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?
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Data is living in our business under outdated data policies and rules.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.
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.
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.
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.
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.
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.