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 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. But to realize full value from that data, organizations need a strong data governance program in place that determines how data is collected, categorized, stored and accessed.
The business decisions that an organization makes based on its data will only be as good as the data in the first place. That makes it vital that data is controlled at the point and time of creation. The growing number of stakeholders depending on data insights and needing them in real-time makes it even more critical.
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 client's income and spending patterns, their savings profiles, their risk affinity, their demographic information, etc. By leveraging this information, banks can build a 360° view of their customers based on their activities across various channels and build up detailed customer profiles.
An enhanced understanding of these data points offers the banks immense possibilities to deliver services, such as car loans, real estate financing, insurances, etc. that resonate with their customers.
Similarly, corporate clients are expecting their banks to offer them value added services that improve their competitive advantage in the market place. They are increasingly demanding products and services tailored to their specific financing and payment cycle needs as well as end-to-end solutions that help them run their businesses more efficiently with reduced risk.
Banks can model their client's financial performance using multiple data sources, model scenarios; assist their clients with their financial planning, risk management, capital structuring, compliance, credit scoring, stress-testing, etc.
By mashing public domain data, such as share price movements, with proprietary internal customer data, further insights can be derived to better manage corporate accounts, identify and mitigate potential issues, improve customer relationships, and enhance their brand equity. The promise that data and analytics beholds is much greater, opening up whole new frontiers in financial services.
The new normal is less about banking, but more about consumers, tools and behaviors. In fact, the banking industry will face additional challenges because, being so highly regulated an industry, and it will be held to higher standards of compliance.
Considering the broad spectrum of customers that banks have traditionally serviced, they are forced to retain such expensive delivery channels as branch offices, longer than their nimbler upstarts that operate predominantly online. Fitness and other new competitors threaten to disrupt the relationships banks have with their customers through focused service offerings.
The wealth of customer data and the scale of the banking networks are assets that banks could leverage to simplify their operations and deliver customer excellence, from offering faster customer service, through personalized products and content through a better experience across all customer interaction channels.
High quality data is absolutely essential in delivering services that banks’ customers value and appreciate. Banks should accept the changed context that they have to strategize for and build additional capabilities. Each bank will have to pick a strategy that leverages its strengths and supports its business goals.
A solid data governance framework helps break data silos, catalyzes sharing of data and best practices about products, services and customers across business units, presents up- or cross-selling opportunities, improves customer retention rates, promotes conversations between the business and IT departments within a bank using the same nomenclature, and makes operations more efficient.
Hence, a data strategy that is closely aligned with the overarching corporate strategy is a key success factor. In other words, the chief data officer (CDO) – a role that is being increasingly formalized and adopted by several banks and chartered with delivering top-and bottom-line business outcomes powered by data – should be viewed as a perfect business partner with the chief operating officer (COO), rather than relegating this key role under the chief information officer (CIO) and treating data as an IT function, rather than as a business function. This role is more about business objectives, rather than IT objectives.
Furthermore, governed data is secure, reliable and meets the quality expectations from users that most rely on data for decision-making. Such consistent data helps identify the datasets most important for business. Knowing which key performance indicators (KPI’s) have the most impact and influence on the overarching corporate goals mandates understanding data at various levels of granularity.
Measuring the value of data governance efforts is challenging, as is the ability to articulate the return on investment (ROI) of data investments. But focusing on a few critical metrics can ensure the effectiveness of data governance programs and big data initiatives.
Similarly, classifying the available datasets based on existing business terminologies, building new taxonomies, mapping relationships, and establishing the policies, frameworks, rules, and standards for data capture, management, processing and consumption of critical data elements must be managed through the wider lens of business use cases. Data and analytics go hand in hand; it's also important for analytical models to be included in the data governance agenda.
The data governance approach and the analytical systems that implement it should be scalable to capture and dimension information about all areas of a bank. Processes need to be created or simplified for both, employees and customers alike. Visualizations that help derive insights should also be identified and articulated in the data governance agenda.
So, how can a bank get started on the path to reaping rewards through data governance?
Implementing a data governance process is not a herculean task. As daunting as it may seem, it does require a comprehensive structure and framework to address the right business levers that deliver sustainable value. Without the proper structure and forethought, it's impossible to harness, let alone extract value from data.
I recommend an iterative approach to executing on a data governance strategy. A portfolio of small projects delivering specific outcomes, building on top of one another will deliver the wins required to tackle the larger data governance agenda. These projects could take dimensions, such as building a catalogue of services, assigning data roles & responsibilities, architecting a data quality framework, etc. Instead of boiling the ocean, a lean approach - small and measured steps - will deliver the success that banks and other data-driven financial institutions expect.
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