The Growing Importance of Master Data Management - A Spotlight Q&A with Software AG's Jignesh Shah

Originally published 21 November 2011

BeyeNETWORK Spotlights focus on news, events and products in the business intelligence ecosystem that are poised to have a significant impact on the industry as a whole; on the enterprises that rely on business intelligence, analytics, performance management, data warehousing and/or data governance products to understand and act on the vital information that can be gleaned from their data; or on the providers of these mission-critical products.

Presented as a Q&A-style article, these interviews with leading voices in the industry including software vendors, end users and independent consultants are conducted by the BeyeNETWORK and present the behind-the-scene view that you won’t read in press releases.


This BeyeNETWORK spotlight features Ron Powell's interview with Jignesh Shah, Vice President of Product Marketing at Software AG. Ron and Jignesh discuss Software AG’s unique approach to process-driven master data management (MDM) and hierarchy management, as well as the trends that are driving more and more organizations to realize the importance of MDM in their enterprises.

Jignesh, from our perspective, it seems organizations are finally realizing the importance of master data management for both operational and analytical data. What do you feel has driven this increased understanding and adoption of master data management?

Jignesh Shah: There are a couple of things that have gone on in the past two years that have put a spotlight on the quality of master data or, rather, the lack of quality around master data. First, I think, has been the focus on improving cross-functional processes – processes that cut across different departmental boundaries. As enterprises have engaged in a variety of different initiatives in things like process reengineering or BPM, they realize a lot of the improvement is not possible without fixing the underlying master data that often connects these functional silos or system silos.

In fact, if you look at some of the research, Gartner's clients reported this year that process improvement has become the number one driver for investment in MDM. That is a sign that a lot of the operational concerns and benefits are driving master data management. The other thing that seems to be happening is a focus on building shared or repeatable business capabilities. Here I'm specifically referring to things like SOA. The idea behind SOA is to build capabilities once that are then reused by different business processes, departments, functions, lines of business and so on. When you attempt to do something by SOA, once again you realize that building repeatable business capabilities is not possible when the underlying data and the underlying systems that support those business capabilities have inconsistent, error-prone, duplicate data. Very often we find that customers who want to scale up on SOA realize that they have to fix data quality problems before they can create SOA services and repeatable business capabilities. These are the two big trends.

There's a third trend on the horizon that I think will focus even further attention on MDM and that is the adoption of cloud computing, particularly the adoption of software-as-a-service applications. In this case, you are essentially renting a service utility from a third party and have essentially zero control over the data model of that service. Let's say you're a Salesforce.com user; you have absolutely no control over how data is represented in those systems. You are forced to find a way to reconcile the master data that resides in those systems with the master data that resides in your on-premises applications. For companies that want to make effective use of software-as-a-service applications, they will have to find a way to fix the master data problem and make sure there is reconciled and consistent uniformity between on-premises applications and cloud applications.

One of the premises that we've always held at the BeyeNETWORK is that if the data is inaccurate, it doesn't matter what you do from a business intelligence perspective or business analytics perspective. You must have a sound data foundation. When I visited Software AG, you showed me a unique approach for process-driven master data management. Could you describe that approach for our audience?

Jignesh Shah: The idea behind process-driven MDM is that measurable process improvement should directly and explicitly guide the scope and implementation of MDM. To understand what this means, let's contrast this approach with the traditional MDM approach.

Traditionally, MDM has been very data-focused so goals are set to address data management or quality challenges – fixing data in and across system silos. Traditionally, the ROI for MDM has been measured in terms of data quality metrics such as duplicate reduction or error reduction. The problem with this approach is that it often becomes a boil-the-ocean type of exercise. It takes too long and it's never clear how the business actually benefits from MDM. There's very little perceived business value; it's an IT thing.

In contrast, with process-driven MDM, we take a step back and start with the question: Who cares about improved master data and why? We arrive at the answer by first analyzing business processes that create, consume and modify master data. We measure how poor quality data is impacting these processes. Then, when we have this analysis and the backdrop of these measurements, we frame the entire MDM implementation, and this analysis then drives the data model for master data. It scopes the systems that are included or excluded. It guides data quality requirements. Data governance is established based on the stakeholders identified by this analysis. The end result of an MDM program that is process driven is a program that produces measureable business results. It's an MDM program that the business managers can appreciate and get involved in. That's the idea behind process-driven MDM.

Andy Hayler, a very respected analyst, is the CEO of The Information Difference, an analyst firm in the UK. He cited that there has been a dramatic upsurge in the awareness of how data governance should be an integrated part of a successful MDM program. The report that he published ranked Software AG as number one in MDM technology. Can you tell our readers more about that?

Jignesh Shah: This is the third year in a row that Information Difference has rated OneData as the number one MDM technology in its report. Information Difference has also ranked us as the vendor with the happiest customers; we're very proud of this achievement.

There are three things we have learned from our customers that guide our technology. First is that enterprises don't want to create MDM silos. Enterprises are realizing that using point MDM solutions or domain system MDM solutions is very, very expensive and a strategy that is not sustainable over the long term because it creates silos of MDM solutions. So the trend is toward multi-domain MDM solutions or platforms that are open, flexible, and cost effective. They represent solutions that are a long-term investment in the MDM infrastructure. This is where OneData particularly has been ahead of the curve. It’s been multi-domain from the ground up, which means it can manage more than one domain of master data in the same instance of the software. It can also support multiple users of that data, whether it’s operational or analytical, and can support different styles of master data management such as consolidated, centralized or hybrid.

The second thing we learned, which alludes to the data governance emphasis that Andy is highlighting in his report, is that enterprises are increasingly involving business users in master data management. This means the MDM solution or the MDM platform has to be business user friendly. It has to integrate data governance into the solution. Data governance cannot be an afterthought or something that's implemented outside of the MDM system, increasing the complexity and decreasing the user friendliness of the MDM solution. So we have taken data governance and we have embedded it directly inside OneData in a way that it is accessible to business users. Business users can come to OneData and be the data steward in charge of approving data governance policies in terms of resolving conflicts, for example. We also have technology that can embed OneData in applications that business users use on a day-to-day basis.

The third thing that we learned is that enterprises don't have 12 to 18 months to invest in setting up MDM before it starts delivering results. Our focus has been on rapid MDM implementations so that enterprises can get MDM projects out there, get feedback, learn, go back and do more projects. We have a drop-in data model that helps you get started without any coding; it's all configuration. Most OneData customers roll out their first MDM projects in about three months.

Taken together, these three things really have differentiated us over the years and have helped us be at the top in terms of the vendors that Information Difference rates.

From an MDM perspective, you almost have to establish your data governance policies before you even begin implementing a major MDM effort.

Jignesh Shah: Absolutely. You have to look at data governance as a key part of the analysis that goes into understanding how data is created, consumed and modified before you even select an approach to implementing master data management.

One of Software AG's unique differentiators is what you call hierarchy management. Could you tell us about its benefits and how it applies to business intelligence?

Jignesh Shah: Hierarchy management is related to the dimensions used in business intelligence. The dimensions are a key component of reporting and analytics. Traditionally, these dimensions and the relationship of data in the dimensions have not been managed well. Generally, the governance and the change management around these dimensions is quite weak. They may be kept in spreadsheets, built into the application, or built directly into the data warehouse or the data mart – which results in inaccuracies or inconsistencies across the dimensions. It definitely makes it very difficult for business users to access these dimensions and work with them. You almost always need IT intervention. The idea behind hierarchy management is to apply good data governance principles to these critical elements of the BI infrastructure.

What's unique about Software AG's hierarchy management solution is the fact that you can start with the hierarchy data model you already have in place. If you have a data model reporting solution and you have a set of hierarchies already in place, you can simply take that data model and drop it into OneData and you can configure the needed governance, approval, workflows, integration, etc., around it. OneData supports different kinds of hierarchies such as leveled, ragged, or recursive hierarchies. We can manage the different ways in which you want to slice and dice the data without creating data integrity issues. We support some of the advanced needs that your BI infrastructure may have – for example, versioning of hierarchies. This is very difficult to do using raw databases, spreadsheets, etc., but we can do it very effectively inside the OneData data management system. We can version your hierarchies in a variety of different ways like point-in-time views, number versions, snapshots and so on. We give you a lot of flexibility in how you evolve your hierarchies and deploy them over a period of time.

One feature of your Web Methods OneData product is the ability to handle multiple domains in other words, being able to use the same operational capabilities to incrementally master multiple domains of data. How does it affect an organization's business intelligence efforts?

Jignesh Shah: From a business intelligence point of view, to deliver a complete picture the dimensions inherently will be cross-domain. For example, let's say you're analyzing orders. It is very likely that you want to analyze orders not only by customers, but also by product and region. BI requirements inherently tend to be multi-domain, and you want good, consistent master data about these  domains. Managing these different domains requires a true multi-domain MDM solution. Deploying point solutions can get very expensive very fast.

With Software AG's OneData, not only can you manage multiple domains, but also you can do it in a single solution that has been integrated with your data quality infrastructure and your data integration infrastructure. You can add dimensions and domains incrementally by building it on top of the same data model. What this means to your BI efforts is that your BI efforts are no longer bottlenecked by how quickly you can get infrastructure in place to provide high-quality data. You can use the same platform for different master data; the platform grows as the scope of your BI effort grows. You can reuse the tools, the integration, and the skill sets that you have. Equally important is that you now have created the foundation for the day your organization is ready to improve data quality upstream. When you're ready, you can start using OneData to feed master data upstream into operational systems. That's the benefit of having something like OneData manage the dimensions of the master data needed by your BI systems.

It seems that the OneData product now provides the complete infrastructure for MDM across an entire enterprise.

Jignesh Shah: Indeed. It is designed to be a platform that you invest in once and that you set up once. It can incrementally handle the requirements to master all of your enterprise data.

You are the author of a very innovative book called SOA Adoption for Dummies. Some of the key chapters in the book are “Creating an Agile Business,” “Realizing the SOA Architecture Blueprint,” “Service Infrastructure,” “Governance Infrastructure” and “Organizational Agility.” I usually finish my spotlights with a prediction question about market trends, but instead could you tell us why you wrote the book and if you might write any other books in the future?

Jignesh Shah: When we wrote the SOA book, there were plenty of books out there that dealt with the technology aspects of SOA. But the hardest part about SOA is not the technology; it's the organizational transformation an enterprise has to go through to adopt a new way of building business capabilities using IT. Where do you start, who do you get involved, how do you get buy-in, what are the pitfalls and so on. We focused our book almost exclusively on the people and process aspects of SOA. Hence, we gave it the title SOA Adoption for Dummies. It has proven to be very popular with more than 50,000 copies distributed in five languages.

There are interesting parallels with MDM, in that a lot of practitioners understand the technology behind MDM but are struggling with getting started and making it effective. My next book is about how to adopt MDM and roll out an MDM program. The book is called Process-Driven Master Data Management for Dummies, and I'm writing with two MDM rock stars from Software AG. Both have years of experience helping organizations with MDM, and the book is for people who want to learn the business value of MDM and how to achieve it. In this book, we will discuss the traditional approach of data-focused MDM and its limitations. We will then show a better way to master your enterprise master data, the process-driven approach. We will dive deeper into the concepts that I outlined earlier and show you how to use MDM to improve your business processes. It’s coming out in late October, early November. [Editor’s Note: Process-Driven Master Data Management for Dummies is now available here.]

As we continue to see the merging of the operational world with the BI world, process-driven master data management is going to be a key component of that. Thank you for taking the time to speak with me today.

SOURCE: The Growing Importance of Master Data Management - A Spotlight Q&A with Software AG's Jignesh Shah

  • Ron PowellRon Powell
    Ron is an independent analyst, consultant and editorial expert with extensive knowledge and experience in business intelligence, big data, analytics and data warehousing. Currently president of Powell Interactive Media, which specializes in consulting and podcast services, he is also Executive Producer of The World Transformed Fast Forward series. In 2004, Ron founded the BeyeNETWORK, which was acquired by Tech Target in 2010.  Prior to the founding of the BeyeNETWORK, Ron was cofounder, publisher and editorial director of DM Review (now Information Management). He maintains an expert channel and blog on the BeyeNETWORK and may be contacted by email at rpowell@powellinteractivemedia.com. 

    More articles and Ron's blog can be found in his BeyeNETWORK expert channel.

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