TRILLIUM SOFTWARE INSIGHTS

Data Governance – Where does Data Quality fit?

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

I’m often asked how data quality fits within data governance, or are they the same thing.  While both essentially strive for the same outcome of optimizing data and information results for business purposes, these disciplines address that goal from very different angles.  They are not the same, but complement each other for the good of your business.

From a high level, many companies may view data quality as a subset or part of data governance.  In other words, data quality focuses primarily at a data element layer where technologies, best practices, and standards are applied to understand, analyze, monitor, fix, and report data anomalies within the data itself.

Data governance deals primarily with orchestrating the efforts of people, process, technology, and lines of business in order to optimize outcomes around enterprise data assets. This includes, among other things, the broader cross-functional oversight of standards, architecture, business process, business integration, and risk & compliance.  In other words, most anything that can impact the integrity, quality, and security of company information.

While data quality is mission critical for any organization, it often is relegated to a single project or data source within the company. But, this approach could severely restrict a company’s bottom line and its return on investment for four primary reasons:

• Project or silo based data quality only, prevents the company from understanding and addressing upstream problems that impact data outcomes caused by people and processes and that cannot be fixed by technology alone.

• Project-based data quality often leads to independent or isolated data standards, business rules, and data models that cannot be fully leveraged across the company, therefore duplicating operational costs for future projects.

• Project-based data quality many times leads to the implementation of multiple technologies for the same purpose within the company, which raises the cost of software, training, and maintenance.

• Enterprise capable technologies are utilized in a single instance when they could be leveraged across multiple projects and data sources.

Data governance exposes these opportunities and risks, and provides the cross-functional platform necessary for organizations to address them accordingly. In other words, data governance enables enterprise data quality by breaking down the barriers associated with project based data quality. All of which further improves data outcomes, bottom line performance, and return on investment.

Does this ring true for your company? What data quality projects are you working on, and how will data governance play in a role in your company to tie it all together?

Data Arbitrage – a new way to manage risk for profit

 By Richard Wilson, Vice President, Product Strategy, Harte-Hanks Trillium Software

Arbitrage is one of those concepts that seem incredibly complex and arcane until you see it in action.  It was originally a practice that gained currency (literally) before the advent of internet-speed information exchanges. In an earlier time, the trading price of commodities could vary slightly in different markets. If you knew the price difference, it was possible to simultaneously buy and sell something and realize immediate gain. This concept can be applied to financial markets or even more recreational endeavors.

Spring is around the corner, and for those of you who equate that with the start of baseball season, you may also know that the concept of arbitrage was used in a fantastic baseball book by Michael Lewis titled Moneyball. In it, he explained how the principles of arbitrage were applied by the small-market Oakland Athletics professional baseball team to achieve a completely different goal – winning baseball games.
 
If you haven’t read the book, it highlights how Billy Beane, the team’s general manager, implemented a series of key metrics that could be applied to the player performance statistics captured throughout the major and minor league baseball system to find undervalued talent. Statistics are everywhere in baseball, but the idea that you could develop statistical metrics to spot and exploit something as seemingly arbitrary as performance on a baseball diamond to win major league baseball games was a stroke of genius. It enabled the Oakland A’s to consistently outperform teams in its division and across the major leagues every year.

One of the great pleasures of my work is that it gives me the opportunity to travel and meet the kind of people who have the same level of insight and intelligence as Billy Beane. Only for them, the challenge is to develop metrics that spot misalignments in financial and customer data that can be exploited to improve operational performance.
 
The same problem exists with data management as in baseball…statistics are everywhere, but you have to be creative and brilliant to develop the metrics that will bring out benefits and minimize risks, resulting in measurable ‘wins.’
 
That’s really what is happening in some of the leading financial services firms I’ve talked with.  Creative minds have begun to spot where data gaps and inaccuracies incur punitive risk reserves. They are finding ways to spot and resolve these gaps, enabling their organization to free up badly needed investment capital that can be pumped back into the investment economy.
 
These days, no available resource is more important than a firm’s maximum investment capital. It’s the equivalent of adding a number one starter to your pitching rotation or a potent number four hitter in the lineup. 
 
I am going to enjoy watching this ‘season’ unfold.