Implementing DataOps across a banking enterprise
Imagine a day in the life of Sarah, a hypothetical Chief Data Officer at a major bank in South Africa. There are many expectations on her shoulders. She struggles to deliver business-ready data to fuel her organization and support the decision makers within the bank. It is her job to put in place a team that will make sense of the myriad of data sources and different representations of data, multiple formats and technologies used to store and move that data. It is also her mission to smooth over the complexities of new and legacy approaches to managing data to serve it up to data analysts and data scientists who will deliver compelling insights and automate the running of the bank with the help of artificial intelligence.
Any crack in her data pipeline spells disaster, as it represents an incomplete representation of the bank’s operational reality. And while managing this complex web of data, she needs to deal with multiple regulations, including data privacy regulations.
But consider the alternative: without Sarah's expertise at the helm, her bank will open up their footprint in the market to old and new competition, with challenger banks, fintech startups and growing volumes of new market entrants who are ready to pounce on any new opportunity for market share.
According to MIT Sloan, there are three things that Sarah needs to focus on:
- Establishing a data office: This involves firmly defining the scope of the role in providing data as a resource to the bank, identifying the key executive stakeholders, and understanding the commitments that every player in the data chain makes to a collaborative operation.
- Aligning with business objectives: When the financial crisis of 2007 hit banking institutions particularly hard, the immediate aftermath involved securing financial operations and addressing credit risks to increase resilience. Increasing market confidence has since seen a shift to rapid response to new opportunities that can only be delivered by an informed and data-led approach. In short, unless there is excellent communication between business and data delivery, Sarah knows that her bank will not thrive.
- Scaling data successes: With every data-led initiative, Sarah needs to ensure that the data produced can be used and reused endlessly, with value increasing each time. This can only be achieved when it is shared centrally, is searchable and aligned with a business language.
IBM DataOps Center of Excellence and a non-hypothetical, actual large bank in South Africa have addressed these challenges by successfully using DataOps to help drive an efficient, self-service data culture to make business-ready data available to the right people and processes to make better business decisions through automation. In a previous blog, my colleague defined what DataOps is and the core components that support a successful practice.
The bank was struggling as each of their country operations tried to succeed in their own digital transformation. They needed to figure out a way to transform their bank to meet the diverse and local needs across Africa and grow their customer base while increasing revenue.
They understood that knowing their customers and relationships could make the difference for their business, allowing them to market and cross-sell or upsell their customers. But many of their operations involved manual steps and led to inconsistencies. They needed to standardize how they defined data across the whole continent, and they needed to scale fast. DataOps provided the framework and approach to focus and deliver iteratively, through:
- Aligning business goals to project deliverables provides the link between executive stakeholders and the ability to exhibit tangible results
- Continuous delivery of business results based on high-quality data is tracked and monitored by the establishment of relevant KPIs
- Technology leveraging the benefits of machine learning, eradicating the need for slow, manual processes
- Communication streams providing a continuous update on the work required, both ensuring delays between contributors are minimized and ensuring executive stakeholders self-inform on progress at any point in time
This South African bank has left the stress and worry of complex and disorganized availability of business-ready data behind, replacing it with a well-oiled machine that inspires confidence in its ability to deliver. The needs of the bank will doubtless change over the coming months and years. But with DataOps in place, Sarah can be confident that she can provide trusted, high-quality data for the bank's data consumers to use.
Learn about the DataOps framework
The shift to adopt DataOps is real. According to a recent survey, 73 percent of companies plan to Invest in DataOps. Accelerate your DataOps learning and dive into the methodology by reading thewhitepaper Implementing DataOps to deliver a business-ready data pipeline.