Judgement Day for Data Quality

Joining in on the spirit of all the 2013 predictions, it seems that we shouldn't leave data quality out of the mix.  Data quality may not be as sexy as big data has been this past year.  The technology is mature and reliable.  The concept easy to understand.  It is also one of the few areas in data management that has a recognized and adopted framework to measure success.  (Read Malcolm Chisholm's blog on data quality dimensions) However, maturity shouldn't create complancency. Data quality still matters, a lot.

Yet, judgement day is here and data quality is at a cross roads.  It's maturity in both technology and practice is steeped in an old way of thinking about and managing data.  Data quality technology is firmly seated in the world of data warehousing and ETL.  While still a significant portion of an enterprise data managment landscape, the adoption and use in business critical applications and processes of in-memory, Hadoop, data virtualization, streams, etc means that more and more data is bypassing the traditional platform.

The options to manage data quality are expanding, but not necessarily in a way that ensures that data can be trusted or complies with data policies.  Where data quality tools have provided value is in the ability to have a workbench to centrally monitor, create and manage data quality processes and rules.  They created sanity where ETL spaghetti created chaos and uncertainty.  Today, this value proposition has diminished as data virtualization, Hadoop processes, and data appliances create and persist new data quality silos.  To this, these data quality silos often do not have the monitoring and measurement to govern data.  In the end, do we have data quality?  Or, are we back where we started from?

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Ethical Use: Do You Have A Data Policy for That?

Security and privacy have always been at the core of data governance.  Typically, company policies, processes, and procedures have been designed to comply with these regulations to avoid fines and in some cases jail time.  Very internally focused.  However, companies now operate in a more external and connected fashion then ever before.

Let's consider this.  Two stories in the news have recently exposed an aspect of data governance that muddies the water on our definition of data ownership and responsibility.  After the tragedy at Sandy Hook Elementary School, the Journal News combined gun owner data with a map and released it to the public causing speculation and outcry that it provided criminals information to get the guns and put owners at risk.  A more recent posting of a similar nature, an MIT graduate student creates an interactive map that lets you find individuals across the US and Canada to help people feel a part of something bigger.  My first reaction was to think this was a better stalker tool than social media.

Why is this game changing for data governance and why should you care?  It begs us to ask, even if a regulation is not hanging over our head, what is the ethical use of data and what is the responsibility of businesses to use this data?

Technology is moving faster than policy and laws can be created to keep up with this change.  The owners of data more often than not will sit outside your corporate walls.  Data governance has to take into account not only the interests of the company, but also the interests of the data owners.  Data stewards have to be the trusted custodians of the data.  Companies have to consider policies that not only benefit the corporate welfare but also the interests of customer and partners or face reputational risk and potential loss of business.

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The Kiss of Death for Data Strategy

The number one question I get from clients regarding their data strategy and data governance is, “How do I create a business case?” 

This question is the kiss of death and here is why.

You created an IT strategy that has placed emphasis on helping to optimize IT data management efforts, lower total cost of ownership and reduce cost, and focused on technical requirements to develop the platform.  There may be a nod toward helping the business by highlighting the improvement in data quality, consistency, and management of access and security in broad vague terms.  The data strategy ended up looking more like an IT plan to execute data management. 

This leaves the business asking, “So what? What is in it for me?”

Rethink your approach and think like the business:

·      Change your data strategy to a business strategy.  Recognize the strategy, objectives, and capabilities the business is looking for related to key initiatives.  Your strategy should create a vision for how data will make these business needs a reality.

·      Stop searching for the business case.  The business case should already exist based on project requests at a line of business and executive level. Use the input to identify a strategy and solution that supports these requests.

·      Avoid “shiny object syndrome”.  As you keep up with emerging technology and trends, keep these new solutions and tools in context.  There are more data integration, database, data governance, and storage options than ever before and one size does not fit all.  Leverage your research to identify the right technology for business capabilities.

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