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.
I had a conversation recently with Brian Lent, founder, chairman, and CTO of Medio. If you don’t know Brian, he has worked with companies such as Google and Amazon to build and hone their algorithms and is currently taking predictive analytics to mobile engagement. The perspective he brings as a data scientist not only has ramifications for big data analytics, but drastically shifts the paradigm for how we architect our master data and ensure quality.
We discussed big data analytics in the context of behavior and engagement. Think shopping carts and search. At the core, analytics is about the “closed loop.” It is, as Brian says, a rinse and repeat cycle. You gain insight for relevant engagement with a customer, you engage, then you take the results of that engagement and put them back into the analysis.
Sounds simple, but think about what that means for data management. Brian provided two principles:
It is easy to get caught up in the source and target paradigm when implementing master data management. The logical model looms large to identify where master data resides for linkage and makes the project -- well -- logical.
If this is the first step in your customer MDM endeavor and creating a master data definition based on identifying relevant data elements, STOP!
The first step is to articulate the story that customer MDM will support. This is the customer MDM blueprint.
For example, if the driving business strategy is to create a winning customer experience, customer MDM puts the customer definition at the center of what the customer experience looks like. The customer experience is the story. You need to understand and have data points for elements such as preferences, sentiment, lifestyle, and friends/relationships. These elements may be available within your CRM system, in social networks, with partners, and third-party data providers. The elements may be discrete or derived from analytics. If you only look for name, address, phone, and email, there is nothing about this definition that helps determine how you place that contact into context of engagement.
Ultimately, isn’t that what the business is asking for when they want the promised 360-degree view of the customer? Demands for complete, relevant, and timely are not grounded in the databases, data dictionaries, and integration/transformation processes of your warehouses and applications; they are grounded in the story.
So, don’t start with the data. Start with the story you want to tell.
There is a shift underway with master data management (MDM) that can't be ignored. It is no longer good enough to master domains in a silo and think of MDM as an integration tool. First-generation implementations have provided success to companies seeking to manage duplication, establishing a master definition, and consolidating data into a data warehouse. All good things. However, as organizations embrace federated environments and put big data architectures into wider use, these built-for-purpose MDM implementations are too narrowly focused and at times as rigid as the traditional data management platforms they support.
Yet, it doesn't have to be that way. By nature, MDM is meant to provide flexibility and elasticity to managing both single and multiple master domains. First, MDM has to be redefined from a data integration tool to a data modeling tool. Then, MDM is better aligned to business patterns and information needs, as it is designed by business context.
Enter The Golden Profile
When the business wants to put master data to use it is about how to have a view of a domain. The business doesn't think in terms of records, it thinks about using the data to improve customer relationships, grow the business, improve processes, or any host of other business tasks and objectives. A golden profile fits this need by providing the definition and framework that flexes to deliver master data based on context. It can do so because it is driven by data relationships.