Big data is meaningless without data scientists

Read time 1min 50sec
David Logan, modelling solution scientist at Absa.
David Logan, modelling solution scientist at Absa.

Without the skills and knowledge of professionals who turn data from multiple sources into actionable insights, big data is meaningless.

ITWeb Business Intelligence & Analytics Summit 2019

To be held from 12 to 14 March, at The Forum, Bryanston with the theme "Being intelligent about business data", the summit will focus on the strategy and tactics of data analytics and BI in today's data-driven business. Click here to book your seat and take your BI and analytics projects to the next level.

This is why more and more organisations are looking to employ data scientists, who know how to glean value from the flood of data that is drowning enterprises today.

Five years ago, the data scientist job title was relatively new in SA, says David Logan, modelling solution scientist at Absa, who will be speaking at ITWeb Business Intelligence & Analytics Summit 2019, to he held on 14 and 15 March, at The Forum in Bryanston.

"Since then, awareness and interest around the [data scientist] role has grown exponentially," he adds. "What was true then remains true now: the key deliverable in data science is the ability to extract value from data."

However, while having a data science capability is an opportunity for value creation, it is not a guarantee, warns Logan, adding that the cost of employing data scientists versus the benefit varies tremendously across organisations.

Speaking of what SA's larger enterprises could be doing better when it comes to harnessing their data, Logan cites Thomas Edison, who is attributed with saying genius is 1% inspiration, 99% perspiration.

"Similarly, 80% of a data scientist's time is spent on finding, cleaning and organising data, and only 20% on actual analysis. Because of this, large enterprises need to balance their team's skills according to the nature of the problems they are trying to solve."

Logan will present a case study on how to successfully implement a data science capability. He will also cover what the necessary skill sets for a successful data scientist are, as well as identify the distinct roles and different skills required for each person within the data science team.

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