MDM Lite: The Start of Linq Analytics


In 2018, my analytics team and I were fighting an all-too-common battle: our marketing, product, sales, and finance teams wanted cleaner customer account data. They wanted it yesterday, yet nobody – ourselves included – was prepared to undertake a $2+ million investment over an 18-24 month period only to have semi-usable data that injected additional rigidity into our go-to-market process.

As many other organizations have faced or are facing similar challenges, I wanted to share our own decision-making process, what led us to choose our approach, and how Linq Analytics came to be – and how a “Master Data Management Lite” approach to challenges in customer data integration can offer reliable customer mapping in a faster, lower-touch, and lower-cost manner than traditional MDM solutions.

This represents the first of three posts on the origins of Linq Analytics and our approach to “MDM Lite.” In future posts, our team will expand on several elements of our broad Master Data Management experiences, including the specific challenges that inorganic growth creates. We welcome questions and feedback, and I invite you to reach out to my team and I to discuss these.

Our Challenge

As mentioned, our central analytics team was seeking to provide our internal clients with improved customer data mapping. As a $1+ billion software business that had grown in large part via mergers and acquisitions, we had to maintain multiple Customer Relationship Management (CRM) instances (multiple Salesforce orgs and Pipedrive instances) and an Enterprise Resource Planning (ERP) instance (Netsuite) in which a single customer was often represented by multiple accounts within and across systems. In some cases, some of our largest enterprise customers had hundreds of unique accounts and were present in at least three CRM instances.

Our business was built on a high velocity sales model, so this account proliferation was inevitable. We were seeking to augment this model with a more customer-centric approach to marketing and sales in order to drive cross-sell and up-sell with our massive customer base. Implementing this augmented model began to create friction with our customers and internal teams. Multiple touch points within a given customer were driving confusion for our customers (Who is this new person reaching out? And why are they trying to sell me something I already have?), conflict among our sales reps (Who owns this account? Who owns this opportunity?), and inefficiency for our marketing teams (Why is customer X in this audience for this campaign when one of the underlying accounts already owns the product for the campaign?).

So, as our team worked with our CIO and our business applications teams to solve these issues and provide data cleansing, we sought to accomplish the following:

  • Improve our understanding of product ownership and white space across our global customer base
  • Mitigate sales team overlap in account coverage (and therefore mitigate expensive rep conflict)
  • Reduce manual data manipulation and long term maintenance for our analytics, data warehouse, and sales ops teams

In our next post, we will discuss the alternatives we identified and the considerations for each of them.