Data Maturity: Finding A Path To A Data-Driven Future

Data Maturity: Finding A Path To A Data-Driven Future

Many leaders would be surprised to discover that their organization is lacking in data maturity. They might see that they have data professionals on staff and that different data resources are being used, and they could think that the organization is getting a lot of value from the data being collected, but this is not always the case. This is why a data maturity assessment can be so important.

In a basic sense, data maturity is a measure of an organization’s ability to use data, along with how well the organization leverages those capabilities. When an organization is data mature, it means that it can deploy its data resources to achieve a range of goals. In many cases, this not only means making data-driven decisions but also making data resources more accessible throughout the organization.

A data maturity assessment (DMA) is a framework for determining how data mature an organization is. There are different models for performing a DMA, but most of them will define different stages of data maturity to represent an organization’s data capabilities and the effectiveness with which the organization deploys those capabilities.

Identifying a lack of data maturity is the first step in making an organization more data-driven. But what are some of the issues that might come up during a data maturity assessment?

  • Unclear Goals Or Vision One of the most common issues uncovered during a DMA is the lack of a data strategy or a strategy that does not have clear goals. Data should be seen as a guiding force in the organization, but you need to have goals and a clear vision of where you want the data to take you.
  • Issues With Data Management Data management is often an issue in organizations that lack data maturity. They often have a fragmented data landscape that can lead to conflicts within the organization. Data quality can be an issue as well. These organizations not only have problems recognizing issues with data quality, but they also perform poorly when it comes to reconciling these issues.
  • A Lack Of Engagement Another sign of data immaturity is a lack of broad engagement with data resources. At these organizations, you might only see IT professionals or those in specific data and analytics roles engaging with data systems. This means that leaders and people from other departments need to rely on the data professionals to create reports and provide insights.

Be honest about where you are in your maturity. Its a long road, but making data resources more accessible throughout the organization and empowering users to make data-driven decisions is worth the effort.

To learn more about how forward-thinking organizations are taking steps to improve data maturity, read the full article on Forbes here.

Alternative Text Kathy Leake
CEO of Crux Intelligence and four-time founder/board member. Putting AI in the hands of every business user.