Crossing the chasm from data management to data governance

The views and opinions expressed in this article are those of the thought leader and not those of CeFPro.

By Jeff Prelle, SVP, Risk Analytics & Data Governance, Bancorp South

What do you believe are they key challenges associated with data governance?

Data Governance is still a fledgling practice in the ways of best practices and research. The amount and importance of data has grown exponentially over the past twenty years. Companies across industries have begun to realize the value of data and analytics and are excited about the possibilities that the lower cost of storage and greater power of analytics can provide. Key challenges quickly become evident as data governance spans multiple industries and disciplines at various phases of data management. Leaders begin to question whether they can trust the data and how does our company ensure the data is accurate and complete? More importantly, how do we create accountability across the various data handshakes in the organization?

Information Technology in our organizations has become the backbone of all companies today and is great at managing the data and ensuring the Extract Transform Load processes, securing information, and creating strong data redundancies. These processes are critical to our data infrastructure; however, a key challenge exists in optimizing workflows that leverage the day-to-day business expertise of each business unit to understand the nuances of data elements in the data warehouse or cloud storages.

The challenge for data governance is customizing data governance for different business lines due to different maturity levels of data understanding. These fundamental differences have to be addressed pragmatically with prioritization being paramount to the overall Data Governance initiative. In short, Data Governance becomes the bridge between the day-to-day business lines, Data Management and IT.

Why is it important to understand data governance strategy?

Data Governance strategy is imperative to optimize the probability of success in data governance practice. Data Governance requires existing business line professionals to participate in and believe in the Data Governance process to see long-term results. In doing so you ask business line leaders to take on additional duties that they may not foresee immediate results.

Identifying the best strategy for your organization and its current data maturity state is important. For instance, if the organization desires to have a federated data system and the organization has massive data sprawl and legacy data systems, it will be important to control the data sprawl by consolidation of data and governance first. Doing so allows the data governance to identify duplicate data sources and elements.

Why can transitioning to cloud-based applications be challenging?

Due to GLBA requirements, Banks want to ensure data privacy for customers and third parties. As a result, it is important for management to preserve (and enhance where needed) the necessary internal controls related to privacy. This involves thoughtful planning and discussion with the cloud service provider to ensure consistency in terminology and an equal understanding of the processes being implemented.

Moving data to cloud-based applications has many advantages but it is worth noting that some cloud-based applications may not have the same functionality as your on premise application versions. Furthermore, an additional layer of monitoring is necessary to be able to respond to updates from the cloud service provider. These monitoring processes are critical for effective data management through cloud-based service providers. Finally, evaluation of Service Level Agreements and negotiating the parameters and assessment of performance of these agreements are paramount to cloud transitions.

Why is tracking data lineage into management reports important?