Are Data Governance Tools Ready for Data Governance?

Michele Goetz

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. 

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PIM: MDM on Business Terms

Michele Goetz

Along with Peter Sheldon in our eBusiness and Channel Strategy role, we just released the Forrester's Wave on Product Information Management.  I'm really exited about this report for two reasons:

  1. 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.
  2. 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!
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Top 4 Things to Keep In Mind When Evaluating MDM Vendors

Michele Goetz

The Forrester Wave for Multi-Platform MDM is out!

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. 

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Data Quality And Data Science Are Not Polar Opposites

Michele Goetz

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.
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Data Science And "Closed-Loop" Analytics Changes Master Data Strategy

Michele Goetz
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:
  • Context is more important than source.
  • You need to know the customer.
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Make Story-Telling The Goal Of Customer MDM

Michele Goetz

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.

MDM Is About Golden Profiles

Michele Goetz

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.  

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What Master Data Management Metrics Matter?

Michele Goetz

I recently had a client ask about MDM measurement for their customer master.  In many cases, the discussions I have about measurement is how to show that MDM has "solved world hunger" for the organization.  In fact, a lot of the research and content out there focused on just that.  Great to create a business case for investment.  Not so good in helping with the daily management of master data and data governance.  This client question is more practical, touching upon:

  • what about the data do you measure?
  • how do you calculate?
  • how frequently do you report and show trends?
  • how do you link the calculation to something the business understands?
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The Great Divide: MDM and Data Quality Solution Selection

Michele Goetz

I just came back from a Product Information Management (PIM) event this week had had a lot of discussions about how to evaluate vendors and their solutions.  I also get a lot of inquiries on vendor selection and while a lot of the questions center around the functionality itself, how to evaluate is also a key point of discussion.  What peaked my interest on this subject is that IT and the Business have very different objectives in selecting a solution for MDM, PIM, and data quality.  In fact, it can often get contentious when IT and the Business don't agree on the best solution. 

General steps to purchase a solution seem pretty consistent: create a short list based on the Forrester Wave and research, conduct an RFI, narrow down to 2-3 vendors for an RFP, make a decision.  But, the devil seems to be in the details.  

  • Is a proof of concept required?
  • How do you make a decision when vendors solutions appear the same? Are they really the same?
  • How do you put pricing into context? Is lowest really better?
  • What is required to know before engaging with vendors to identify fit and differentiation? 
  • When does meeting business objectives win out over fit in IT skills and platform consistency?
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Master Data Management Does Not Equal The Single Source Of Truth

Michele Goetz

The number one reason I hear from IT organizations for why they want to embark on MDM is for consolidation or integration of systems. Then, the first question I get, how do they get buy-in from the business to pay for it?

My first reaction is to cringe because the implication is that MDM is a data integration tool and the value is the matching capabilities. While matching is a significant capability, MDM is not about creating a golden record or a single source of truth.

My next reaction is that IT missed the point that the business wants data to support a system of engagement. The value of MDM is to be able to model and render a domain to fit a system of engagement. Until you understand and align to that, your MDM effort will not support the business and you won’t get the funding. If you somehow do get the funding, you won’t be able to appropriately select the MDM tool that is right for the business need, thus wasting time, money, and resources.

Here is why I am not a fan of the “single source of truth” mantra. A person is not one-dimensional; they can be a parent, a friend, or a colleague, and each has different motivations and requirements depending on the environment. A product is as much about the physical aspect as it is about the pricing, message, and sales channel it is sold through. Or, it is also faceted by the fact that it is put together from various products and parts from partners. In no way is a master entity unique or has a consistency depending on what is important about the entity in a given situation. What MDM provides are definitions and instructions on the right data to use in the right engagement. Context is a key value of MDM.

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