By Bobby Holmes
How frustrating is it when you call your cable provider and they fumble your information? I recently had this issue, and after 45 minutes of annoyance, the customer service rep finally told me that they’d call me back because they needed to get a manager involved to assist with the issue (which really meant they flat out couldn’t find my information).
I came to find out that the information was not available because my account was part of an acquisition they had made and they had not consolidated their data. I — like many, I would assume — was not a happy camper. This scenario is a classic example of not only a poor Customer Experience, but also an inadequate Worker Experience. That customer service rep was equally frustrated with the fact that they didn’t have all of my information readily available, enabling a seamless transaction. A technology architecture that consolidates all of your customer data into an easily retrievable repository — improving both the Worker and Customer Experience — takes quality data management.
The critical role of data content management
Master Data Management (MDM) is a discipline aimed at promoting data accuracy, consistency, and accountability with enterprise information. It is designed to remove duplicative entries and standardize varying data sets, therefore reducing the risk of error. If implemented correctly, the greatest benefit is increasing the speed and agility of your organization to enable data-driven decisions. There are 5 key operating model components that make up this ecosystem; without any one of these, your data quality will suffer.
Of course, data quality and capture are fundamental to your MDM strategy. If data is inaccurate (e.g., misspelled names, incorrect products, etc.) or stale (i.e., data that hasn’t been updated), then this is where your efforts should start. Data cleansing can be an exhaustive effort, but the time spent here will save your team future (or continued) headaches.
While MDM strategy and execution is often an IT-led task, it should not be misconstrued as an IT-only solution. The purpose of MDM is to allow for faster and better business decisions with quality data. To that end, business processes are instrumental in deriving the maximum value from your MDM program by connecting the business with IT and managing the end-to-end flow of data. How your employees access the data, enter new information, and execute a transaction should be thoughtfully mapped out to reduce processing time and increase quality — ultimately increasing their experience.
As most IT professionals know, MDM is not a one-and-done project. It is an ongoing effort to keep one of your organization’s most valued assets — data — in top shape. Having a team of people with specific roles in place for data quality, data governance, and data sourcing allows for a more focused effort — as opposed to having one person responsible for the entire program. Role definition, governance, and defined processes are critical to ensuring coordination and maximizing this team’s value. Depending on the size of your organization, these roles should report to the director (or similarly high-level role) of the MDM program.
Data governance is vital in ensuring data assets are formally managed across an organization. It creates accountability by arming data stewards with specific tasks regarding data cleansing, quality, and sourcing — among other things — to manage the full data life cycle in your organization. Key artifacts in a data governance model include cross-functional and key member roles and responsibilities, data definitions, compliance standards, processes, controls, and data security. Members of this group should have a defined cadence for ongoing communication regardless of where your organization is in your MDM journey. Most of the time, MDM governance is a subset of a larger enterprise data governance model. At its core, the data governance model keeps the other 4 operating model components coordinated and accountable.
While it’s impossible to have a MDM program without the technology component, technology should not be the driver of your efforts; it should enable them. The vendor marketplace for MDM technology is still maturing and very much in a growth stage. This area has garnered attention with the advancements of big data and companies embracing the Internet of Things (IoT).
Whether your company is beginning their MDM journey with mainly structured data or advancing its capabilities to include unstructured data, your MDM strategy should identify core requirements around data modeling capabilities, scalability, latency, and security. Be deliberate in your requirement definitions and knowledgeable about the current capability offerings. For example, if a subset of your MDM strategy is to enhance your analytics capabilities, make sure you identify technology that supports downstream business intelligence (BI) tools. While the market is evolving, there will likely not be a single technology that supports all of your business needs in this space. Prioritization is key to delivering the best solution for your organization’s needs.