Maximize Your Return on Big Data - How to Sell Master Data Management to Your CXO
Thomson Reuters, with its 60,000 employees across more than 100 countries and more than 400,000 financial business users, is the world's leading source of intelligent information for businesses and professionals. However, being the leader in every region it operates comes with its own set of challenges. For Thomson Reuters, that challenge was utilizing its biggest asset – data. With one million market-moving news stories a year and 20 million Intellectual Property and Science users, data was fast becoming more of an issue than an asset.
“We acquired over 30 companies in 2012 and about 40 companies in 2011. That’s about 70 companies in 100 weeks. So the amount of data that was coming in was enormous,” says Nallan Sriraman, Principal Architect and Head of India Operations–MIS, Thomson Reuters. Thomson Reuters engaged several SIs to look at the data, and each one of them came back with huge presentations which spoke about the problems faced with data, and why it needs to be sorted out. “All of these presentations boiled down to one slide – which said we need a Master Data Management (MDM) solution. However, there was no recommendation on how long would implementing an MDM solution take, how much would it cost, and not even how to implement this solution,” observes Sriraman.
“And then, just like in any kind of technological implementation, there was the biggest hurdle of all—convincing the management and getting buy-in for MDM,” continues Sriraman. “We soon realized that the best way to sell MDM was to not sell MDM directly. This is simply because MDM seems to have a bad connotation. The perception is that MDM takes a long time to implement, and is sometimes, too expensive to justify the investment. And frankly, the business doesn’t know about MDM, and what’s more, it doesn’t care what master data management is. All they want is clean data,” he adds.
The first task for the team was to identify the pain points and the domain that had the dirtiest data—whether it was customer, product or vendor. Another important function was to identify how many systems the dirty data manifests and infects. “The system with the maximum interaction with the customer was an obvious starting point. However, cleaning the data at this source was a losing battle, because as soon as it was cleaned, an acquisition or merger would dirty the data all over again. We called this the sewer line,” he laughs.