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March 5, 2018

2018 will be the year of self service data science for the enterprise

It’s time for self-service data science for the enterprise, empowering workers to make faster and better, data-driven decisions off their own data, with their own knowledge.

By James Nunns

The search for the most effective ways to deal with data has long been a principal concern of the business world. Organisations are data gathering and data generating machines.

Nick Jewell, Technology Evangelist at Alteryx

Enterprises have more access to data every year, and increasingly want to use the insights quickly. After all, good data analytics yields insight, informs strategy and impacts the bottom line. It’s good business sense to apply better fit practices as fast as possible.

The Internet of Things is the biggest driver triggering this massive influx of data. The number of devices and sensors used within business is expected to grow 45% this year alone. In 2020, cross industry devices will reach 4.4 billion units, according to Gartner. As such, there will be even more data for analysts and scientists to manipulate and convert into business actions. Data simply grows and grows.

However, research suggests that the number of trained analysts, equipped to manipulate this effectively data are dwindling in number. Aware of this, organisations of all sorts, even up to the size of KPMG, are now creating courses to turn PHDs in data scientists, to try and plug this talent gap. In addition, although keeping data insights centralised and within the realm of specialised data scientists has been promoted as a way to improve consistency, it means a significant delay between questions and answer. It also divorces the people that ‘know’ from the people that ‘ask’ about the data. That means those with the knowledge must go to those with the skills and the tools to ask questions about their own data and their own domain. That doesn’t make great sense anymore, given the strides made in the accessibility of data, and the solutions that allow anyone to investigate it – themselves.

It’s time for self-service data science for the enterprise, empowering workers to make faster and better, data-driven decisions off their own data, with their own knowledge.


Embracing a data culture to empower your people

As Jeff Immelt, CEO of General Electric once noted, ‘If you woke up as an industrial company today, you will wake up as a software and analytics company tomorrow.’ As such, all business is analytics driven and all business users are analytics users. Although IT are best equipped to handle data, in 2018, there is no need for IT specifically to own all of it.

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Self-service analytics platforms exist that can help more people in the enterprise foster big data analytics innovations without having to rely on the hard-to-find skillsets of highly qualified data scientists. As more businesses endeavour to compete with vast volumes of data, it will become critical that more people within the enterprise can understand the tools available and get insight quickly from them.

This will be achieved by spreading a data culture throughout the organisation, making it much easier for people who make the business decisions to be armed quickly to dig deep into data. It will take a cultural shift across the company and can be achieved by training the right people, giving them the tools they need to become analysts of their own data – and leading by example. The zeitgeist of this trend is revolutionising business through data science and data analytics – even if those terms are not used by the line of business people becoming analysts themselves!


Enter the citizen data scientist – AKA the everyday analyst

According to Gartner, in their Magic Quadrant for Business Intelligence and Analytics Platforms, the number of ‘citizen data scientists’ will grow five times faster than the number of data scientists. Citizen data scientists (a technical term that could be described in more friendly terms as an everyday or everyman analyst) are the data analysts of tomorrow. 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.’ In plainer terms, it’s people who use data properly to do their jobs better.

With more and more citizen data scientists and increasingly efficient data analytics tools to match, 2018 will be the year of self-service data analytics for the enterprise. They will be armed with technologies that allow them to add and blend data, then extract insight from data sets without needing a background in coding or certified skill-sets. Given the consumerisation of so many technologies, it’s no surprise that data science is coming out of the IT department and into the hands of business people.

It also makes sense to help turn knowledge workers into business analysts rather than bring in a specialist, often at considerable cost. Those who already work in the business, owning their own data every day understand their market better, can convert into business actions from data insights quicker than an outsourced data expert.

Advanced data analytics are now available to far more people. The rise of the citizen data scientist is made possible only by the data analytics platforms that exist today.

There is a lot to be gained from a culture of data and analytics, with people across the organisation armed with advanced analytics that fit business objectives. By empowering more people to be effective users of data by embracing a data culture throughout, you will shorten the time it takes for data insights to be translated in effective business decisions.

It’s clear that there are better ways of dealing with information that can lift a business from a second-rate data user to a first place information leader. Perhaps the key question for 2018 is, how fast can we make it happen? The closing words can belong to Gartner, who believes that “Self-Service Analytics and BI Users Will Produce More Analysis Than Data Scientists Will by 2019

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