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5 Simple Rules for Implementing a Master Data Program

Lessons and Insights from a Company in the Trenches

More and more organizations are looking to use data and technology at scale to find new ways to engage with customers, partners, vendors and suppliers to gain a competitive advantage. But the volume and velocity of data is overwhelming. Instead, what should be driving meaningful insights is creating unnecessary chaos. It’s the lack of an organizational data structure to help pull all these different disparate data sources together that makes the job so difficult. That’s why many companies are turning to master data to address the challenge.

Master data is the common business data shared across methodologies processes workflows and across entire ecosystems. Master data is foundational data, a standardized data structure that allows other data sources to connect. But employing a wide-scale master data strategy is no easy feat; it’s often a long, arduous process that requires considerable effort from the right resources. When done right, it can drive tremendous value across the enterprise.

NetApp’s Sr. Director of Enterprise Reporting & Data Management, Nick Triantos, recently shared how he and his team implemented an enterprise-wide master data strategy to help drive more effective business decisions. “Our goal was to think about how we can use a foundational master data strategy that could build trust and confidence across the organization as part of our daily processes, and have a master plan to move that forward.”

Check out Triantos’ 5 simple rules you should know to make the most of your master data program and avoid common pitfalls as you develop your strategy.

1.       Think Big, Start Small, Move Fast

It’s no wonder most master data projects fail. Triantos believes the reason most companies don’t succeed is that they try to solve for everything in one step. “Thinking of master data as a project and not a process or a lifestyle is where companies go wrong,” says Triantos. “What I've been preaching internally, and what I believe has been fundamental to our success, is employing a phased strategy where you enable a scalable data foundation that you iterate and drive improvement in steps, sometimes they may even need to be small steps. Ultimately, we had a master plan, we thought big, we knew what we wanted to do, but we knew we couldn't afford to make all those changes at once so we made little steps. We governed, we pushed, we moved, we stepped, we improved.”

Thinking of master data as a project and not a process or a lifestyle is where companies go wrong.
Nick Triantos, Sr. Director of Enterprise Reporting & Data Management, NetApp
 

This is the most important phase when starting a master data strategy because it sets the tone for the work throughout the organization. While you should have a grand vision and an idea for where you want to go, don’t try to accomplish it all in one step. Make small steps, keep improving and show value along the way.

2.      Speak the Same Language

When it comes to data, everyone within the organization needs to be speaking a universal language, yet doing so remains one of the biggest obstacles to master data implementation. Establishing common attributes for how you define data creates a common language between departments about simple but vital elements of your business records. Once the data is structured and standardized, it can harmonize and integrate better into your processes, methodologies, workflows—between your systems, regions, go-to-markets and even externally with other 3rd parties. “We started with a core hub-and-spoke model where we simply started to bring in key customer data and have a common definition for those customers across a couple of systems,” explains Triantos. “We started to grow that vision around our overall company master landscape, which included bringing our customers, partners, prospects, and vendors data into a common platform, joining it with a governed internal enrichment process where we managed that core enrichment in a central structure we could all drive from.”

While creating a centralized, core hierarchy structure doesn’t necessarily solve every problem, it does help drive consistency and quality that enables the team to work efficiently across the business. Triantos adds, “It’s a long journey to get perfect definitions, and I wouldn't aspire to create perfect definitions very aggressively. What we really did was start building this concept of a meta data library for the company on simple things like acronyms and terms and just business definitions all across the company.”

3.       Establish a Data Governance Program

Once you’ve aligned on how to universally structure your data across the enterprise, it’s time to bring that together under a governed structure to ensure quality data practices are being implemented and adhered to. “Governance really is vital as you start to work on a monitor and corrective action, because you can't monitor and fix something when you don't even know what that is,” says Triantos. “You can't drive policy and process if you’re not on the same page with every stakeholder about your policies and processes. You can't protect it if you don't know what needs protecting. You can't understand the flow if you don't know what it's used for. You can't drive consistency and accountability and accuracy. It really is an important part of the process and these are some of the core components that we use with our data governance effort.”

Data governance provides the foundation required to focus on business issues and activities relating to the quality and performance of master data.

4.         Secure Executive Sponsorship

For those brave few charged with employing and managing a large-scale master data strategy, it can often feel like you’re isolated from the rest of the business because you’re immersed in what others view as a very complex and overly technological undertaking. That’s why communication becomes a critical step in the master data process. The senior leadership will not always “get it”, so it’s important to connect what you’re doing to the overall business. “We actually established an enterprise architecture executive committee,” explains Triantos. “This body was created to help drive our core investments and align us to the ultimate business strategy we all shared. By clearly demonstrating to them how we manage our data, how we use our data, how we support the business and transform and grow the business, we're able to drive much better visibility and decision making.”

At the end of the day, it’s important to have the senior leadership team on your side. Just be sure to talk to them in terms they understand, not in tools and technologies. “Speak to them about those things and bring them along the journey,” says Triantos.

5.          Articulate the Value

The last step in the master data process may seem simple enough, but it’s important to understand what value means in terms of the work you’re doing. It’s certainly not just about the numbers, and while your executive team may be looking for concrete ROI to quantify the investment, it’s important to demonstrate the long-term value. “What I did was educate our leadership team more on the business-end benefits that the team can understand and see,” says Triantos. “For instance, I showed them how we can impact customer experience, how we can enable security, how we can enable go-to-market initiatives, how we can report and understand our customers, and then continually showed the improvement of the data using metrics, data, and real life examples.”

Every master data initiative is different, but they all demand working collaboratively across the enterprise to make it succeed. Together, your team will need to decide how to communicate the value. It may not always be in pure dollars, but it should show how it impacted the business. “We still have a way to go,” says Triantos. “We're on that continuous journey, but we've been able to see some of that value by improving customer and partner interactions though better quality data.” 

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