Data governance vital to digital transformation efforts
Though big data is often associated with digital inputs, such as customers’ online behavior and social network interactions, the traditional data derived from purchase transactions, financial records and offline interaction, such as call centers and point-of-sale (POS) terminals contain a wealth of information that needs to be properly harnessed.
At the most fundamental level, extracting the value from such massive amounts of data requires a scalable technology infrastructure to manage the various attributes that characterize big data (e.g. volume, velocity, variety and veracity) and data collection interfaces. Take the financial service industry, where today several banks are at various stages of implementing data lakes –a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data.
The data structure and requirements are not defined until the data is needed. The technological tangent is only a part of the solution; any technological investment, such as a data lake or an enterprise data warehouse, also requires a concurrent implementation of the necessary governance structures, frameworks, processes and policies to properly manage, deploy, sustain and leverage data as a strategic asset.
Traditionally, businesses wanted more information about their customers, their products and their markets to support decisions. Data was governed as soon as it was discovered or sourced by the enterprise. It was very much a straightforward approach – integrate data with the existing infrastructure, govern it to the required standard, manage it in a central repository and then use it. Correspondingly, many businesses viewed their data infrastructure investments a cost centers that support core business operations rather than as a strategic asset that proffered competitive advantages.
However, the forces of digital transformation have disrupted this established perspective and made data operations central to business operations. Today, IT and data practices not only support the core business as they traditionally did, but they also deliver competitive insights, map customer behavior, amplify sales operations and enable other valuable services that demonstrate business value.
Today, businesses are replete with data, but simultaneously we move into an unsettling turf, where more data doesn’t automatically imply more trust. The new approach to data governance is iterative; it’s about profiling the data (structured / unstructured, origins / lineage, etc.), understanding its fitness for purpose and progressively determining the necessary governance structures. Businesses should simultaneously ensure that appropriate controls are in place, without having to trade off speed, agility, flexibility, and performance.
The banking industry, for example, is facing the forces of disruption, largely brought forth by the recent regulations, technological developments and changing consumer expectations. The banking industry is highly regulated and compliance is enforced by both, national and international regulatory bodies.
Banks are not only making significant investments in compliance operations to satisfy the expectations from their supervisory agencies, but are also developing such capabilities as analytics and omni-channel to deliver a consistent customer experience across all touch points as expected by their customers. Data Governance and Management are central to balancing the diverse expectations placed on them by various stakeholders, all under growing budgetary and regulatory constraints.
To present some scenarios, retail banks are uniquely positioned to understand their consumers better than businesses in other industries. They can see their clients’ income and spending patterns, their savings profiles, their risk affinity, their demographic information, etc.
