By 20 July 2020 | Categories: feature articles


by Shaun Dippnall, Cofounder Explore Data Science Academy

As the COVID pandemic sweeps across the world, businesses are facing many challenges, some of them new, others already existing.  Often, in the scramble to adjust to a new reality, management of data in organizations can be side-lined, a fundamental error that organizations make at their peril. 

Why is this?  Because data, and the use of data, is core to corporate success today.  In business, knowledge is power and data is the fuel that creates that power.  Typically, however, 80% of corporate data is unstructured and needs predictive analytic tools to gain insights from it.  And, even during the COVID period, new skills and techniques have become available and organizations dare not pause along their digital data journeys.

Many companies in South Africa have already embarked on the data science journey.  Some are at the beginning while others are well-advanced.  For all these companies, however, the basic principles of successful data management need to be emphasised, and with that comes an assessment of skills that are required.  Experience across the world has shown that any corporation seeking to embark on a data science journey needs to consider several key points.

It is critical to start properly (or re-start properly if you have gone down the wrong track!)

Firstly, identify the specific areas and opportunities in the organisation where Data Science can add value.  Finding the skills and technical resources needed to cope with the technicalities comes next.  Usually, existing internal resources are insufficient, so external resources should be called in if required. 

Company executives need to understand the process.  This can be summarised as follows:

·       Clearly articulate the particular problem statements in the business.

·       Gather, clean, wrangle and prepare the data.

·       Analyse the data.

·       Visualise results in interactive and intuitive ways.

·       Communicate findings to non-technical stakeholders who need to act on the insights.

Building the team comes next.  To deliver value, a team requires a combination of different skills to be effective.  A Data Science team consists of some or all the following: analytics translator, data scientist, data engineer, data analyst, business intelligence developer and software developer.

Once the team is assembled, management needs to firstly pay attention to issues of communication, shared goals, motivation and recognition.  Once there is a competent data team in the business, it needs to fit comfortably into the organization structure.  Ideally, a team should operate as a “Centre of Excellence” within the organization, rather than decentralising it across departments or simply placing it in the IT department. 

For many executives and line managers, though, this whole data science journey is a daunting prospect, especially now.  The challenge they face is how to successfully achieve the intersection between analytics and people that is vital to the integration of data science into a business.

Reskill or upskill?

Pete Kropman, Head of Quant Talent Analytics at Nedbank, talks of senior executives trying to upskill themselves in their spare time in order to understand data science. 

“Because data science is a journey and not a destination, executives need to look at the twin focus areas of upskilling and reskilling.  These may sound similar but they are not.  What has worked at Nedbank, is the introduction of a continuous, planned upskilling and reskilling strategy across the entire company,” he says.

The organisation I lead Explore Data Science Academy was established in 2017 with the initial aim of training young data scientists in a 12-month data science learnership.  This programme continues successfully but, as the need for data science skills and understanding in business has grown, so our focus has broadened to upskilling and reskilling people across all levels of any business – providing input that is appropriate to their respective roles within the business.

Our experience in this area provides a useful template for companies to follow as they upskill and reskill employees of all levels and in various job categories in data science.

Firstly: Upskilling should focus on teaching new skills at all levels.  This includes:

•          Strategic briefings to senior executives. 

•          Hands-on interventions in all areas of management: line managers, production managers, accountants, logistics, marketing and HR personnel.

•          Building a pipeline of external talent to feed into the business, such as our established basic courses.

Secondly: Reskilling is key.  Our experience suggests that everyone within the business needs to grasp the fact that data science learning is a process, not a destination.  Technology is changing all the time and employees need to have a hunger to learn.  But what does this mean?

•          Employees at the front line of data science need to be exposed to new technologies, techniques and opportunities regularly.  This is where a credible external training source, such as ourselves, is so important.

•          This principle also applies across the organization.  Everyone, from senior executives across all levels of management, should understand the importance of upskilling in their areas of responsibility.  Tailor-made courses are available to do this.  The “Boot-Camp” approach offered by Explore is one such medium.

This brief summary only touches the surface of data science skilling and upskilling.  Strategies will vary considerably across different organizations, but the basic principles remain: commit to doing it and get the best advice.

The journey to a mature state of excellence in terms of data management in a company is essentially never ending.  Technologies change all the time; the organisation’s needs change; people change.  The road to maturity is difficult and existing organisational habits and reflexes may have to be discarded to get ahead.

In a world coping with all the changes brought about by COVID, companies need to be more vigilant and agile than ever.  And, perhaps, the constant-learning environment of data science will enable businesses to evolve overall into more competitive, efficient and effective organizations in the new, post-COVID world we long for.

Those wanting to know more about how to upskill or reskill using data science should go to


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