Top Four Reasons Why Financial Services Companies Need Solid Data Governance

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In working with clients in the financial services business, I’ve noticed that there is a common set of reasons why they adopt data governance.  When it comes down to proving value of data management, it’s all about revenue, efficiency and compliance.

Number One - Accurate Risk Assessment
Based on new regulations like Sarbanes and Dodd-Frank, a financial services company's risk and assurance teams are often asked to determine the amount regulatory capital reserves when building credit risk models. A crucial part of this function is understanding how the underlying data has the on the accuracy of the calculations. Teams must be able to attest to the quality of the data by having in place the appropriate monitoring, controls, and alerts.  They must provide regulators with information they can believe in.

Data champions in this field must be able to draw the link between the regulations and data. They must assess the alignment of data and processes that support your models, quantify the impact of poor data quality on your regulatory capital calculations, and put into place monitoring and governance to manage this data over time.

Number Two – Process Efficiency
If your team is spending a lot of time checking and rechecking your reports, it can be quite inefficient. When a report generated conflicts with another report, it may bring some doubt to the validity of all reports. There is likely a data quality issue is behind it. The problem manifests itself as a huge time-suck on monthly and quarterly closes.  Data champions must point to this inefficiency in order to put in place a solid data management strategy.

Number Three - Anti-money Laundering
Financial Services companies need to be vigilant about money laundering. To do this, some look for currency transactions designed to evade current reporting requirements. If a client is making five deposits of $3,000 each in a single day, for example, it may be an attempt to keep under the radar on reporting. Data quality must help identify these transactions, even if the client is making deposits from different branches, using different deposit mechanisms (ATM or Customer Service Rep.) and even when they are using slight variation on their name.

Other systems monitor wire transfers to look for countries or individuals that appear on a list compiled by Treasury’s Office of Foreign Assets Control (OFAC). Being able to successfully match your clients against the OFAC list using fuzzy matching is crucial to success.

Number Four – Revenue
Despite all of the regulations and reporting that banks must attend to, there is still obligation to stockholders to make money while providing excellent service to the customers.  Revenue hinges upon a consistent, current and relevant view of clients across all of the bank’s products.  Poor data management creates significant hidden cost and can hinder your ability to recognize and understand opportunity – where you can up-sell and cross-sell your customers.  Data champions and data scientists must work with the marketing teams to identify and tackle the issues here.  Knowing when and how to ask the customer for new business can lead to significant growth.

These are just some examples that are very common to financial services.  In my experience, most financial services companies have all of these issues to some degree, but tackle them with an agile approach, taking a small portion of one of these problems and solving it little by little. Along the way, they follow the value brought and the value potential if more investment is made.

Big Data After the Hype

Total Data Management

This year, I’ve been following the meteoric rise of big data. It has been a boon for vendors who are venturing into this area.  It has produced countless start-ups and much buzz in the data management world.

However, when it comes down to it, what we’re really talking about here is data management and data governance.  Whether you have to deal with big data, enterprise data or spread-marts, data needs to be managed no matter what size. The tides are turning for a total data management approach. Recent surveys shows that despite the market hype, most technologists and business users feel that big data is an off-shoot of data management, not a branch of technology in itself. 

So, why the hype?  I'm convinced it is mostly vendor-generated. In 2010, when big data began to gain notoriety, there was a disconnect for some vendors.  While partnered with traditional enterprise data management companies like the Oracle and IBM’s of this world, not all vendors were prepared for the growing popularity of open source and Hadoop. Others were (and still are) better positioned. They began talking about big data as a product differentiator. Vendors who don’t have the basic architecture for managing data in Hadoop have been and will continue to struggle. 

For example, ETL tools that have a basic connection to move data in and out of Hbase, Hortonworks and Cloudera can’t stop there.  The power of Hadoop must be harnessed, and it’s not always an easy thing to do when your technology requires executables tied to CPUs.  One of the powerful things about Hadoop is that it scales based on a languages like PIG, Sqoop and Java without having to install anything.  Want to expand the number of servers?  Add a datanode server, tell the name namenode and rebalance - and your off and running.  However, even this simple innovation is more difficult on some vendors’ architectures than others.

Another rethinking that is taking place in the market is long-standing CPU-based pricing structure.  Vendors who they keep their pricing structure based on core processors for Hadoop will continue to struggle because it runs counter to the power of Hadoop. You hear about the volume, velocity and variety.  Technically, if you want to step up the volume with another datanode, it’s no big deal. However, it becomes a big deal if you have to renegotiate a vendor contract each time.

Last year, around this time, I did write about the various costs associated with the scale of data. In summary, the costs of licenses and connectors are the bigger for enterprise data, while the costs associated with skills are more likely to affect you with big data.  There will come a time where the skills gap will be closed, however.

In the year 2013, we’ll begin to see the un-hyping of big data in favor of this total data management approach. For buyers, big data will be a tick-box in their RFP’s in the effort to manage data, no matter what the size.