For all the hype surrounding advanced analytics, machine learning and other data innovations, a remarkable number of organizations are still using spreadsheets for analysis and arguing about data quality.
In a recent Gartner survey, 87.5% of respondents had low data and analytics maturity, falling into “basic” or “opportunistic” categories.
A good data and analytics strategy starts with a clear vision
Organizations at the basic level have business intelligence (BI) capabilities that are largely spreadsheet-based analyses and personal data extracts. Those in the opportunistic category have individual business units that pursue their own data and analytics initiatives as stand-alone projects, but there is no common structure across them.
Low maturity can be the result of limited budgets, lack of vision and skills, inexperience in strategic planning and deployment, or primitive or aging infrastructure.
Organizations in the early stages of data and analytics maturity often do not have the ability to exploit advanced analytics. They struggle to deal with poor data quality, inconsistent processes and poor coordination across the enterprise.
If your organization wants to maximize the value of its data assets, you may need to improve maturity levels first, says Melody Chien, Senior Director Analyst, Gartner.
“Low maturity severely constrains leaders who are attempting to modernize BI,” Chien says. “But organizations with low BI maturity can learn from the success of more mature organizations to speed up modern BI deployment and take their data and analytics capabilities to the next level.”
According to Chien, you can evolve your organization’s capabilities for greater business impact by taking simple steps in the areas of strategy, people, governance and technology.
- Strategy. A good data and analytics strategy starts with a clear vision. In this context, vision can be defined as the business value that data and analytics can bring. Coordinate with IT and business leaders to develop a holistic BI strategy. Then, create a short-term roadmap with achievable goals, clear milestones, performance measurements and monitoring.
- People. Anticipate upcoming needs and ensure that the proper skills, roles and structures exist, are developed or can be sourced to support the work identified in the strategy. If you have limited analytics capabilities in-house, strive for a flexible working model by building virtual BI teams that include business-unit leaders and users.
- Governance. Most organizations with low BI maturity don’t have a formal data governance program in place. Consider governance as the “rules of the game.” Those rules enable the organization to balance opportunities and risks in the digital environment. Start by creating an inventory of your information assets, where they are located and who uses them. Then, establish an agreed-to framework for working with the data.
- Technology. Low-maturity organizations often have BI platforms that are more traditional and reporting-centric, embedded in ERP systems or are simple disparate reporting tools that support limited uses. To improve analytics maturity, create integrated analytics platforms that extend your current infrastructure to include modern analytics technologies. Organizations with limited BI resources and a scarcity of analytics talent should consider packaged applications that best fit requirements and company culture for a quick start.