Data Science has been a buzz word in the market for quite some time, it has even been labelled the “Sexiest Career of the 21st Century”.
That quote alone has cropped up many times in presentations, articles and undoubtedly many internet searches. So, where did this “new” career suddenly come from?
The term Data Science is not actually as new as we think it is, the discipline of Data Science has already been around for over thirty years. In 1997, C.F. Jeff Wu gave the inaugural lecture entitled “Statistics = Data Science?” for his appointment to the H. C. Carver Professorship at the University of Michigan. In this lecture, he characterized statistical work as a trilogy of data collection, data modelling and analysis, and decision-making. In his conclusion, he initiated the modern, non-computer science, usage of the term “data science” and advocated that statistics be renamed data science and statisticians, data scientists.
Fast forward a decade or two and Data Science started to take on an entirely new meaning. In a 2010 TDWI blog post, Wayne Eckerson was the first to define the “purple person”—someone with the mix of business and technology skills that is present in many successful business intelligence and analytics people. Wilson used the “purple people” analogy to describe a particular problem:
“The business people, the actuaries, know what data they need and can define requirements, but typically don’t have the skill set to design a data architecture that gives them the data they need. Technology people typically don’t understand the business requirements, but they can design the data architectures. It’s like the people in IT speak blue, the people in business speak red, but we need people who speak purple in order to create an appropriate solution.” 
This definition has evolved into what we view as Data Scientist today. Data Scientists are not merely statisticians or actuaries, that is only one aspect of what they do, in fact they need to understand technology, possess expertise in coding, and show insight into data and help drive business strategies forward using data.
So what does a Data Scientist actually do?
According to the University of Wisconsin, “a data scientist’s job is to analyze data for actionable insights”, sounds straightforward enough but this is no small task.
The University of Wisconsin goes on to list some of the tasks a Data Scientist is likely to perform in their day-to-day duties:
- Identifying the data-analytics problems that offer the greatest opportunities to the organization
- Determining the correct data sets and variables
- Collecting large sets of structured and unstructured data from disparate sources
- Cleaning and validating the data to ensure accuracy, completeness, and uniformity
- Devising and applying models and algorithms to mine the stores of big data
- Analyzing the data to identify patterns and trends
- Interpreting the data to discover solutions and opportunities
- Communicating findings to stakeholders using visualization and other means
In the book, Doing Data Science, the authors describe the data scientist’s duties this way:
“More generally, a data scientist is someone who knows how to extract meaning from and interpret data, which requires both tools and methods from statistics and machine learning, as well as being human. She spends a lot of time in the process of collecting, cleaning, and munging data, because data is never clean. This process requires persistence, statistics, and software engineering skills—skills that are also necessary for understanding biases in the data, and for debugging logging output from code.
Once she gets the data into shape, a crucial part is exploratory data analysis, which combines visualization and data sense. She’ll find patterns, build models, and algorithms—some with the intention of understanding product usage and the overall health of the product, and others to serve as prototypes that ultimately get baked back into the product. She may design experiments, and she is a critical part of data-driven decision making. She’ll communicate with team members, engineers, and leadership in clear language and with data visualizations so that even if her colleagues are not immersed in the data themselves, they will understand the implications.” 
Why is a Data Scientist important to an organisation?
James Parsons, chief executive of digital workforce and consultancy Arrows Group, says: “Data scientists are the rocket scientists of the digital world and the role of the chief data scientist (CDS) is emerging as the influence of data spreads horizontally across business functions.” 
We now know where Data Scientists come from and what they do but what will they be doing in the future? Technology and data driven strategies are changing at such a rapid rate with topics like robotics and artificial intelligence becoming the latest buzz words. What does this mean for the future of the Data Scientist? Is this hot “new” career already on the decline?
 Gartner Inc. says “More than 40 percent of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists.” Gartner defines a citizen data scientist as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics
Gartner also predicts that citizen data scientists will surpass data scientists in the amount of advanced analysis produced by 2019. A vast amount of analysis produced by citizen data scientists will feed and impact the business, creating a more pervasive analytics-driven environment, while at the same time supporting the data scientists who can shift their focus to more complex analysis.
“Most organizations don’t have enough data scientists consistently available throughout the business, but they do have plenty of skilled information analysts that could become citizen data scientists,” said Joao Tapadinhas, research director at Gartner. “Equipped with the proper tools, they can perform intricate diagnostic analysis and create models that leverage predictive or prescriptive analytics. This enables them to go beyond the analytics reach of regular business users into analytics processes with greater depth and breadth. Access to data science is currently uneven, due to lack of resources and complexity — not all organizations will be able leverage it, for some organizations, citizen data science will therefore be a simpler and quicker solution — their best path to advanced analytics.”
Data Science is set to evolve again, the next evolution of the “Purple Person” will bring even more depth and insight to organisations and perhaps also make this skillset more obtainable both for individuals and organisations.
To realise data’s full potential, Deloitte Data Analytics is demystifying Data Science through impactful initiatives. In February 2018, the Deloitte School of Analytics will host its three-day annual education event, where they breakdown this data phenomenon and help clients use data science to drive their organisation forward. The annual education event is a three-day training programme that focuses on enhancing Analytics capabilities across Africa. Deloitte Analytics SMEs and TDWI lecturers will be engaging with the delegates through hands on and classroom based training, structured case study discussions, presentations and practical examples. The education journey will include modules such as Practical Application of Machine Learning and Artificial Intelligence, Data Architecting for the Future and Demystifying Data Science.
Given the large demand for data analytics skills, experts place the talent pool supply at 20% of the actual market demand. The majority of organisations find themselves at low levels of analytic maturity and need to move from information and hindsight to optimisation and foresight to remain relevant and competitive in the future.
Deloitte recruits top graduates to complete structured training with practical examples through an intensive four-week Boot Camp (two weeks twice a year). 80% of the programme involves student Secondment to clients whilst 20% focuses on skills development as well as mentorship at Deloitte.
The Deloitte Data Factory is structured around rapid skills development offering graduates a structured programme that fast-tracks their ability to deliver on data related projects, allowing them to add significant value to clients within a short time frame.
For more information:
- Deloitte School of Analytics education event – http://www.deloitteschoolofanalytics.co.za/
- Deloitte Data Factory – https://www2.deloitte.com/za/en/pages/risk/articles/deloitte-data-factory.html
 Source: O’Neil, C., and Schutt, R. Doing Data Science. First edition