Analytics & Insights
Five Reasons Data Science Projects Fail
The difference between a transformational project and a resource drain lies not in the algorithms, but in the integration.Read More
Author: Robert Sunker
Stick with me – I want to make a quick analogy that might help you better appreciate data governance.
Let’s say you are training for a race. Day one, you may not be in the best physical condition. Maybe you haven’t even run, walked or crawled since your school years. But you know this will be good for you and you realize you just have to get started, you just have to start putting one foot in front of the other and hit the pavement.
The same is true with data governance in an information-driven business. Many if not most organizations aren’t in the best data governance condition – their muscles are weak and their stamina is poor. Data governance is the conditioning and training of the analytics and insights capabilities of an organization. Sure, you can go out and just do it without governance but the results will likely be poor, it won’t be easy and you may even pull something.
Before I offer a definition of data governance, I want to stress that data governance is first and foremost about on-going quality control. When your data is good, secure, and usable, it improves your quality control throughout the company. That’s how important data is.
Now with that said, I define data governance as: the alignment of people, process, and technology to maximize and protect the value of organizational data assets. Sadly, it’s an often-underappreciated discipline. Sad because when it’s done right, businesses can realize the benefit: more accurate, reliable and insightful business intelligence, reporting, analytics, and advanced analytics, such as data science.
A quick definition of data science is: a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract information and insights from data. Two categories of data science include:
Like running that race, the race time (results of predictions and prescriptions) is only going to be as good as the training and preparation (original data quality). In other words, data science doesn’t work and can’t be trusted without good data governance.
Unfortunately, many companies don’t treat data like the asset it is. Among many bad outcomes, this can lead to uncertainty in decision making and an inability to measure and drive business objectives. However, with proper data governance, your company can:
To achieve these benefits, leverage a data governance framework that includes: strategic alignment, directives, organization, measurement, change enablement, and technology. Where you start depends on the size and maturity of your business.
If this process seems burdensome, you’re not alone. For many, the steps to create a data governance framework appear heavy and academic; measuring the impact of preventing bad data is like determining the value of insurance you never use. Still, it’s worth the effort. And the goal is not perfection. You can train for a personal best time or a spot on the podium; in either case, you will improve your results, as long as you train.
How do you get started? Consider setting a 90-day plan to:
Making the time and investment to build the right processes, tools, integrations, and organization structures will help you better manage, share, and attain business value from your data. Just get started.
Robert Sunker is passionate about data and how it can drive value for organizations. He leads the development of offerings and solutions at RevGen that can enable our clients to navigate disruption and thrive.