Climbing the data and analytics maturity curve faster
WHILE the world is racing towards new age technologies such as the internet of things (IoT) and 5G, it’s important to remember the fundamentals: Data and analytics.
If a business hasn’t been able to fully harness data, analytics, and create a culture that seeks and values business intelligence (BI), it’s not going to get very far with IoT or any of the other bleeding-edge technology projects either.
At the end of the day, it’s about these companies using data that they gather via IoT to say feed into artificial intelligence (AI) models that help companies autonomously decide whether warehouse A needs to stock more candies for Christmas or warehouse B, or whether employee A needs to be trained on Python or C++ this quarter.
Unfortunately, not many businesses have gone deep with data and analytics. According to a brand new Gartner survey, more than 87 percent of organizations are classified as having low BI and analytics maturity.
“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,” said Gartner Senior Director Analyst Melody Chien.
According to Chien, organizations with low maturity exhibit specific characteristics that slow down the spread of BI capabilities.
These characteristics include primitive or aging IT infrastructure, a 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.
Gartner believes that data and analytics leaders must follow four steps if they want to quickly evolve their organizations’ capabilities for greater business impact:
# 1 | Develop holistic data and analytics strategies with a clear vision
Organizations with low BI maturity often exhibit a lack of enterprise-wide 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.
# 2 | 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.
# 3 | 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.
# 4 | 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.
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