Customer Information Data Management Systems | Pitney Bowes

Master Data Management: Moving from the Grid to the Graph

MDM platforms must react to needs across the business, rather than the narrow demands of a single part of the company

"It’s the first step to truly holistic customer insight" Aaron Wallace
Product Manager, Customer Information Management
Pitney Bowes

In the age of Big Data, businesses are collecting mountains of information about their customers that, when organized effectively, offers the potential to deliver important and actionable buyer insights. To do this, many organizations store information in data warehouses, or more recently, data lakes in big data environment. However, with customer databases collecting hefty streams of data on a daily basis, wading through and determining what information is useful or relevant for a specific business division or decision can become a very daunting task.

To tackle this challenge, businesses will often deploy Master Data Management (MDM) technologies that aggregate the wealth of incoming transactional data per customer. The goal is to offer a complete perspective of each customer’s relationship with the business, from the first transaction to the most recent. In the past, these MDM solutions were built on traditional relational databases that were able to provide easy views of a defined subset of the information through a trusted data hub. 

However, MDM platforms based on relational databases limit what businesses can do – and how quickly they can do it – with the customer data they collect. With more customer information available today than ever before, there are now greater opportunities to leverage this information across the organization. These opportunities require agile MDM platforms that can react to needs across the business, rather than the narrow demands of a single part of the company. 

The answer is to adopt a new approach and develop a MDM strategy around Graph databases, instead of relational databases. 

Compared to a relational database, a graph database offers a more flexible starting point from which to build an MDM program. Graph databases inherently offer individual departments within a business the ability to pull answers from the full range of information available on each customer, as opposed to solely the data sets that are deemed relevant to that specific business unit. 

Additionally, by using a graph database-based MDM platform from the outset, businesses can add and remove new data sources quickly, identify new connections in data and explore connections that previously would not have been obvious. It’s the first step to truly holistic customer insight. Equally, those who have already implemented a number of MDM solutions on more traditional relational databases, can benefit by adding Graph to connect them – effectively creating a “hub of hubs”. 

New MDM strategies need to have the entire scope of the business in mind when looking at the customer. Customer data should provide insight into interactions at every level of the business and offer guidance into how those interactions can be improved. All of this needs to be offered from a central viewpoint. 

That’s a limitation of relational databases, which place a heavy restriction on the amount of time needed to reframe available information in a unique context relative to the questions being asked. Graph databases, however, offer a fluid setting for the analysis of information that allows for the realignment of priorities at a moment’s notice. 

This is just one factor of graph-based MDM solutions that we discussed in our recent webinar, which offered tips on building an accurate view of the customer journey at your company. Along with Gene Leganza, a leading analyst in the MDM space at Forrester Research, Pitney Bowes experts offered a deep dive into how a streamlined approach to MDM can deliver big dividends for businesses of all kinds. Replay the webinar here