MDM tools today don't look like your father's MDM. No longer an integration hub between applications and DBMSs, today's tools are transitioning or have reinvented MDM to handle the context missing from system traditional implementations. Visualizations, graph repositories, big data and cloud scale, along with application like interfaces for nontechnical users, mean MDM and master data gets personal with stakeholders.
Semantics and insight are not an outcome of MDM but an integrated part of the engine and hub. Three MDM evolutions stand out:
Business-defined views of data: For graph-based vendors such as Reltio and Pitney Bowes, master domains are shaped by business use cases. For example, customer master can be defined beyond the bounds of a household, identity, and account. Customer behavioral characteristics can be the starting points for taxonomies and hierarchies. Integration of master domains is based on physical, logical, linkage, and semantic schemas for a more seamless navigation and querying of master data to align with the explosion of data views created by analytics, applications, and microservices.
Last week, I participated in a roundtable during a conference in Paris organized by the French branch of DAMA, the data management international organization. During the question/answer part of the conference, it became clear that most of the audience was confusing data management with data governance (DG). This is a challenge my Forrester colleague Michele Goetz identified early in the DG tooling space. Because data quality and master data management embed governance features, many view them as data governance tooling. But the reality is that they remain data management tooling — their goal is to improve data quality by executing rules. This tooling confusion is only a consequence of how much the word governance is misused and misunderstood, and that leads to struggling data governance efforts.
So what is “governance”? Governance is the collaboration, organization, and metrics facilitating a decision path between at least two conflicting objectives. Governance is finding the acceptable balance between the interests of two parties. For example, IT governance is needed when you would like to support all possible business projects but you have limited budget, skills, or resources available. Governance is needed when objectives are different for different stakeholders, and the outcome of governance is that they do not get the same priority. If everyone has the same objective, then this is data management.
You can't bring up semantics without someone inserting an apology for the geekiness of the discussion. If you're a data person like me, geek away! But for everyone else, it's a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.
It begins with a seemingly innocent set of questions:
"Is there a better way to master my data?"
"Is there a better way to understand the data I have?"
"Is there a better way to bring data and content together?"
"Is there a better way to personalize data and insight to be relevant?"
Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They're born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester's Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized.
Early this year a host of inquires were coming in about data quality challenges in CRM systems. This led to a number of joint inquires between myself and CRM expert Kate Legget, VP and Principal Analyst in our application development and delivery team. Seems that the expectations that CRM systems could provide a single trusted view of the customer was starting to hit a reality check. There is more to collecting customer data and activities, you need validation, cleansing, standardization, consolidation, enrichment and hierarchies. CRM applications only get you so far, even with more and more functionality being added to reduce duplicate records and enforce classifications and groups. So, what should companies do?
One of the biggest stumbling blocks is getting business resources to govern data. We've all heard it:
"I don't have time for this."
"Do you really need a full time person?"
"That really isn't my job."
"Isn't that an IT thing?"
"Can we just get a tool or hire a service company to fix the data?"
Let's face it, resources are the data governance killer even in the face of organizations trying to take on enterprise lead data governance efforts.
What we need to do is rethink the data governance bottlenecks and start with the guiding principle that data can only be governed when you have the right culture throughout the organization. The point being, you need accountability with those that actually know something about the data, how it is used, and who feels the most pain. That's not IT, that's not the data steward. It's the customer care representative, the sales executive, the claims processor, the assessor, the CFO, and we can go on. Not really the people you would normally include regularly in your data governance program. Heck, they are busy!
But, the path to sustainable effective data governance is data citizenship - where everyone is a data steward. So, we have to strike the right balance between automation, manual governance, and scale. This is even more important as out data and system ecosystems are exploding in size, sophistication, and speed. In the world of MDM and data quality vendors are looking specifically at how to get around these challenges. There are five (5) areas of innovation:
Spending time at the MDM/DG Summit in NYC this week demonstrated the wide spectrum of MDM implementations and stories out in the market. It certainly coincides with our upcoming MDM inquriry analysis where:
An IT mindset has dominated the way organizations view and manage their data. Even as issues of quality and consistency raise their ugly head, the solution has often been to turn to the tool and approach data governance in a project oriented manner. Sustainability has been a challenge, relegated often to IT managing and updating data management tools (MDM, data quality, metadata management, information lifecycle management, and security). Forrester research has shown that less than 15% of organizations have business lead data governance that is linked to business initiatives, objectives and outcomes. But, this is changing. More and more organizations are looking toward data governance as a strategic enterprise competence as they adopt a data driven culture.
This shift from project to strategic program requires more than basic workflow, collaboration, and data profiling capabilities to institutionalize data governance policies and rules. The conversation can't start with data management technology (MDM, data quality, information lifecycle management, security, and metadata management) that will apply the policies and rules. It has to begin with what is the organization trying to achieve with their data; this is a strategy discussion and process. The implication - governing data requires a rethink of your operating model. New roles, responsibilities, and processes emerge.
Clients now have a report that helps them make more informed choices about selecting a PIM solutions. PIM is not always a well understood master data solution option for Enterprise Architects. Questions arise about, do I need PIM or MDM or do both? Aren't PIM and Product MDM the same? How does this fit in my architecture? This report takes away the confusion, answers these questions. It gives insight into how vendors satisfy PIM demands, differentiate from MDM and operate in hybrid scenarios.
The first Forrester Wave collaboration across the Business Technology and Marketing and Strategy groups. In the age of the customer, tighter collaboration between business decision makers and technology management professionals is critical. This wave addresses both perspectives providing the business requirements for marketing and product professionals while also addressing the technical questions that are important when selecting tools. Yes, business and technology management can work together, be on the same page, and produce great results!
The last Forrester Wave for MDM was released in 2008 and focused on the Customer Hub. Well, things have certainly changed since then. Organizations need enterprise scale to break down data silos. Data Governance is quickly becoming part of an organization's operating model. And, don't forget, the big elephant in the room, Big Data.
From 2008 to now there have been multiple analyst firm evaluations of MDM vendors. Vendors come, go or are acquired. But, the leaders are almost always the same. We also see inquiries and implementations tracking to the leaders. Our market overview report helped to identify the distinct segments of MDM vendors and found that MDM leaders were going big, leveraging a strategic perspective of data management, a suite of products, and pushing to support and create modern data management environments. What needed to be addressed, how do you make a decision between these vendors?
The Forrester Wave for the Multi-Platform MDM market segment gets to the heart of this question by pushing top vendors to differentiate amongst themselves and evaluating them at the highest levels of MDM strategy. There were things we learned that surprised us as well as where the line was drawn between marketing messaging and positioning and real capabilities. This was done by positioning the Wave process the way our clients would evaluate vendors, rigorously questioning and fact checking responses and demos.
Big data gurus have said that data quality isn’t important for big data. Good enough is good enough. However, business stakeholders still complain about poor data quality. In fact, when Forrester surveyed customer intelligence professionals, the ability to integrate data and manage data quality are the top two factors holding customer intelligence back.
So, do big data gurus have it wrong? Sort of . . .
I had the chance to attend and present at a marketing event put on by MITX last week in Boston that focused on data science for marketing and customer experience. I recommend all data and big data professionals do this. Here is why. How marketers and agencies talk about big data and data science is different than how IT talks about it. This isn’t just a language barrier, it’s a philosophy barrier. Let’s look at this closer:
Data is totals. When IT talks about data, it’s talking of the physical elements stored in systems. When marketing talks about data, it’s referring to the totals and calculation outputs from analysis.
Quality is completeness. At the MITX event, Panera Bread was asked, how do they understand customers that pay cash? This lack of data didn’t hinder analysis. Panera looked at customers in their loyalty program and promotions that paid cash to make assumptions about this segment and their behavior. Analytics was the data quality tool that completed the customer picture.
Data rules are algorithms. When rules are applied to data, these are more aligned to segmentation and status that would be input into personalized customer interaction. Data rules are not about transformation to marketers.