TRILLIUM SOFTWARE INSIGHTS

Data Governance – No Two Solutions are Ever the Same

By Jim Orr, Director, Enterprise Data Strategy, Harte-Hanks Trillium Software

The intriguing thing about data governance is that there never seems to be a right or wrong answer to anything you do. Rather than hold steadfast to the virtues of planning a data governance project via one standardized method, I instead, initially tout general best practices and methodologies that will increase your odds of success, knowing that one customer’s solution will be quite different from another. To some folks, this might seem too simplistic early on, but in the end, it really makes a lot of sense, especially for this dynamic discipline.  The key is asking a lot of questions about data governance goals and needs to learn more about the customer, instead of simply talking and trying to sell a solution that won’t help them.

For example, when it comes to data governance there are a variety of elements that determine where, when, why, and how a company should approach a project. Some of the most prevalent of these include:

• Company size and complexity
• Industry
• Business drivers
• Business processes and technology
• Regulations
• Data volumes and type of data
• Executive awareness and support
• Ability to execute
• Funding

To complicate matters, none of these attributes are identical from one company to the next. In fact, because companies are so different, there are no data governance programs that look remotely the same, even between organizations in the same industry!

Just this past week, I spoke to two very contrasting organizations.  One was a multi-billion dollar/multi-national insurance organization and the other a 75 person contractor overseeing government programs and grants. Both had legitimate and similar data governance needs, but each will have very different solutions with different results and final goals.

With that said you can understand my dilemma when I’m commonly asked to explain exactly how data governance will or should work in a prospective organization. I may have a general idea but the real answer is that I never know for sure until I’ve had an opportunity to understand the finer points of the company’s business.

Companies really should keep this in mind when researching and planning for data governance.  While it is important for an organization to hear about the success and case studies of similar companies in their industry, it is infinitely more important to look to the cross-industry best practices and methodologies that generated the success.  You need to then apply these best practices to your own individual organization to create your customized data governance program and process that will succeed and generate the results that you need.

Bank Data on the Hot Seat for Poor Regulatory Reporting

By Steve Bailey, Director, Financial Services Solutions, Harte-Hanks Trillium Software

Regulatory requirements for enterprise risk reporting are critical considerations for banks, especially in the current political climate.  So, as a result, many organizations that I work with are capitalizing on their improvements in data quality principally to achieve better risk management and reduced financial exposure.

Once business leaders understand how data quality and data management affect the bottom line of their business, data quality suddenly becomes a top priority.  That’s what we’re seeing on an increasingly regular basis.

Improved regulatory reporting turns out to be a terrific by-product of improved data quality.  For the business, that improvement leads to new data quality initiatives. Through more in-depth data quality, managers are able to demonstrate knowledge about the data that goes into their risk rating models.  As a result, it becomes possible to accurately report on data quality standards to internal and external auditors.

So, as I see it, enterprise data management is critical to financial services firms and their efforts to better operate and comply with new levels of regulatory oversight.

If data quality is managed correctly, financial services firms will be competitively positioned to bring new product offerings to the market and provide a better customer experience. Technology that fosters data quality and governance allows firms a far greater ability to access more timely data. Creating enterprise level risk management in compliance with regulatory reporting requirements makes the data quality challenge even more complex for these financial service firms.

Given the current regulatory oversight that banking firms have to manage under, best practices of data governance require that the implementation team understand the business policies, and collaborate with technology teams to specify, identify, validate, and manage data. As a result, the responsibility for demonstrating provably correct accuracy shifts from IT to the business users.

Technology teams must continue to promote best practices for systems, security, and platforms as the business begins to work with IT to improve best practices for information accuracy, information quality and data integrity. I refer to this concept of business best practices and data governance as ‘data veracity.’ Data veracity captures the business’ responsibility for data accuracy and it transcends the traditional standard metrics for IT data quality.  Data veracity to me means better regulatory reporting.  What can data veracity mean to you and your business?