Bad data is a perennial problem for companies. But in a climate where the underlying business environment is shifting fast, bad data can trip companies into insolvency.
Data governance is supposed to weed out bad data and stop it from taking root within corporate data sets. Yet, the reality is that many companies are still struggling to govern their data.
So, why the gap in data governance? It begins when companies see it as an IT problem.
Companies need to own up to a stark reality: that it is a business issue, and not just an IT problem, said Praveen Kumar Chandrashekhar, senior vice president, and general manager for the Asia Pacific at ASG Technologies.
“The data owner has to be the business owner or someone at the top; it’s a corporate responsibility. IT might say we are data governed, but you step out of it, you find out that there are many aspects that are overlooked within the enterprise.”
The political game
Part of the problem is that there is very little justification for business teams to claim ownership over data governance. So, they leave it to the IT team to set it up.
“It’s more of the ability not to take responsibility. It’s not that [business owners] don’t want to do it. It is whether they can do it as part of their day job,” said Chandrashekhar.
Business owners and department/division heads are already under tremendous pressure to deliver on business metrics during the pandemic. Adding data governance on top of these can be a tall ask.
In places like Europe, the cost of bad data or inadequate data governance can be high. Regulations like GDPR state how much a company will have to pay if governance fails.
“The penalty is so huge and a detriment that it allows one to make a business case. You can justify adding an additional CDO under the specific business and not under IT,” said Chandrashekhar.
While countries in the Asia Pacific are moving in similar directions, it is not region-wide or uniform. As companies look to cut costs, spending on data governance will be the last thing on business owners’ minds. “Because data governance is a lot of money, and their question is, when am I going to get that revenue?”
Another problem is the approach. Many companies in APAC put a higher value on having humans taking control of governance. In areas like Australia and the Western world, governance is seen as a technology issue.
“In Asia, including Japan, the concept of data governance is very human-led, and not technology-led. They throw people at the problem to fix it,” observed Chandrashekhar.
COVID-19 is changing mindsets on the value of data governance.
As demand stalls, competition heightens, and the market demand evolves fast, companies see value in having the right data. Bad data can now directly impact business survival.
As a result, companies are now changing the conversation about data governance.
For example, Chandrashekhar noted how a CDO at an Australian insurance firm talked about reducing data deduplication within his company to save about 30% of costs. While the issue is about data governance, he noted that it can be a tough sell. But equating to actual savings gets senior management and business owners on the same page.
MDM vs. data inventory
Companies are also starting to realize the importance of understanding what data they have on the technology front.
“This whole concept of Master Data Management (MDM), which people keep selling, is very difficult to implement because of the variety of applications that an enterprise has. These applications actually store data in different forms that are built over a period of time. Moving all of that to a central MDM is a Herculean task. And that is a lot of money to be spent with no return that can be visible over the next maybe 36 months,” said Chandrashekhar.
ASG Technologies takes a different tact with data inventory. The company works closely with the business to build one before looking at data governance. The inventory approach asks questions such as the type of data, which applications process it, how it is moved from one point to another, and the different data value chain steps.
The data inventory builds a data landscape unique to each company. Then Chandrashekhar and his team examine the areas where a company will need to fix. “Unlike others who say, first let’s do data governance, we go in with the inventory approach first. That’s where we build greater trust before we build data governance.”
Post-pandemic role of governance
This trust is vital for good data. In fact, Chandrashekhar sees trusted data as inherently good data. “It all boils down to trust. If you can trust the data, then the data is good. If you cannot trust the data, the data is bad.”
It is with this binary definition of good data, which is driven by ASG Technologies’ Data Intelligence solution that Chandrashekhar is hoping to change the way companies do data governance “once and for all.”
He is also forecasting that the use of Data Intelligence to get a clear data landscape will play a more prominent role post-COVID-19. It is when companies will start to merge or acquire each other by taking advantage of low valuations. “Because the cost of enforcing data governance in an organization post-acquisition will be just too enormous.”
Image credit: iStockphoto/OSTILL