Once you understand the differences among the five distinct stages of data maturity, it’s time to examine your own data process and determine how mature you are. The way to do this is to answer a series of questions. When considering your answers to these questions, be as honest as possible. It’s good to have aspirations to make your team more mature with its use of data, but you can’t skip steps in this maturation process.
Below is a series of questions about your data achievements. Start at the beginning and answer each question with either a yes or a no. If you answer yes, keep advancing. If you answer no to a question, stop advancing and take note of where you are in the list.
Your company’s data maturity is the last stage where you can answer yes to every question. For example, if you answer yes to the first four questions and no to the fifth, you’re in Stage 2 of data maturity. The later questions provide a glimpse into where your data team will go as it becomes more mature.
If you answer yes to some questions in a stage, but not all of them, then you’re on the right track to attaining that level of data maturity, but you’re not there yet. For example, if your first no comes at the seventh question, you’re still at Stage 2 of data maturity, but you’re close to attaining Stage 3.
- Do you use different platforms to report on different business functions?
- Do your analytics tools need integrations with lots of business applications?
- Do you have a single source of truth for sales and marketing?
- Do you have a single reporting platform?
- Can your analysts blend modeled data with raw data from multiple sources?
- Do you have associated data from all phases of the customer journey?
- Do you blend data from multiple sources into a warehouse?
- Do you have a governed single source of truth for 90%+ of your data?
- Does everyone have appropriate access to data?
- Do business units hire their own analysts who use data and tools provided by a central team?
- Do you have model management and productization capabilities?
- Do analysts use data to train and evaluate ML models?
With a clear understanding of where your company falls on the data maturity spectrum, it’s time to start thinking about the tactics you can take to improve. In our Data Maturity Curve guide, we’ll cover steps you can take that will increase the maturity of your team further.
As you think about what types of personnel and tool changes you need to make to increase your data maturity, it’s also vital to think about the new types of value your data can provide as you improve your data processes. As you build a plan plan to mature with your data, think about the questions your data can answer today and the questions you’d like to be able to answer next.