Four ways to build a strong foundation for self-service analytics

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New research by Gartner has revealed that organisations are rapidly embracing self-service analytics to enable sales and marketing professionals to become more data-driven.

Self-service analytics tools encourage line-of-business professionals to perform queries and generate reports on their own, with nominal IT support.

This enables them to potentially surface vital insights quicker and more effectively than if they had to rely on data specialists.

And the adoption of self-service analytics is so pronounced that Gartner is predicting that by 2019, the analytics output of business users with self-service capabilities will surpass that of professional data scientists.

"The trend of digitalisation is driving demand for analytics across all areas of modern business and government," says 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 non-specialists to perform effective analysis and better inform their decision-making."

In a recent survey of more than 3,000 CIOs conducted by Gartner, it was revealed that analytics and business intelligence are ranked as the top differentiating technologies for organisations. And the 2018 Gartner CIO Agenda Survey suggests that analytics is attracting the most investment of any tech category.  

As part of this growth, data and analytics leaders are increasingly implementing self-service capabilities to support a data-driven culture within their organisations. 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.

But in their rush to develop a more data-driven focus in their organisations, there is a danger that some businesses will be trying to run before they can walk.

Idoine warns: "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."

Focus on four areas

Gartner suggests that the scale of the task of implementing self-service analytics can catch organisations by surprise, especially if adoption is rapid. 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.

In its report "How to Enable Self-Service Analytics and Business Intelligence: Lessons From Gartner Award Finalists", Gartner recommends addressing four areas to build a strong foundation for self-service analytics:

  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," says 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," Idoine notes. "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," says 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. "Daa 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 notes. "A formal onboarding plan will help automate and standardise this process, making it far more scalable as self-service usage spreads throughout the organisation."

About Neil Davey

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Neil Davey is the managing editor of MyCustomer. An experienced business journalist and editor, Neil has worked on a variety of newspapers, magazines and websites over the past 15 years, including Internet Works, CXO magazine and Business Management. He joined Sift Media in 2007.

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01st Feb 2018 08:45

Thank you for sharing your thoughts on the rising importance of analytics for the growth of a business.
Indeed analytics has given a new dimension to customer service. I was going through a blog which I think can add to this.
https://www.callcenterhosting.in/blog/predictive-analysis-services-why-y...

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