Self-service analytics and BI outpacing the output of data scientists

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The adoption of self-service analytics in many industries and by government agencies is on such a brisk pace that by 2019 the analytics output of business users with self-service capabilities will surpass that of formal data scientists.

That is the prediction of Gartner, Inc., which surveyed more than 3,000 chief information officers who ranked analytics and BI as the top differentiating technology for their organizations. It attracts the most new investment and is also considered the most strategic technology area by top-performing CIOs, the survey found.

"The trend of digitalization is driving demand for analytics across all areas of modern business and government," said Carlie J. Idoine, research director at Gartner. "Rapid advancements in artificial intelligence, the Internet of Things and SaaS analytics and analytics and BI platforms are making it easier and more cost-effective than ever before for non-specialists to perform effective analysis and better inform their decision making."

The result: data and analytics leaders are increasingly implementing self-service capabilities in order to create a “data driven culture” throughout their organization, Idoine said. This means that business users can more easily learn to use and benefit from effective analytics and BI tools, driving favorable business outcomes in the process. But it’s not quite as simple as it sounds.

"If data and analytics leaders simply provide access to data and tools alone, self-service initiatives often don't work out well," Idoine cautioned. "This is because the experience and skills of business users vary widely within individual organizations. Therefore, training, support and onboarding processes are needed to help most self-service users produce meaningful output."

Gartner recommends that organizations address four key areas to build a strong foundation for self-service analytics and BI:

Align self-service initiatives with organizational goals and capture anecdotes about measurable, successful use cases

"It's important to confirm the value of a self-service approach to analytics and BI by communicating its impact and linking successes directly to good outcomes for the organizations,” Idoine said. "This builds confidence in the approach and justifies continued support for it. It also encourages more business users to get involved and apply best practice to their own areas."

Involve business users with designing, developing and supporting self-service

"Creating and executing a successful self-service initiative means forging and preserving trust between the IT team and business users," Idoine said. "There's no technical solution to build trust, but a formal process of collaboration from the start of a self-service initiative will go a long way to helping IT and business users understand what each party needs from the other to make self-service a success."

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Take a flexible, light approach to data governance

"The success of a self-service initiative will depend hugely on whether the data and analytics governance model is flexible enough to enable and support the free-form analytics explorations of self-service users," Idoine said. “Strict, inflexible frameworks will deter casual users. On the other hand, a lack of proper governance will overwhelm users with irrelevant data, or create serious risks of a breach of regulation. "IT leaders must find the right balance of governance to making self-service successful and scalable."

Equip business users for self-service analytics success by developing an onboarding plan

"Data and analytics leaders must support enthusiastic business self-service users with the right guidance on how to get up and running quickly, as well as how to apply their new tools to their specific business problems," Idoine said. "A formal onboarding plan will help automate and standardize this process, making it far more scalable as self-service usage spreads throughout the organization."

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