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Stack your offense with a solid data quality strategy

Melissa Nazar Data quality

Football season is in full swing. If you’re like me, you’re working to perfect your fantasy football line up. And if you’re a true fantasy football guru, you know all too well that a  good offense will score you many more points than a strong defense—that’s why you’ll find benches stacked with wide receivers and quarterbacks rather than defensive lines.

The same holds true when you look at data quality strategy. Yes, I did make a football/data quality analogy—'tis the season. Being on the offensive with your data quality will put you in a stronger position than constantly investing in your defensive line.

Defensive vs. offense data quality

What do I mean when I say offensive or defensive data quality? A simple explanation:

  • Defensive data quality: A reactive data quality approach, focused on attacking problems as they are identified, rather than avoiding them to begin with. A defensive approach is often in response to new industry standards and regulations and can typically be short-term in scope.
  • Offensive strategy: A proactive approach to data quality that is part of a larger, comprehensive data governance program. Not only does the strategy help prevent data quality surprises, but also helps drive potential cost savings and revenue opportunities for your business.  

Defense on the rise: A financial services case study

While the ideal state is focusing on your offensive posture and managing defensive issues in the instances they come up, the reality is that a defensive data quality approach is the case for a large number of companies.

Consider the banking industry. Since the financial collapse of 2008, financial institutions have been under increased scrutiny around their practices, particularly around data management and data quality. The financial services industry, like many others, realized that the business data they relied on was often outdated, inaccurate, and maintained with archaic, manual processes. Combined with the impact of rapidly merging and changing businesses, banks ended up with multiple versions of data as well as complex architectures that couldn’t talk to each other, making it impossible to discern which data is “good.”

As a result of all that bad data, accurate reflections of risk were not able to be provided, making it impossible for regulators and banks alike to identify growing systemic risk and take appropriate actions. This led to unsustainable practices by banks, which ultimately lead to the banking collapse of 2008.

Post-collapse, several regulations have been rolled out, including BCBS 239. This regulation is intended to help banks and regulators better identify risks through improved data governance, aggregation and reporting. Top of mind for banks is figuring out “what do we need to do to meet the requirements of BCBS 239?” (See my colleague, Dave Bresnick's recent post on the subject from the Data Governance in Financial Services Conference in September.) This is an example of a defensive posture. The real question should be “How can I proactively manage my data and implement a data governance program that supports my business needs?”

What’s my strategy like?

Curious where your strategy stands? Take a look at the statements below. Which ones represent your organization most?

Defensive approach

  • You do not have a documented data quality strategy.
  • Data quality is taken care of by tactical fixes.
  • You handle data quality when issues come up.
  • You consider data quality when the regulators come knocking.

Offensive approach

  • You have a documented data quality strategy in place and defined roles/responsibilities.
  • You seek out the root causes of data quality issues and work to address them.
  • You have both business and IT owners for data quality problems.
  • You monitor and review our data quality issues and strategy regularly.

Many organizations will start with a more defensive strategy, responding to an issue that came up unexpectedly or in response to new industry rules and regulations. Ideally, these strategies will be become more sophisticated and proactive over time, avoiding data quality fire drills. Determining where you are in data quality sophistication can help drive your next steps.

Avoid a defensive data quality strategy by being proactive and developing a program that supports your organization’s needs and objectives. Learn how with our whitepaper, Creating an ideal data quality strategy.

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