The Theory of Data Trust Relativity

Since the dawn of big data data quality and data governance professionals are yelling on rooftops about the impact of dirty data.  Data scientists are equally yelling back that good enough data is the new reality.  Data trust at has turned relative.

Consider these data points from recent Forrester Business Technographics Survey on Data and Analytics and our Online Global Survey on Data Quality and Trust:

  • Nearly 9 out of 10 data professionals rate data quality as a very important or important aspect of information governance
  • 43% of business and technology management professionals are somewhat confident in their data, and 25% are concerned
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When CRM Fails On Customer Information

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?

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Data Before Technology: IBM Watson's Vision

I sat down with Steve Cowley, General Manager for IBM Watson, on Tuesday at IBM Insights to talk about Watson successes, challenges since the January launch, and what is in store.  While the potential has always intrigued me, the initial use cases and message gave me more than a bit of pause: the daunting task to develop and train the corpus, the narrowness of the use cases, what would this actually cost?  Jump ahead nine months and the IBM Watson world is in a very different place.

IBM is clearly in its market building phase.  It is as much about what IBM Watson is and how IBM overall is repositioning itself as it is about changing the business model for selling technology.  However, it is easy to get negative very fast on this strategy as seen with the tremors on Wall Street as IBM's stock has gone from a 52 week high of $199 to $164 at close on Friday 10/31, much of that happening in the past month since earnings release. Wall Street may not like company uncertainty during transitional periods, but enterprise architects care about what will make their organizations successful, make development and management of technology easier, and making sure costs don't sky rocket when new bright shiny objects come in. And, that is where IBM is headed with an eye toward changing the game.

IBM Watson delivers on information over technology.

Steve surprised me with this statement, "[With] traditional programmed systems, the system is at its best when it is deployed, because it is closest to the business need it was written for. Over time these systems get further and further away from the shifting business need and so either they fall in effectiveness, or require a great deal or maintenance." Steve pointed out that data is what is changing the game.*

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Creating the Data Governance Killer App

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:

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Disruption Coming For MDM - The Hub of Context

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:

  • Big data is influencing MDM strategies and plans
  • Moving from MDM silos to enterprise MDM hubs
  • Linking MDM to business outcomes and initiatives
  • Cloud, cloud, cloud
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Quality, Trusted, Fit for Purpose Data?

Often lagging in priorities when it comes to data strategy, it appears that data quality is coming back in favor. As organizations expand beyond data exploration and discovery to putting real action in place organization wide based on these insights, the risk of getting the answer wrong or having dirty data is higher.  

But, this may be anecdotal supposition, even in light of the wide conversations I've had with clients.   What we do know quantitatively is:

1) Data quality is the most important topic for information governance according to our recent Business Technographics research for data and analytics.  In fact,

2) We see an uptick in data quality inquiries from last year.  

3) Vendors are introducing data preparation tools that infuse data quality and governance into analytic and BI processes

Anecdotal evidence and quantiative evidence leads me to the thought that there is a bigger shift happening in how we think about data quality, how we act upon it, and what doing so does for our buisnesses.  When things are a-changing it always make my brain itch to get more data, more stories, and more evidence.  And, while I'm curious, I'm assuming you are too. It is great to see that something in influencing change - and we want to know what that is in order to determine if we too need to change.  However, what is more important is what are organizations doing and which are standing out in terms of success and improved ways of thinking and execution?  In the end, do we need to write a new playbook* for data quality?  Has the bar been reset and we need to rebenchmark?

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Cognitive Computing Forum: 7 Things You Need To Know

Day one of the first Cognitive Computing Forum in San Jose, hosted by Dataversity, gave a great perspective on the state of cognitive computing; promising, but early.  I am here this week with my research director Leslie Owens and analyst colleague Diego LoGudice.  Gathering research for a series of reports for our cognitive engagement coverage, we were able to debrief tonight on what we heard and the questions these insights raise.  Here are some key take-aways:

1)  Big data mind shift to explore and accept failure is a heightened principle.  Chris Welty, formerly at IBM and a key developer of Watson and it's Jeoapardy winning solution, preached restraint.  Analytic pursuit of perfect answers delivers no business value.  Keep your eye on the prize and move the needle on what matters, even if your batting average is only .300 (30%).  The objective is a holistic pursuit of optimization.

2)  The algorithms aren't new, the platform capabilities and greater access to data allow us to realize cognitive for production uses.  Every speaker from academic, vendor, and expert was in agreement that the algorithms created decades ago are the same.  Hardware and the volume of available data have made neural networks and other machine learning algorithms both possible and more effective.  

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Are Data Governance Tools Ready for Data Governance?

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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Can You Afford To Ignore The Artificial Intelligence Wave?

Recent news of a a computer program that passed the Turing Test is a great achievement for artificial intelligence (AI).  Pulling down the barrier between human and machine has been a decades long holy grail pursuit.  Right now, it is a novelty.  In the near future, the implications are immense.

Which brings us to why should you care.

Earlier this week the House majority leader, Eric Cantor, suffered an enormous defeat in Virginia's Republican primary by Tea Party candidate David Brat.  No one predicted this - the polls were wrong, by a long shot.  Frank Luntz, a Republican pollster and communication advisor, offered up his opinion on what was missing in a New York Times Op-Ed piece - lack of face-to-face discussions and interviews with voters.  He asserts that while data collection was limited to discrete survey questions, what it lacked was context.  Information such as voter mood, perceptions, motives, and overall mind set were missing. Even if you collected quantitative data across a variety of sources, you don't get to these prescient indicators.  

The new wave of AI (the next 2 - 5 years) makes capturing this insight possible and at scale.  Marketing organizations are already using such capabilities to test advertising messages and positioning in focus group settings.  But, if you took this a step further and allowed pollsters to ingest full discussions in person or through transcripts in research interviews, street polls, social media, news discussions and interviews, and other sources where citizen points of view manifest directly and indirectly to voting, that rich content translates into more accurate and insightful information.

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PIM: MDM on Business Terms

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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