A worldwide survey of 196 organisations by Gartner, Inc. showed that 91% of organisations have not yet reached a ‘transformational’ level of maturity in data and analytics, 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,” said Nick Heudecker, research vice president at Gartner.
“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.”
The global survey asked respondents to rate their organisations according to Gartner’s five levels of maturity for data and analytics (basic, opportunistic, systematic, differentiating and transformational). It found that 60% of respondents worldwide rated themselves in the lowest three levels.
The survey revealed that 48% of organisations in Asia Pacific (APAC) reported their data and analytics maturity to be in the top two levels. This compares to 44% in North America and just 30% in Europe, the Middle East and Africa (EMEA).
The majority of respondents worldwide assessed themselves at level three (34%) or level four (31%). Twenty-one percent of respondents were at level two, and 5% at the basic level, level one. Just 9% 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,” said Mr. Heudecker. “First, focus on improving how people and processes are co-ordinated inside the organisation and then look at how you enhance your practices with external partners.”
Improving process efficiency was by far the most common business problem that organisations sought to address with data and analytics, with 54% of respondents worldwide marking it in their top three problems. Enhancing customer experience and development of new products were the joint second most common uses, with 31% of respondents listing each issue.
The survey also revealed that, despite a lot of attention around advanced forms of analytics, 64% of organisations still 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% of organisations now report using external data.
“It’s easy to get carried away with new technologies such as machine learning and artificial intelligence,” said Mr. 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.”
Organisations reported a broad range of barriers that prevent them from increasing their use of data and analytics. There isn’t one clear reason; organisations tend to experience a different set of issues depending on their geography and current level of maturity. However, the survey identified the three most common barriers as: defining data and 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,” said Jim Hare, research vice president at Gartner. “As organisational maturity improves to enterprise level and beyond, organisational and funding issues tend to rise.”
In terms of infrastructure, on-premises deployments still dominate globally, ranging from 43 to 51% of deployments depending on use case. Pure public cloud deployments range from 21 to 25% of deployments, while hybrid environments make up between 26 and 32%.
“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,” said Mr. 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.”
