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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How Bad Are Firms In China At Data Management?

Data management is becoming critical as organizations seek to better understand and target their customers, drive out inefficiency, and satisfy government regulations. Despite this, the maturity of data management practices at companies in China is generally poor.

I had an enlightening conversation with my colleague, senior analyst Michele Goetz, who covers all aspects of data management. She told me that in North America and Europe, data management maturity varies widely from company to company; only about 5% have mature practices and a robust data management infrastructure. Most organizations are still struggling to be agile and lack measurement, even if they already have data management platforms in place. Very few of them align adequately with their specific business or information strategy and organizational structure.

If we look at data management maturity in China, I suspect the results are even worse: that fewer than 1% of the companies are mature in terms of integrated strategy, agile execution and continuous performance measurement. Specifically:

  • The practice of data management is still in the early stages. Data management is not only about simply deploying technology like data warehousing or related middleware, but also means putting in place the strategy and architectural practice, including contextual services and metadata pattern modeling, to align with business focus. The current focus of Chinese enterprises for data management is mostly around data warehousing, master data management, and basic support for both end-to-end business processes and composite applications for top management decision-making. It’s still far from leveraging the valuable data in business processes and business analytics.
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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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