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.
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.
However, this was no laughing matter in reality. The team diligently calculated the revenue and the opportunity lost due to dirty data and the cost of fixing it. They highlighted that it was labor-intensive to manage and clean the data, and factored in the cost of labor as well. They showcased that there were tens of systems actively managing customer data, and tens of millions of customer data, and Thomson Reuters lacked a 360 degree view of customers, thus hindering its ability to cross-sell or up-sell other products. But the tipping point was when the company began moving from a portfolio-based approach to running an integrated business, which affected how the company managed its customer data.
Idea of Enterprise Customer Master
“We partnered with the business, especially those departments that had the maximum issues due to dirty data, and it has been a fantastic IT-business partnership,” says Sriraman. The IT department pitched this as an authoritative source for customer data, and having all customer-related activities driven directly through an enterprise customer master (ECM) solution. The team highlighted how ECM would lead to effective data governance with ownership from business units across the enterprise using fewer tools to achieve this. “The business case was for business transformation of customer data and not for MDM. In fact, MDM was never mentioned at all,” he adds.
“The big lesson we learnt was to start small and get the data model right. This required limiting the fields to just three: Hierarchies, legal entities, and locations. By doing an aggressive inventory and having a measure of quality to quantify the data, we were able to establish a governance model which was not just on paper, but also had enough teeth to actually work. It is important to understand the nuances of managing the data and test the governance model. More importantly, to demonstrate incremental value every quarter, and not wait for 18 months to gain the RoI,” he concludes.
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Gopal Kishore is a principal correspondent for CIO India and ComputerWorld India. Send your feedback to firstname.lastname@example.org.