This brief interview with her will reveal just why she’s so passionate about data governance and how she thinks the industry can get more people going into this competency.
Thuthu, you’re one of those professionals who’s passionate about data governance and management – an area that perhaps doesn’t grab the data analytics headlines. How do you sell the value of these fundamental areas across the business? And what advice can you give to others who are tasked with this role?
Data Governance not linked to business value is fruitless, you will forever live in the world of self-justification as a profession. For any data driven business improvement or growth opportunity there is a component of data governance that becomes paramount to the success of that initiative. Data availability, accessibility and quality are always the key draw cards which make the discipline indispensable.
You’re responsible for the development of RMB’s data and information strategy. Are there some key components that you feel must be include in a data strategy? And where does it sit within the broader corporate strategy?
A good strategy should possess both the defense and the offensive interventions aligned to the business strategy and in the center of it all is the Culture. A strategy that does not plan or design for the cultural composition of the organisation is destined to fail. The real elephant in the room is always the mindset change required to drive a data strategy. Appropriate change management interventions and problem statements that connect with the organisation drive the willingness to implement the strategy.
There’s so much focus on recruiting Data Scientists that very little is said about the skills shortage in other data competencies. What’s your perception of the skills shortage and what do you think needs to be done to increase the number of people going into these more fundamental roles?
My personal departure point on Data Scientists is that the focus should be on Data Science as a discipline as opposed to a single resource that will derive insights from our data. If we look at it as a discipline and assess existing capabilities within an organisation, we will realise that we may not be not too far off. i.e. if you assess the Gartner Analytics Maturity model where you have the Descriptive up to Prescriptive analytics, you will notice that most organisations play in the former stages of analytics primarily the Descriptive and Diagnostic nature of data interrogation.
The use cases on Predictive and Prescriptive analytics are only emerging now and that’s really where data scientists partnered with business SMEs, Data Engineers and Data Analysts will create ‘magic’ in the space.
The skill set debate should start at Tertiary level, through applying practical level training to the students so they become productive from the first day they engage with the corporate environment. The corporates should also look to partner with such institutions to derive applicable and effective ways of teaching and grooming the young minds.
We’ve talked briefly about the number of women in data. What’s needed to get more women into this profession?
Interventions at grass route level are essential. I remember in high school we used to have a career resource centre which highlighted various jobs one could get into at that time there was nothing on data, it would be interesting to see how that has been updated and how the career counselors speak of the data profession. It requires that people in my position are deliberate about imparting knowledge, deliberate in recruiting and elevating the profession to entice the diverse skills and talent. I am actually a product of such interventions when a woman that is well regarded in the profession introduced me into the profession is 2011.