Let's Break All The Data Rules!

When I think about data, I can't help but think about hockey. As a passionate hockey mom, it's hard to separate my conversations about data all week with clients from the practices and games I sit through, screaming encouragement to my son and his team (sometimes to the embarrassment of my husband!). So when I recently saw a documentary on the building of the Russian hockey team that our miracle US hockey team beat at the 1980 Olympics, the story of Anatoli Tarsov stuck with me. 

Before the 1960s, Russia didn't have a hockey team. Then the Communist party determined that it was critical that Russia build one — and compete on the world stage. They selected Anatoli Tarsov to build the team and coach. He couldn't see films on hockey. He couldn't watch teams play. There was no reference on how to play the game. And yet, he built a world-class hockey club that not only beat the great Nordic teams but went on to crush the Canadian teams that were the standard for hockey excellence.

This is a lesson for us all when it comes to data. Do we stick with our standards and recipes from Inmon and Kimball? Do we follow check-box assessments from CMMI, DM-BOK, or TOGAF's information architecture framework? Do we rely on governance compliance to police our data?

Or do we break the rules and create our own that are based on outcomes and results? This might be the scarier path. This might be the riskier path. But do you want data to be where your business needs it, or do you want to predefine, constrain, and bias the insight?

Read more

Is Zombie Data Taking Over?

It is easy to get ahead of ourselves with all the innovation happening with data and analytics. I wouldn't call it hype, as that would imply no value or competency has been achieved. But I would say that what is bright, shiny, and new is always more interesting than the ordinary. 

And, to be frank, there is still a lot of ordinary in our data management world.

In fact, over the past couple of weeks, discussions with companies have uncommonly focused on the ordinary. This in some ways appeared to be unusual because questions focused on the basic foundational aspects of data management and governance — and for companies that I have seen talk publicly about their data management successes.

"Where do I clean the data?"

"How do I get the business to invest in data?"

"How do I get a single customer view of my customer for marketing?"

What this tells me is that companies are under siege by zombie data. 

Data is living in our business under outdated data policies and rules. Data processes and systems are persisting single-purpose data. As data pros turn over application rocks and navigate through the database bogs to centralize data for analytics and virtualize views for new data capabilities, zombie data is lurching out to consume more of the environment, blocking other potential insight to keep the status quo.

The questions you and your data professional cohorts are asking, as illustrated above, are anything but basic. The fact that these foundational building blocks have to be assessed once again demonstrates that organizations are on a path to crush the zombie data siege, democratize data and insight, and advance the business. 

Keep asking basic questions — if you aren't, zombie data will eventually take over, and you and your organization will become part of the walking dead.

Read more

Yellow Elephants and Pink Unicorns Don't Tell The Real Big Data Story

Big data and Hadoop (Yellow Elephants) are so synonymous that you can easily overlook the vast landscape of architecture that goes into delivering on big data value. Data scientists (Pink Unicorns) are also raised to god status as the only real role that can harness the power of big data -- making insights obtainable from big data as far away as a manned journey to Mars. However, this week, as I participated at the DGIQ conference in San Diego and colleagues and friends attended the Hadoop Summit in Belgium, it has become apparent that organizations are waking up to the fact that there is more to big data than a "cool" playground for the privileged few.

The perspective that the insight supply chain is the driver and catalyst of actions from big data is starting to take hold. Capital One, for example, illustrated that if insights from analytics and data from Hadoop were going to influence operational decisions and actions, you need the same degree of governance as you established in traditional systems. A conversation with Amit Satoor of SAP Global Marketing talked about a performance apparel company linking big data to operational and transactional systems at the edge of customer engagement and that it had to be easy for application developers to implement.

Hadoop distribution, NoSQL, and analytic vendors need to step up the value proposition to be more than where the data sits and how sophisticated you can get with the analytics. In the end, if you can't govern quality, security, and privacy for the scale of edge end user and customer engagement scenarios, those efforts to migrate data to Hadoop and the investment in analytic tools cost more than dollars; they cost you your business.

Read more

3 Ways Data Preparation Tools Help You Get Ahead Of Big Data

The business has an insatiable appetite for data and insights.  Even in the age of big data, the number one issue of business stakeholders and analysts is getting access to the data.  If access is achieved, the next step is "wrangling" the data into a usable data set for analysis.  The term "wrangling" itself creates a nervous twitch, unless you enjoy the rodeo.  But, the goal of the business isn't to be an adrenalin junky.  The goal is to get insight that helps them smartly navigate through increasingly complex business landscapes and customer interactions.  Those that get this have introduced a softer term, "blending."  Another term dreamed up by data vendor marketers to avoid the dreaded conversation of data integration and data governance.  

The reality is that you can't market message your way out of the fundamental problem that big data is creating data swamps even in the best intentioned efforts. (This is the reality of big data's first principle of a schema-less data.)  Data governance for big data is primarily relegated to cataloging data and its lineage which serve the data management team but creates a new kind of nightmare for analysts and data scientist - working with a card catalog that will rival the Library of Congress. Dropping a self-service business intelligence tool or advanced analytic solution doesn't solve the problem of familiarizing the analyst with the data.  Analysts will still spend up to 80% of their time just trying to create the data set to draw insights.  

Read more

Beyond Big Data's Vs: Fast Data Is More Than Data Velocity

When you hear the term fast data the first thought is probably the velocity of the data.  Not unusual in the realm of big data where velocity is one of the V's everyone talked about.  However, fast data encompasses more than a data characteristic, it is about how quickly you can get and use insight.  

Working with Noel Yuhanna on an upcoming report on how to develop your data management roadmap, we found speed was a continuous theme to achieve. Clients consistently call out speed as what holds them back.  How they interpret what speed means is the crux of the issue.

Technology management thinks about how quickly data is provisioned.  The solution is a faster engine - in-memory grids like SAP HANA become the tool of choice.  This is the wrong way to think about it.  Simply serving up data with faster integration and a high performance platform is what we have always done - better box, better integration software, better data warehouse.  Why use the same solution that in a year or two runs against the same wall? 

The other side of the equation is that sending data out faster ignores what business stakeholders and analytics teams want.  Speed to the business encompasses self-service data acquisition, faster deployment of data services, and faster changes.  The reason, they need to act on the data and insights.    

The right strategy is to create a vision that orients toward business outcomes.  Today's reality is that we live in a world where it is no longer about first to market, we have to be about first to value.  First to value with our customers, and first to value with our business capabilities.  The speed at which insights are gained and ultimately how they are put to use is your data management strategy.  

Read more

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
Read more

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?

Read more

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.*

Read more

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:

Read more

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
Read more