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:
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 The financial services industry, like many others, realized that the business data they relied on was often outdated, inaccurate, and maintained with archaic, manual processes.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?
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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