More analysis than data scientists

For the past few years, data scientists are highly sought after to analyse data that can help organisations better understand their business, customers and trends. But, it looks like artificial intelligence-based solutions may be taking over that role in the near future.

According to Gartner, the analytics output of business users with self-service capabilities will surpass that of professional data scientists in 2019.

“The trend of digitalisation is driving demand for analytics across all areas of modern business and government. Rapid advancements in AI, Internet of Things and SaaS (cloud) analytics and business intelligence (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,” said Carlie J Idoine, Research Director of Gartner.

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 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,. 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,” said Iodine.

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: 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.
  2. Involve business users with self-service: Collaborating 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: Find the right balance of governance to make self-service successful and scalable.
  4. Develop an onboarding plan: A formal onboarding plan will help automate and standardise this process, making it far more scalable as self-service usage spreads throughout the organisation.