In this special guest feature, Joe Fuqua of North Highland reviews the importance of strategy and governance of big data. Joe is the senior director of information strategy within North Highland’s Data and Analytics network. Joe has more than 25 years of experience in designing, deploying, and managing leading-edge technologies to create competitive advantage and enhance business value.
It’s no secret that big data has the potential to make a profound impact on a company’s bottom line – but there is a big issue with implementation that needs to be addressed. Organizations rolling out big data projects need to have a strategy in place for governance the information collected.
Let’s face it – big data is messy by its very nature. Banks in particular are overloaded with gigantic sets of information from dozens of sources, both structured and unstructured. For decades, financial institutions have largely ignored the majority of data collected, focusing only on information central to running the business and/or complying with regulators.
Interestingly, the latter focus – regulatory compliance – has driven a level of rigor in collecting, managing and distributing information that has drastically increased the reliability of critical data and arguably, enabled modern financial institutions to be managed better and ultimately, more competitive.
So what about ‘big data’ (I’ll defer to the reader to choose one of the myriad definitions – they’re all relatively similar)? Due to its nature, big data embraces almost the opposite view. In many cases, to consume and process enormous volumes of complex data, controls are sacrificed for speed and volume. Redundancies and inconsistencies are accepted as a cost of deriving new insights.
This occurs more frequently in less regulated industries, such as retail, but is generally unacceptable in banking. Therefore, almost all big data applications in banking thus far sit on the periphery, used most commonly for mundane tasks such as data archival. The financial services industry needs to be rigorous in how all information is managed in a big data environment. For example, typical big data storage technologies are not designed to prevent information from becoming outdated from one location to another.
To leverage big data for the betterment of the business, where it can have a real impact on activities such as customer behavioral analysis and product cross-sell optimization, the industry needs to take a step back and adopt traditional data management/governance techniques, such as data stewardship, the concept of Authorized Data Sources, and an intentional focus on data quality. Key benefits to this approach include:
- Clear ownership – one person/organization owns the data and is responsible for overall quality, access, etc.
- Explicit, trusted sources – information consumed is used for the purpose intended
- Reliability – intentional focus on maintaining and improving the quality of data and any transformations
- Accepted use – access is only available to approved consumers (people and systems) and defined limits on how data is transformed and consumed
The long-term effects on adoption of big data across the enterprise will be significantly enhanced due to increased trust in the data. Furthermore, as big data is adopted into mainstream consumption by the business, regulatory and compliance requirements will be easier to address. The challenge, of course, is how to implement governance of big data; resistance to increased control and structure is natural, especially with emerging technology.
Fortunately, most banks have an existing structure in place, so the key is to identify the appropriate stakeholders who should work together to determine the best strategy for governance of big data. Oftentimes, a ‘test and learn’ approach works well – where enterprises implement one platform and ‘prove the concept’ then onboard the next platform.
Establishing a strong data governance foundation will ensure businesses manage this information with the same level of care that they would for any other mission-critical data and enable them to best compete and succeed in the marketplace.
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