Data Governance: Did We Make The Right Choices?

Coming back from the SAS Industry Analyst Event left me with one big question - Are we taking into account the recommendations or insights provided through analysis and see if they actually produced positive or negative results?

It's a big question for data governance that I'm not hearing discussed around the table.  We often emphsize how data is supplied, but how it performs in it's consumed state is fogotten.  

When leading business intelligence and analytics teams I always pushed to create reports and analysis that ultimately incented action.  What you know should influence behavior and decisions, even if the influence was to say, "Don't change, keep up the good work!"  This should be a fundamental function of data govenance.  We need to care not only that the data is in the right form factor but also review what the data tells us/or how we interpret the data and did it make us better?

I've talked about the closed-loop from a master data management perspective - what you learn about customers will alter and enrich the customer master.  The connection to data governance is pretty clear in this case.  However, we shouldn't stop at raw data and master definitions.  Our attention needs to include the data business users receive and if it is trusted and accurate.  This goes back to the fact that how the business defines data is more than what exists in a database or application.  Data is a total, a percentage, an index.  This derived data is what the business expects to govern - and if derived data isn't supporting business objectives, that has to be incorporated into the data governance discussion.

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Top 4 Things to Keep In Mind When Evaluating MDM Vendors

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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Artificial Intelligence - What You Really Need to Know

It looks like the beginning of a new technology hype for artificial intelligence (AI). The media has started flooding the news with product announcements, acquisitions, and investments. The story is how AI is capturing the attention of tech firm and investor giants such as Google, Microsoft, IBM. Add to that the release of the movie ‘Her’, about a man falling for his virtual assistant modeled after Apple’s Siri (think they got the idea from Big Bang Theory when Raj falls in love with Siri), and you know we have begun the journey of geek-dom going mainstream and cool.  The buzz words are great too: cognitive computing, deep learning, AI2.

For those who started their careers in AI and left in disillusionment (Andrew Ng confessed to this, yet jumped back in) or data scientists today, the consensus is often that artificial intelligence is just a new fancy marketing term for good old predictive analytics.  They point to the reality of Apple’s Siri to listen and respond to requests as adequate but more often frustrating.  Or, IBM Watson’s win on Jeopardy as data loading and brute force programming.  Their perspective, real value is the pragmatic logic of the predictive analytics we have.

But, is this fair?  No.

First, let’s set aside what you heard about financial puts and takes. Don’t try to decipher the geek speak of what new AI is compared to old AI.  Let’s talk about what is on the horizon that will impact your business.

New AI breaks the current rule that machines must be better than humans: they must be smarter, faster analysts, or they manufacturing things better and cheaper. 

New AI says:

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The Seven Deadly Sins of Data Management Investment and Planning

When it comes to data investment, data management is still asking the wrong questions and positioning the wrong value.  The mantra of - It's About the Business - is still a hard lesson to learn.  It translates into what I see as the 7 Deadly Sins of Data Management.  Here are the are - not in any particular order - and an example:

  1. Hubris: "Business value? Yeah, I know.  Tell me something I don't know."  
  2. Blindness: "We do align to business needs.  See, we are building a customer master for a 360 degree view of the customer." 
  3. Vanity: "How can I optimize cost and efficiency to manage and develop data solutions?"
  4. Gluttony: "If I build this cool solutions the business is gonna love it!"
  5. Alien: "We need to develop an in-memory system to virtualize data and insight that materializes through business services with our application systems...[blah, blah, blah]"
  6. Begger: "If only we were able to implement a business glossary, all our consistency issues are solved!"
  7. Educator: "If only the business understood!  I need to better educate them!."
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Can Machines Be Our Friend? IBM Watson Thinks So.

IBM launched on January 9, 2014 its first business unit in 19 years to bring Watson, the machine that beat two Jeopardy champions in 2011, to the rest of us. IBM posits that Watson is the start of a third era in computing that started with manual tabulation, progressed to programmable, and now has become cognitive. Cognitive computing listens, learns, converses, and makes recommendations based on evidence.

IBM is placing big bets and big money, $1 billion, on transforming computer interaction from tabulation and programming to deep engagement.  If they succeed, our interaction with technology will truly be personal through interactions and natural conversations that are suggestive, supportive, and as Terry Jones of Kayak explained, "makes you feel good" about the experience.

There are still hurdles for IBM and organizations, such as expense, complexity, information access, coping with ambiguity and context, the supervision of learning, and the implications of suggestions that are  unrecognized today. To work, the ecosystem has to be open and communal. Investment is needed beyond the platform for applications and devices to deliver on Watson value.  IBM's commitment and leadership are in place. The question is if IBM and its partners can scale Watson to be something more than a complex custom solution to become a truly transformative approach to businesses and our way of life. 

Forrester believes that cognitive computing has the potential to address important problems that are unmet with today’s advanced analytics solutions. Though the road ahead is unmapped, IBM has now elevated its commitment to bring cognitive computing to life through this new business unit and the help of one third of its research organization, an ecosystem of partners, and pioneer companies willing to teach their private Watsons.

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Big Data Governance Protect And Serve Are Equals

I had the opportunity to speak and participate in a panel on data governance as it pertained to big data. My presentation was based on recently completed research sponsored by IBM to understand, what does data governance look like by firms embarking/executing on big data? The overarching theme was that data governance is about protect and serve. Manage security and privacy while delivering trusted data.

Yet, when you look at data governance and what it means to the data practice, not the technology, protect and serve is also a credo. In business terms it represents:

  • Protect the reputation and mitigate risk associated with inappropriate use or dirty data.
  • Serve information needs of the business to have information fast and stay agile to market conditions.
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Data Quality And Data Science Are Not Polar Opposites

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 Quality Blooms With Crowdflower

Sometimes getting the data quality right is just hard, if not impossible. Even after implementing data quality tools, acquiring third-party data feeds, and implementing data steward remediation processes, often the business is still not satisfied with the quality of the data. Data is still missing and considered old or irrelevant. For example: Insurance companies want access to construction data to improve catastrophe modeling. Food chains need to incorporate drop-off bays and instructions for outlets in shopping malls and plazas to get food supplies to the prep tables. Global companies need to validate address information in developing countries that have incomplete or fast-changing postal directories for logistics. What it takes to complete the data and improve it has now entered the realm of hands-on processes.

Crowdflower says they have the answer to the data challenges listed above. It has a model of combining a crowdsourcing model and data stewardship platform to manage the last mile in data quality. The crowd is a vast network of people around the globe that are notified of data quality tasks through a data stewardship platform. If they can help with the data quality need within the time period requester, the contributor accepts the task and get to work. The crowd can use all resources and channels available to them to complete tasks such as web searches, visits, and phone inquiries. Quality control is performed to validate crowdsourced data and improvements. If an organization has more data quality tasks, machine learning is applied to analyze and optimize crowd sourcing based on the scores and results of contributors.

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

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

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.