As per a survey conducted by Gartner, over 87 percent of organizations are classified as having low business intelligence (BI) and analytics maturity. This creates a big obstacle for organizations wanting to increase the value of their data assets and exploit emerging analytics technologies such as machine learning.
Organizations with low maturity fall into ‘basic’ or ‘opportunistic’ levels on Gartner’s IT score for data and analytics. 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.
Melody Chien, Senior Director Analyst at Gartner, says, “Low BI maturity severely constrains analytics leaders who are attempting to modernize BI. 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.”
He further says that 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.”
Develop holistic data and analytics strategies
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.
Create a flexible organizational structure
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.
Create integrated analytics platforms
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.