A major step toward becoming a data-driven organization is sizing up the gap between where your company currently stands in regard to data usage and where it wants to be — then determining the specific actions your business must take to get there, in what order, when and how.
Data maturity is a major goal for enterprises looking to reap the competitive benefits of data-driven decision making, but it’s not something that can happen overnight — nor does it happen automatically by way of deploying certain tools or evangelizing the idea of data. Those are important components, but true data maturity requires sustained and targeted efforts across a range of categories.
Here’s more on what data maturity means and how companies can keep working toward it.
What Does Low Data Maturity Look Like?
According to research from Gartner, 87 percent of organizations have low maturity in the realm of data analytics and business intelligence (BI). Some of the hallmarks of low maturity per Gartner include:
- Outdated IT and analytics infrastructure
- Lack of cooperation between IT teams and “regular” users
- Difficulty linking data to the improvement of business outcomes
- BI that revolves mostly around reporting
- Bottlenecks caused by data silos and lack of democratization
Defining a Model for Data Maturity
All data maturity models aim to do the same thing, even if the exact terminology varies. The key is clearly outlining different levels of data usage based on specific criteria.
One strategist recommends this approach to data maturity modeling: Break down maturity into five levels, then fill in criteria for each level based on four areas — data organization, data architecture, data technology and data processes. An issue like how employees are able to get their hands on data insights would fall under “processes” on the rubric.
Level one would likely be an org using mostly spreadsheets. The second level would describe the IT department building reports based on siloed data warehouses. At the third level we see employees gaining access to insight-laden dashboards. Reaching levels four and five require the addition of machine learning and automated insight detection to advanced ad hoc reporting. The key here is carefully defining both the levels and the areas of focus to guide your organization’s efforts toward maturity.
Along similar lines, the Dell Maturity Model breaks down maturity into four classifications:
- Data aware: Pulling data from different systems to create non-standardized reports manually. The goal here is standardizing reporting.
- Data proficient: Using an organization-wide reporting platform to standardize the process. The goal is to use BI software to track performance metrics.
- Data savvy: Using data to make important business decisions. The goal is to fuel decisions with relevant data.
- Data–driven: Fully integrating data with all business processes; requiring data to make decisions. The goal here is to scale up and recoup return on investment.
Some organizations draw on existing models to gauge their progress; others create their own proprietary measures to define data maturity. The important thing is companies are able to take stock of where they stand and build out steps to move toward becoming truly data-driven.
Moving Steadily Toward Data Maturity
The good news is organizations are making leaps and bounds toward ranking higher in data maturity. Boston Consulting Group found adoption across eight key industries — like telecommunications, financial services and retail — improved maturity by 19 percent between 2016 and 2019. The key takeaway from this is steady, consistent improvement across all areas of data capabilities is powering this change, rather than steep, sudden improvement in certain areas.
Analytics architecture, BI tools, company culture, governance and people all work together to determine data maturity for an organization, which is why it’s important to measure progress and set goals across these various categories.