This year marks an important milestone — the 20th anniversary of the data scientist. Thanks to the advent of data science, we are capable of doing things today that would have been absolutely impossible two decades ago: from advanced algorithms that allow consumers to open a bank account or apply for a loan in seconds, to enabling global businesses to exist that are completely digital, to empowering marketers to dynamically target individuals with personalized adverts rather than broad demographics.
The data scientists that have been key to these innovations are now among the most in-demand professionals; the role has even been labeled as the “sexiest job of the 21st century”. Without the analytic talent and know-how of the data scientist, companies like Amazon, Netflix or Gymshark would not exist - nor would they be able to focus on creating products and services that perfectly meet customer needs.
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A look back: The early stages of data analytics
When the data scientist specialism surfaced back in 2001, the biggest challenge was how to leverage insights from relatively limited data sources. Initially, there was no specific training to prepare people for the daily tasks of a data scientist, so mathematicians, statisticians and physicists were often hired to try and extract insights from existing data.
Through the 2000s, the daily routine of a data scientist consisted of dealing with several different tools and products for data preparation, reporting, cataloging, visualization and modeling to try and unlock the potential business value of data. However, this process was time-consuming, as data was stored in different departments within segregated silos, and it required an exceptionally high level of technological expertise to generate any real added value from these complex applications.
Over time, companies started to realize the business-altering potential of advanced data science. From retailers using Machine Learning to analyze customer behavior and sentiment and gain a competitive advantage, to supply chains adopting AI-powered dynamic demand forecasting for more effective inventory management, industry after industry is recognizing the need to harness and analyze data in order to produce powerful, transformational insights.
As a result, data science is in high demand today. Unfortunately, there remains a distinct lack of data scientists across the globe. Research by QuantHub showed an alarming data scientist shortage of 250,000 in 2020. This shortage is contributing to a divide between those businesses which can optimize analytics, automate processes and leverage data to make better decisions, and those that cannot.
Thankfully, with the development of code-free, code-friendly technology and fully automated platforms, the barriers to unlocking the power of data science and advanced analytics are dropping rapidly. Gradually, we are moving beyond having just a few specialists looking at data science and expanding the opportunity to the many. In the future, it will be possible for any employee, given the right technology, to be able to answer complex data questions, innovate, and discover major breakthroughs using their company’s data; by opening up the power of data science and analytics automation, ordinary workers will be able to deliver business-changing insights.
Does this mean that today’s data scientists won’t have a role in business in the future? Not at all. They will be key not only to training this next generation of citizen data scientists, but also to leading these teams in focusing on complex strategy and direction. The democratization of data science means that existing experts can focus on high-value tasks instead of getting bogged down in data preparation work and day-to-day demands.
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A bright future, but what hurdles must be overcome?
The future that has been outlined is nothing if not eminently achievable. While many organizations are already shifting to this new way of working, there are a number of hurdles that must become for other organizations to join these early adopters. The first is the general lack of data skills in the labor market. Although not every worker needs to become a data scientist, most will require a basic level of data literacy to thrive in increasingly “data-rich" environments. Unfortunately, many workers lack these essential skills.
Then there is the misconception that knowledge workers should not be equipped with data science tools and capabilities. Some IT and data science teams earnestly believe that providing these tools to workers will somehow lead to damage and chaos within the business. But this is a myth. It’s the equivalent of believing that accountants should stick to using the abacus because they could make terrible mistakes if they use a calculator. Data science technology will lead to fewer mistakes, not more.
The third hurdle is a hesitancy among business leaders to invest in upskilling their workforce, perhaps because they are concerned that staff may leave for a competitor once they have been trained. However, the winners and losers in an increasingly data-centric business environment will be defined by those who are willing – or not - to train their workforce to leverage data analytics.
This is not about teaching advanced maths or computer programming, but showing workers how to view their questions as solvable problems and to look at business challenges with an analytical mindset.
By investing in non-technical tools such as self-service platforms that every employee - from the marketing department to production - can easily use, workers are given the space to experiment and learn new data skills at their own pace. These platforms can support workers as they discover how to automate analytic processes to unlock powerful insights from data, creating a strong skills foundation for the future.
Baking data into business culture
Upskilling the workforce is not the only thing that businesses must do to ensure a future where anyone can harness the power of data science.
The next steps to taking full advantage of modern data science is to develop a culture of analytics – one in which employees are encouraged to both ask the right questions and empowered to use analytics to generate value. In this analytics-optimized environment, business analysts and data scientists will need to be able to speak together in the same terms to drive positive business outcomes.
In the future, workforces will be made up not simply of employees, but of citizen data scientists able to leverage powerful automated analytics. And while today’s schoolchildren learn coding and app development, children in 2041 could be learning how to design their own artificial intelligence and machine learning algorithms, underpinned by strong data analysis skills. Here’s to the 40th anniversary of the data scientist, and beyond.
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Richard Timperlake, SVP, EMEA, Alteryx