If you’re reading this, you probably know a thing or two about data. Data is one of the most rapidly growing resources in our world, with an estimated 2.5 quintillion bytes created every day. It seems like data is everywhere, but in reality, we’re just getting started with it. Over 90% of data existing today was created within the last five years.
But something changed in the last year that made us want to reconsider what data maturity means today: data usage. Businesses using data to guide decision making has skyrocketed in the last year. The pandemic forced businesses to redefine baselines, find new customers, and make hard decisions about where to close operations. These tough decisions forged more data mature businesses. While innovative businesses have gotten more mature, there are still many organizations that are just beginning their data journey. This model speaks to all stages of the data maturity journey.
SafeGraph is a true data company; we provide CSVs with information about physical places so our clients can perform analytics and grow their businesses. We provide some of the ingredients for data analytics, but we are not a one-stop-shop for solutions. Our laser-focus on data has exposed us to organizations throughout the spectrum of data maturity, and we are fascinated by the different ways organizations interact with data.
To better understand these differences, we researched and developed a data maturity model that details the different levels of experience companies have with data and how that experience manifests in different areas of the business.
Our data maturity model isn’t wildly revolutionary. Others came before and helped guide us in the right direction. But we hope this sheds light on data maturity in 2021, and gives companies direction as they start to become more data mature.
Here’s what did need to change from previous models to reflect today’s environment:
To create the data maturity model, we looked at six aspects of a business: strategy, data, culture, architecture, data governance, and procurement/onboarding. We used the different levels of sophistication across each of these aspects to then develop four unique stages in data maturity.
Explorer: Organizations that are just getting started with data generally do not have a defined strategy for incorporating data into their business. While they may use data for reporting purposes, it is on an ad-hoc basis. They do not source data for these reports and only use internally-collected data.
User: User organizations are aware of how important data quality is for success. They make it a standard to use data internally across the organization with the addition of ad-hoc datasets to assist with amplifying internal data sources. Their reactive use of data is convenient for making insightful business decisions.
Leader: Similar to Users, Leaders centrally use data for decision making within their organization. However, they also use data for competitive intelligence. In order to accomplish organizational missions and business success, Leaders use third party datasets in addition to their own data.
Innovator: Data is used for more than just analysis and observation. In fact, organizations that are Innovators are using data to create algorithms and predict how they can stay ahead of the game. With data governance being a part of the entire organizational business strategy, Innovators must constantly utilize data in new ways to adapt to the uncertainty of the future.
Want to learn more about each stage? Download our free eBook for a detailed breakdown.
Most organizations today are using data in some capacity, but those that have reached the Innovator stage, where data is at the center of their strategy and operations, are truly leveraging it to the fullest potential. How data mature is your organization? Take our Data Maturity Survey and help us dive deeper into our research. Results are anonymous and will be shared in a white paper and webinar in the coming months.
That's it – that's all we do. We want to understand the physical world and power innovation through open access to geospatial data. We believe data should be an open platform, not a trade secret. Information should not be hoarded so that only a few can innovate.