4 Stages of Data Maturity: Why Companies Continue to Invest in Analytics
When people see great data visualizations or a cool dashboard, they intuitively know how that can positively impact their business. They see how their data can come alive and actually help them make decisions.
But do they have that data foundation or maturity underneath the business intelligence (BI) layer to make it possible? Without the foundation, you can't really make use of that top layer and get value out of a dashboard tool on its own.
Data-Curious companies go through four levels of data maturity (and subsequently investments in their data analytics) in order to reach data-transformation.
What is Data Maturity?
Data maturity means the fidelity by which your company or department uses data analytics for conversations, ideation, or decision making. This is where we go from "hunches" to "hunches backed up by data" to "Hey, something in the data gives us a new idea."
Stage 1: Data-Exploration - We're all on Spreadsheets
Everyone is doing the best they can, manually pulling data out of systems and trying to make sense of it. There's no standardized way of calculating metrics or presenting the story that the data is trying to tell. Most companies go through this stage as they are growing.
Why companies invest more at this stage: the data volume, definitions, sources, definitions and complexity become too much to fit in everyone's heads.
Stage 2: Data-Informed - We Hired Some Analysts
Someone steps up or is hired to understand the data behind the business and begins to communicate the how and why. It's not a real technical state, but at least someone becomes the go-to person for data analytics.
Why companies invest more at this stage: The go-to person quickly realizes standardized definitions are missing across the org, causing people not to understand nor agree upon which data is noise and which data is signal.
Stage 3: Data-Driven - We Need a Data Warehouse
The company realizes they need to have canonical definitions of "what is a customer," and "what is revenue," and some of the basics on "how are we calculating all of this?" To do this, you need a data warehouse — somewhere that you can pull data from disparate sources across the business together in one place.
Why companies invest more at this stage: To gain a 360 degree view of the business by continuing to add more sources, metrics, and reports for each division and team.
Stage 4: Data-Transformation - Data-Driven Decisions Are Part of Our Culture
Here, people are using the data. They are looking at reports. They might be building their own reports and exploring them on the fly. No one has compile manual reports after the kids go to bed.
Why companies invest more at this stage: The company may want to embed reports in their ERP, expose data (and charge for it!) to their customers, or embed visualizations in their customer-facing product. Here, this is where AI exploration comes in with Natural Language Processing (NLP), which more and more companies wishing to "ask questions of their data" alongside the pre-defined, automated, daily reporting metrics.
Conclusion
When your company invests in data analytics, it's important to agree upon where your data maturity goals are and at what stage of the process you are entering.
Data maturity isn't all or nothing, and different data audiences (departments, teams, etc.) may not all need to end up at stage four. In short, this is a handy, informal guide to get your business talking about data maturity.
Related Article: How Much Do Data Analytics Services Cost?
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Transcribed and edited from my interview with Go West IT