In my recent posts, I shared my experiences at the inaugural Chief Data Officer Africa Forum held in Johannesburg earlier this month. The event gathered Chief Data Officers (CDOs) representing various financial services firms. Their primary focus was on striking a balance between delivering advanced analytics and establishing robust foundations for data governance.

Key Themes

During the forum, several key themes emerged, aligning with those discussed in a recent Information Management roundtable on “How Financial Firms Can Improve Data Quality.” The roundtable, sponsored by Precisely Trilium, brought together senior data management leaders from major global banks and insurance groups, leading to insightful discussions.

Here are some of the prominent themes:

1. Advanced Analytics Driving Data Investment

CDOs emphasized the symbiotic relationship between data initiatives and analytics initiatives. The demand for advanced analytics is becoming a driving force behind investment in data strategies.

“A lot of my data initiatives are driven by my analytics initiatives, kind of a symbiotic supply and demand area.”

Louis DiModugno

2. Enterprise Information Management (EIM) and Trusted Data

Creating and maintaining trusted data is a fundamental aspect of Enterprise Information Management (EIM). Participants stressed that providing inaccurate information undermines confidence in the data and its usability.

“… if you put bad information out there, it is hard to get everyone back on board with confidence in the information you are providing them.”

Jed Maczuba

3. Contextual Understanding

The lack of contextual understanding of data usage can pose challenges for consumers. It is crucial to comprehend where the data comes from, how it was filtered, grouped, and what has been done to it before using it.

“The thing that keeps me up at night is what happens to that second or tertiary consumer that perceives the tremendous potential of this data and wants to use it, but they do it without knowing what it means, where it came from, how it was filtered or grouped, or what has been done to that data’”

David Gleason

4. Empowering Business Users with Data Quality Tools

Participants highlighted the importance of accessible data quality tools for business users. Involving those who interact with the data in the quality assessment process significantly impacts the choice of analytics tools.

“… if the actual consumers of data or the people who interact with the data are most familiar with it and are in the best position to affect or to determine its quality or, in fact, improve its quality, it has a profound bearing on what kind of analytics tools are most appropriate to use.”

SR Ramakrishnan

5. Demonstrating Value Through Data Governance

To prove the value of data governance, measuring data quality is essential. CDOs emphasized the significance of demonstrating the tangible benefits of effective data governance practices.

“To demonstrate value [of data governance] you have to first be able to measure data quality.”

SR Ramakrishnan

6. Data Scientists and Their Role

Most data scientists are aligned with business units, but IT also plays a pivotal role in facilitating data access and self-service capabilities. Collaboration between data scientists and IT teams is crucial for optimal data utilization.

“Most of the data scientists are sitting with the business units. But within IT, there’s one or two – especially a liaison looking at things and helping to facilitate access to data, self-service or whatever the case may be, or data acquisition, so that’s really important”

Eric Meltzer

7. Rising Concerns about Privacy

Privacy emerged as a growing concern. As organizations manage vast amounts of data, information security and privacy have become vital aspects of data management.

“I look back at how over the last decade or so, all of the people in our business were aware that we all had a role in managing risk. Now we are all aware we have a role in managing information security.”

David Gleason