Self Service Analytics and BI users will produce more analysis than Data Scientists, said research firm Gartner. Organizations are embracing self-service analytics and business intelligence (BI) to bring these capabilities to business users of all levels. This trend is so pronounced that Gartner, Inc. predicts that by 2019, the analytics output of business users with self-service capabilities will surpass that of professional data scientists.
“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, Internet of Things and SaaS (cloud) analytics and BI platforms are making it easier and more cost-effective than ever before for nonspecialists to perform effective analysis and better inform their decision making.”
Gartner’s recent survey of more than 3,000 CIOs shows that CIOs 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.
As a result, data and analytics leaders are increasingly implementing self-service capabilities to create a data-driven culture throughout their organization. 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.
“If data and analytics leaders simply provide access to data and tools alone, self-service initiatives often don’t work out well,” said Ms. Idoine. “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.”
The scale of the task of implementing self-service analytics and BI can catch organizations by surprise, especially if they are successful. In large organizations, popular self-service initiatives can very rapidly expand to encompass hundreds or thousands of users. To avoid a descent into chaos, it’s crucial to identify the right organizational and process changes before starting the initiative.
“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 organization,” said Ms. Idoine. “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.”
“Creating and executing a successful self-service initiative means forging and preserving trust between the IT team and business users,” said Ms. Idoine. “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.”
“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,” said Idoine. 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,” she added.
“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,” said Ms. Idoine. “A formal onboarding plan will help automate and standardize this process, making it far more scalable as self-service usage spreads throughout the organization.”