"The trend of digitalisation 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 organisations. 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 organisation. This means that business users can more easily learn to use and benefit from effective analytics and BI tools, driving favourable 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 organisations. 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 organisations by surprise, especially if they are successful. In large organisations, 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 organisational and process changes before starting the initiative.
Gartner recommends addressing four areas to build a strong foundation for self-service analytics and BI:
1.Align self-service initiatives with organisational 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 organisation," 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."
2.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," 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."
3.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," said Ms 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.
4.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," said Ms Idoine. "A formal onboarding plan will help automate and standardise this process, making it far more scalable as self-service usage spreads throughout the organisation."