Race is on for the data science maestro

A skilled data scientist needs a wide range of skills, advanced qualifications and several years of business experience.

Read time 4min 00sec
Jurgens Hendriks.
Jurgens Hendriks.

Effective data science is becoming the differentiator between business success and failure, but few have the broad range of skills and expertise needed to deliver on the promise of data science.

It is both an art and a science, demanding a range of seemingly disparate skills. Much like a conductor must coordinate and guide a performance to deliver a meaningful final product, the data scientist must apply a range of toolkits and skills to not only interrogate data, but ask meaningful questions that deliver true value, and then ensure the data product drives meaningful business change.

These maestros of data science are in short supply. Both globally and in South Africa, forward-thinking enterprises are looking to harness skilled data scientists to drive more than just competitive edge; they need their insights to help companies reinvent, innovate and disrupt into the future.

Data science transforms data, extracts true business value from it and derives it into a data product. In straightforward terms, a data-driven product is software, a service or platform that is able to solve deeply complex problems and provide actionable insights by utilising internal or external data and a number of different machine learning algorithms.

But finding a skilled data scientist is easier said than done. A true data scientist needs a wide range of skills to deliver on the promise latent within enterprise data: they need advanced qualifications, preferably starting with a Masters or PhD in computer science or quantitative fields.

In addition, they need several years of business experience, preferably at a strategic level. They also need certain personality traits: curiosity, creativity, innovative flair and an entrepreneurial mindset, as well as the ability to negotiate and collaborate with people at all levels of the business.

They must be au fait with prescriptive analytics, predictive analytics, descriptive statistics, pattern recognition, data visualisation, data preparation, hypothesis testing, time series models and Bayesian models, and need skills in the use of a range of computing tools.

Data science transforms data, extracts true business value from it and derives it into a data product.

Without this combination of skills and attributes, data scientists cannot deliver on what business needs them to do: pose the right questions, find the right answers, and use the findings to effect measurable change in the business.

In South Africa, there may be only tens or hundreds of people equipped with all the skills and experience needed to deliver on the promise of data science, yet all companies across all verticals need data scientists now, if they are to remain relevant and competitive into the future.

There is almost no corporate sphere that cannot benefit from data science and industries such as cyber security, healthcare, advertising, marketing, retail, banking and insurance are booming with job opportunities.

Getting to grips with data science

Since the profession of data science is relatively new and the role is still evolving, there are misconceptions around exactly what data science entails, and where it fits in to the organisation.

In a nutshell, data science is the art of finding business solutions in vast pools of data, and applying these solutions to reinvent, innovate and disrupt. Pioneers in the field are already harnessing data science to reach new markets, reinvent entire industry models and challenge the status quo.

But many traditional large corporates might not yet have official data science capabilities in place. This typically means that if a data scientist is in place, they will either report directly into business or will fall into a BI/MI space.

It should be noted that if data science is relegated to the 'IT back-office', it cannot deliver on its potential to revolutionise the business. Data science must be empowered to apply data products to effect change in the business, which means it must have a voice in the C-suite or the boardroom.

In many other enterprises, there are no in-house data science resources. Even with a commitment in place to upskill internal resources, equipping the right candidates with the right levels of skills and experience can take years to achieve. In a highly competitive market, there is no time to waste.

For most, the most viable solution to the scarcity of this expertise is to partner with data science specialists who can deliver immediately on the needs of the enterprise.

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