To meet the complexity of global challenges during uncertain times, data agility is critical to our client’s success. We work with our clients by focusing on their data quality and governance so they can adapt to changing market forces, stay competitive and become more resilient. Clients tell us improved data agility provides more opportunities to monetize their data, the capability to respond quickly to their data pipeline requirements and build new use cases.
Aliye Ozcan, Head of IBM DataOps Marketing
How have we implemented IBM DataOps with our clients?
IBM works with clients to help modernize their data operations (DataOps), we look at opportunities to automate and drive efficiencies with their processes, workflows and data pipelines—to result in improved data quality. IBM is collaborating with Standard Bank, a financial services group in Africa, that is headquartered in Johannesburg, South Africa and operates across 20 countries on the African continent, with reported assets of approximately USD 157 billion in 2019.
Standard Bank is generously providing a retrospective on their data management journey in South Africa and how it enabled them to improve their data quality, data agility, governance, and metrics reporting while creating a data stewardship and self-service culture. Let me introduce, Itumeleng Monale, Head of Information Management and thank Itumeleng for sharing Standard Bank’s journey with our diverse data community.
Itumeleng Monale, Head of Information Management, Personal & Business Banking, Standard Bank
DataOps journey
Our data journey with IBM started when needing to respond to regulatory changes. We have since evolved to applying the technology to various business cases; most recently adapting to COVID-19 market conditions and now looking to the future for building new use cases with DataOps and AI. The journey begins with seeking to improve our data management, in the areas of data quality, governance, business reporting and customer experience.
Regulatory market changes
The first market force challenges were regulatory -the Basel Committee on Banking Supervision (BCBS) 239, as well as the local regulatory standards and guidelines for record keeping to support Anti-Money Laundering. This regulatory requirement evolved as our first use case for end-to-end data management. A regulatory fine escalated this to a burning platform. We were investing tens of millions of dollars on data fixes in disparate places and we needed a disciplined data lifecycle approach that was sustainable. Improved data management required us to modernize our data operations with a data integration platform, governance catalog, and software tools for analyzing, cleansing and integrating data. By implementing a suite of IBM DataOps software with two-week agile implementation sprints, we now have a data catalog with its metadata to meet regulatory and compliance requirements— and embedded data quality and governance.
After meeting regulatory requirements, we then expanded on new use cases to improve our business reporting and customer experience, realizing data agility was as much of an asset as money in the bank. For business reporting, we changed the aggregation of our data sources, transforming from batch delivery styles and using static dashboards to persona-based access for near real-time reporting. Our branches improved their seller’s productivity as business performance monitoring reports provided metrics and insights on their marketing tactics. It also allowed for improvements in data modeling and provided our service teams the agility to make decisions for improving the customer experience.
Our data catalog, combined with our master repository for client data, became our single source of truth with embedded governance, capabilities to share a common business glossary of terms and definitions, and the ability to track data lineage. Software tools provided the capabilities to understand, cleanse and transform our data, while also analyzing the quality, structure, format and related relationships. Enabling the capabilities for self-service access to our data was critical to success, neither did we know how important it would be for our future—the agility needed to adapt to market forces.
Building agility into our culture
Also contributing to our success was creating an agile culture of continuous improvement, refining the criteria for data quality and business rules and definitions for the data catalog, conducting two-week sprints, standup’s and implementing metrics dashboards. We are beginning to see more cultural changes across our business units, such as data stewardship, data owner accountability and self-service access evolving across more job roles.
COVID-19 market forces
In 2019, our second use case focused on how to monetize our data for new business opportunities, versus just a focus on mitigating bank risk. For example, we mapped 52 key metrics to our target data sources and created dashboards to measure our success, while delivering metrics reporting in 24 hours. With COVID-19, our relationship bankers are working remotely and the ability to provide self-service access to dashboards has kept our teams engaged to maintain our productivity and business continuity metrics. These interactive dashboards have helped adjust to this new way of working and keeping our teams focused on meeting business commitments. The metrics reports with trusted, high-quality data have helped prepare Standard Bank for the implications of COVID-19 and the agility needed to navigate and pivot to new market conditions.
During COVID-19, we rapidly responded by creating a marketing tactic that leveraged client records with small business or student loans. Clients were advised their monthly loan payments would be deferred for three months due to COVID-19. We know our clients appreciated our level of responsiveness and especially not having to contact the bank directly to determine eligibility— a competitive differentiator in the markets we serve.
AI opportunities
What’s next for Standard Bank is to prepare for our digital future and build use cases for business opportunities with AI. Building a foundation for AI capabilities can help us leverage the API economy, as we can partner and ingest data from third party sources to create new revenue models. We will continue to focus on building our data agility and resilience as we partner with IBM and other entities on AI technologies— so we can be prepared for future disruptors.
Learn more
Learn how IBM DataOps capabilities can create opportunities to monetize your data, increase data agility and build new models for AI.
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