Chief Data Officer, Absa Bank Kenya
Hartnell Ndungi was the first Chief Data Officer to be appointed in East and Central Africa. In the banking and financial services sector, he has been able to master the development and execution of key data and digital strategies for large financial institutions.
He is Absa Kenya’s Chief Data Officer and he leads the bank’s data science, data governance, BI and analytics teams. Previously, he worked as a senior data consultant, providing advisory and analytics services to governments, banks, insurance firms and telecoms companies. He has also led and set up data teams for top banks in the region.
With a deep understanding of AI, data management and digital transformation, Ndungi seeks to ensure that organizations are able to set up dynamic data teams, understand the data they hold, harness actionable insights and deliver key use cases that drive profitability, operational efficiency, growth and transformation.
He holds a master’s degree in analytics from Georgia Institute of Technology, an executive data science specialization from Johns Hopkins University and a degree in electrical and electronics engineering from Moi University.
Getting to know...
Our research suggests that 82% of analytics leaders believe that enterprises that don’t embrace AI will lose market share to their competitors within five years. What are your thoughts on this?
This is totally correct for organizations in the financial services, retail and manufacturing sectors. AI technologies have unlocked new capabilities which improve customer experience, operational efficiency, business penetration, fraud prevention and access to new market ecosystems. Companies that adopt these technologies are able to gain competitive edges in their service offerings and customer retention strategies, which set them apart from their competitors.
Getting familiar with and embracing these technologies is, however, just one aspect to deriving value from AI. Hiring qualified resources, identifying correct business problems, prioritizing use cases, iterating delivery and implementing impactful change management initiatives are other prerequisites to gaining the competitive edge.
Ensuring good data quality is still a challenge many organizations wrestle with. Why do you think this is?
Most companies seek to tackle data quality by reconfiguring data entry points and launching ad hoc data cleanup programs. But no data quality initiative can be successful without proper data governance.
Data quality assessment and remediation projects are most effective when done in a structured, governed environment. This involves having qualified data owners for all data domains in an organization and appointing experienced data stewards who are hands-on to solve data management and quality issues.
Other ownership roles that work closely with owners and stewards are subject matter experts, business stakeholders and senior executives.
A proper oversight structure for data should also be in place to provide support, governance and approval. A monthly data governance council and a biweekly working group are usually sufficient for most organizations.