including streamlining data prep and automatically highlighting important findings via visualizations or narratives. These tools can do all these things without a user ever building a model or writing an algorithm.
What else is interesting is that although new algorithms are introduced (and are replaced) frequently, the general methodology used to analyze data and drive organizational change has not changed much over the years. The biggest change has been the trend towards automation of these methodologies through the introduction of smart data discovery tools enabling organizations to scale their analytics practices.
From my perspective, informed business analysis practices are even more important with the introduction of smart data discovery tools for the enterprise. Formalizing or even revisiting best practices while adopting new tools is a must. Per our oil analogy, best practices may mean the difference between monetizing a new pipeline or an offshore disaster.
Here are 5 reminders for any organization when adopting smart data discovery tools:
As the focus of many smart data discovery tools is to automate parts of the extraction, cleansing and analysis process, customers should look out for tools that can provide governance and auditability into this process. The ability to identify the who, what and why data changed or explain data inconsistencies is tantamount to trust and adoption of tools within the enterprise.
This is especially true of specific industries in which this information is highly regulated, such as financial services and healthcare.
Similarly, transparency of analyses allows users to build trust in a system which may ease adoption of emerging tools as advanced techniques are now hidden behind a simplified experience to reach the business user.
An added benefit of transparency is the ability for a tool to describe the steps it took to arrive at a specific outcome, which provides organizations with actionable insights instead of struggling to understand a “black box.