As organisations fast-track digital transformation initiatives in the aftermath of the pandemic, access to quality, timeous data has never been more critical. Better client insights, and improved products and services are reliant on information to provide input to strategic direction and ultimately achieve higher revenue.

This is where self-service analytics becomes a crucial business enabler, says Andreas Bartsch, head of innovation and services at PBT Group.

Gartner defines self-service analytics as a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support.

“Self-service analytics is certainly not a new concept. Many organisations have attempted to establish such a competency in-house, however unfortunately with varying degrees of success. In some cases, the adoption has been minimal, resulting in complete failure. Considering the significant investments made to achieve this, executives have rightly questioned whether this is worth the investment,” says Bartsch.


Business relevance

According to Bartsch, one of the key performance indicators for the success of any modern data platform and its associated analytic capabilities lies in its utilisation by business users.

“Such a platform is not for IT to pride themselves on the underlying cloud technology, the sheer volume of data, or the cool functionality of the dashboards. The business data consumer must be the judge of its value. For organisations to establish a data-driven culture, trustworthy data must be accessible at an operational level. This will not only aid line managers in their daily decision-making but will greatly contribute to improved productivity and efficiency,” he says.

This means that the business data consumers should not await an IT response to their query, but rather should have the capability at their proverbial ‘fingertips.’ Successful self-service data and analytics will result in organisational speed and agility, and a definite awareness of the importance of quality data.


Defining success

Bartsch believes that the successful establishment and adoption of a self-service data and analytics capability and competency relies on a well-architected modern data platform, best practice data engineering, and appropriate data governance principles. The data model must reflect integrated data in a simplified manner, accessible via an intuitive, user-friendly visualisation tool that supports self-service functionality.

“However, as is the case with most strategic initiatives, the importance of organisational change management cannot be over-emphasised. Typically, this entails communication, education, and the necessary training. Being transparent with the objectives, benefits, and expectations will also provide credibility to the platform and give it the necessary business support.”

For its part, training does not only apply to the visualisation tool or self-service portal, but also in providing an understanding of the data product life cycle, the data model, and its data elements.

“The establishment of a self-service data and analytics capability is a non-negotiable investment for any organisation embarking on a data journey. The investment should never be questioned. Instead, the spotlight must turn to how it is being implemented,” he concludes.