World slow to advance data & analytics use - but APAC's on top

Gartner has released the findings from a global survey of 196 organisations.

The study delved into how organisations are utilising data and analytics (D-A), with a whopping 91 percent having not yet reached a 'transformational' level of maturity in D-A, despite this area being a number one investment priority for CIOs in recent years.

"Most organisations should be doing better with data and analytics, given the potential benefits," says Gartner research vice president Nick Heudecker.

"Organisations at transformational levels of maturity enjoy increased agility, better integration with partners and suppliers, and easier use of advanced predictive and prescriptive forms of analytics. This all translates to competitive advantage and differentiation."

Part of the survey involved respondents ranking their organisations according to Gartner's five levels of maturity for D-A – 60 percent rated themselves in the lowest three levels.

Overview of the Maturity Model for Data and Analytics

Asia Pacific came out on top in terms of D-A maturity, with 48 percent of organisations reporting their D-A maturity to be in the top two levels – in comparison with 44 percent in North America and just 30 percent Europe, the Middle East, and Africa (EMEA).

The majority of respondents worldwide assessed themselves at level three (34 percent) or level four (31 percent). 21 percent of respondents were at level two, and five percent at the basic level, level one. Only nine percent of organisations surveyed reported themselves at the highest level, level five, where the biggest transformational benefits lie.

"Don't assume that acquiring new technology is essential to reach transformational levels of maturity in data and analytics," says Heudecker.

"First, focus on improving how people and processes are coordinated inside the organisation, and then look at how you enhance your practices with external partners."

By far and away the most common business problem that organisations sought to solve with D-A is improving process efficiency, with 54 percent marking it in their top three problems.

Enhancing customer experience and development of new products were the joint second most common uses, with 31 percent of respondents listing each issue.

And despite a lot of attention around advanced forms of analytics, the sheer majority (64 percent) of organisations consider enterprise reporting and dashboards their most business-critical applications for data and analytics.

In the same manner, traditional data sources such as transactional data and logs also continue to dominate, although 46 percent of organisations now report using external data.

"It's easy to get carried away with new technologies such as machine learning and artificial intelligence," says Heudecker.

"But traditional forms of analytics and business intelligence remain a crucial part of how organisations are run today, and this is unlikely to change in the near future."

In terms of reasons for organisations not adopting or increasing their use of data and analytics, there were many with no clear reason. Gartner asserts organisations "tend to experience a different set of issues depending on their geography and current level of maturity.

Despite this, the survey was able to determine the three most common barriers, which were defining analytics strategy, determining how to get value from projects, and solving risk and governance issues.

"These barriers are consistent with what Gartner hears from client organisations who are at maturity levels two and three," says Gartner research vice president Jim Hare.

"As organisational maturity improves to enterprise level and beyond, organisational and funding issues tend to rise."

When it comes to infrastructure, on-premises deployments hold the fort globally, ranging from 43 percent to 51 percent depending on the use case. Pure public cloud deployments range from 21 to 25 percent of deployments, while hybrid environments make up between 26 and 32 percent.

"Where the analytics workloads run is based a lot on where the data is generated and stored. Today, most public cloud workloads are new and we won't see the percentage of cloud use rise until legacy workloads migrate en masse," says Hare.

"This scenario will happen eventually, but given the extent to which modern data and analytics efforts overwhelmingly use traditional data types stored on-premise, this shift will likely take several years to complete."