A Brief Conversation about Data Governance
Reader: “Woah, woah, hold on a second. Really Mike? - A post on Data Governance? - Don't you represent QlikView!? Shouldn't you be blogging about Business Discovery, Big Data or those sexy Data Visualizations!?”
Mike T: “Easy now, take a moment and breath. <sarcastic>You seem to really know your trendy labels, don't you?</sarcastic> Before we can discover our business, visualize our data or understand if our Big Data's signal-to-noise ratio is even relevant – something more needs to happen. Applications and data are typically prepared from gathered requirements before they are deployed to the masses. However, it is this preparation process that will determine the accuracy, consistency, assurance and overall longevity of the BI solution; aspects commonly overlooked when a proper Data Governance framework is NOT in place.”
Reader: “A proper Data Governance what?!”
Mike T: “Exactly!”
Now that I’ve gotten your attention I’d like to introduce you to my new series on – yes, Data Governance. Over a series of articles I will introduce you to the concept of Data Governance and the common symptoms and problems that arise from lack thereof. I’ll also include an example where an agency of the US Government could have saved millions annually if a Data Governance framework had been in place. With help from products such as the QlikView Governance Dashboard and QlikView Expressor, I’ll also cover solutions and best practices that can help increase data confidence and reduce risk in the key organizational information used to make decisions.
It’s a Problem
Over the course of my career I have seen many organizations quickly adopt a BI solution and jump right into creating reports and dashboards for one or a few specific needs, while giving little thought to the rest of the BI solution and how others may benefit from previous work. So what happens? Another application is then developed with its own requirements, possibly using data and attributes similar to the first. When developed in an independent and ad hoc manner (as with many organization) business models, data definitions and semantics can be stored and defined inconsistently. This causes inaccuracies which only delays decisions as users search for the truth in data. As Enterprises strive to consolidate data and express a need for data repurposing, it becomes critical to introduce Data Governance standards. It’s been established by many analysts that a high percentage of BI projects fail to meet their objectives; siting a variety of issues including failure to implement a centralized data repository, inconsistent data models, little to no metadata management and lack of authority to institute and uphold best practices.
Mike T: So Reader, will you join me in my next post where I will address these challenges and solutions in greater detail? Hopefully, you will see QlikView is much more than just visualizing and analyzing data. It’s about driving decision-making using the right data.
Senior Product Marketing Manager
QlikView and QlikView Expressor
Follow me: @mtarallo