Most organizations have limited BI and analytics maturity
The vast majority of organizations still have low business intelligence and analytics maturity, which is creating an obstacle for firms wanting to increase the value of their data assets and exploit emerging analytics technologies such as machine learning.
That is the finding of a new study by research firm Gartner, in its IT Score for Data and Analytics. Organizations with low maturity fall into “basic” or “opportunistic” levels, Gartner explains. Organizations at the basic level have BI capabilities that are largely spreadsheet-based analyses and personal data extracts. Those at the opportunistic level find that individual business units pursue their own data and analytics initiatives as stand-alone projects, lacking leadership and central guidance.
“Low BI maturity severely constrains analytics leaders who are attempting to modernize BI,” says Melody Chien, senior director analyst at Gartner. “It also negatively affects every part of the analytics workflow. As a result, analytics leaders can struggle to accelerate and expand the use of modern BI capabilities and new technologies.”
According to Chien, organizations with low maturity exhibit specific characteristics that slow down the spread of BI capabilities. These include primitive or aging IT infrastructure; limited collaboration between IT and business users; data rarely linked to a clearly improved business outcome; BI functionality mainly based on reporting; and bottlenecks caused by the central IT team handling content authoring and data model preparation.
“Low maturity organizations can learn from the success of more mature organizations,” said Chien. “Without reinventing the wheel and making the same mistakes, analytics leaders in low BI maturity organizations can make the most of their current resources to speed up modern BI deployment and start the journey toward higher maturity.”
Gartner said there are four steps that data and analytics leaders can follow in the areas of strategy, people, governance and technology, to evolve their organizations’ capabilities for greater business impact.
Develop holistic data and analytics strategies with a clear vision
Organizations with low BI maturity often exhibit a lack of enterprisewide data and analytics strategies with clear vision. Business units undertake data or analytics projects individually, which results in data silos and inconsistent processes.
Data and analytics leaders should coordinate with IT and business leaders to develop a holistic BI strategy. They should also view the strategy as a continuous and dynamic process, so that any future business or environmental changes can be taken into account.
Create a flexible organizational structure, exploit analytics resources and implement ongoing analytics training
Enterprises must have people, skills and key structures in place to foster and secure skills and develop capabilities. They must anticipate upcoming needs and ensure the proper skills, roles and organizations exist, are developed, or can be sourced to support the work identified in the data and analytics strategy.
With limited analytics capabilities in-house, data and analytics leaders should strive for a flexible working model by building “virtual BI teams” that include business unit leaders and users.
Implement a data governance program
Most organizations with low BI maturity do not have a formal data governance program in place. They may have thought about it and understand the importance of it, but do not know where to start.
Analytics leaders can consider governance as the “rules of the game.” Those rules can support business objectives and also enable the organization to balance out the opportunities and risks in the digital environment. Governance is also a framework that describes the decision rights and authority models that must be imposed on data and analytics.
Create integrated analytics platforms that can support a broad range of uses
Low-maturity organizations often have primitive IT infrastructures. Their BI platforms are more traditional and reporting-centric, embedded in ERP systems, or simple disparate reporting tools that support limited uses.
To improve their analytics maturity, data and analytics leaders should consider integrated analytics platforms that extend their current infrastructure to include modern analytics technologies.