Data Maturity: Advancing from Business Intelligence to Ad Hoc Analysis
The five stages of data maturity are a way for companies to take a look at their overall data processes, ask some difficult questions and start a discussion about how to do more with data. It can also be used as a roadmap for organizations looking to make long-term plans to develop their data team into a more powerful resource.
At Stage 2 (Business Intelligence), companies have incorporated data into their workflow at the level of standardized BI. There are regular, uniform reports from a single source of data, dashboards that update automatically and triggers set up to alert the team if certain events happen. As you evolve into Stage 3 (Ad Hoc Analysis), you’ll build a centralized data team to query the data in new ways and ask your own unique questions.
The biggest benefit of maturing from Stage 2 to Stage 3 is that you will be able to start answering questions about why your data is moving the way that it is. In Stage 2, your whole team gains access to descriptive data that tells you what is happening across all the parts of your business. For some organizations, this is the right depth to analyze data and those teams can focus on expanding their BI operation wider and deeper without the need for a full-time data team. For other organizations, there's a need to add advanced analysis to that BI functionality and test hypotheses about the levers that are affecting important metrics. These teams will ultimately want to consider advancing to Stage 3.
Stage 3: time for a data team
The first big piece of the move to Stage 3 is the establishment of a full-time data team. The move from Stage 1 to Stage 2 was primarily about getting the right tooling set up and removing barriers to access, but the move to Stage 3 is about establishing a data team to start investigating new questions about the data. Once the right people are in place, Stage 3 is a maturity level that allows companies to complement their BI reports by asking their own questions about why the numbers in those reports move the way they do. This flexibility allows the team to dig deeper on important issues and adjust to change much faster.
At Stage 3, the data team is building new models with the data to investigate explanations for KPI movement. Since so much new exploration and modeling is happening, it’s crucial that a data team is able to perform these tasks as quickly as possible. They’ll need to use SQL to perform these investigations, so tools like SQL Views, SQL Snippets and SQL Filters come into use.
Features that help a company mature to Stage 3
These features are a common code resources that let a team create a set of code blocks that are reusable in multiple different queries. With a robust library of reusable SQL, new data queries are much faster to create and new questions get answered in a fraction of the time.
With a data team in place and a library of these SQL building blocks established, Stage 3 companies can still allow nontechnical professionals to explore datasets through simple drag-and-drop functionality, but only after the data team has prepared that environment. Tools like Periscope’s Data Discovery make it easy for a data team to create a safe environment for other teams to sort through data and find their own insights.
At Stage 3, the focus begins to shift from backward-looking descriptive data to diagnostic data, which also begins to attract new audiences. For example, leaders from sales and marketing are interested in data from Stage 2’s shared BI dashboards, but product engineering professionals don’t get the same value from it. At Stage 3, that team is now compelled to join the data process because they can ask questions that provide a lot more value to their workflow.
To learn more about the stages of data maturity, assess which stage your company is in and find out how to advance further, download our Data Maturity Curve guide.