Evaluating Your Data Maturity: 5 Key Questions
Navigate the Data Maturity Pyramid with Confidence and Style
We’ve written previously about the concept of data maturity - an organization’s ability to effectively leverage its data for decision-making - and what it looks like at each stage of the data maturity pyramid. But assessing your data maturity isn’t a one-and-done process. Regularly evaluating your org’s data maturity is an evergreen step towards identifying areas for improvement and prioritizing initiatives.
Today, we're serving up some food for thought. We've got questions to help you reflect on how your organization views its data. Who knows? You might be further along than you think!
Remember, there's no judgment here. Improving data maturity looks different at each stage, and being aware of where you stand doesn't always mean you need to act right away. Sometimes, there are good reasons to hit pause on data maturity improvements (more on that later).
So, let’s dive in and see where you land on the data maturity scale.
First, a quick recap of the Stages of the Data Maturity Pyramid
Ad Hoc: Data is scattered and inconsistently managed. Accessing and using it is a manual process.
Defined: Some basic data management practices and policies are in place. Data quality and consistency are starting to become a priority.
Scalable: Processes are starting to be automated. Teams are empowered to use data in their decision-making.
Strategic: Data is treated as a competitive asset, with robust governance and analytics.
Innovative: Data drives innovation and is fully integrated into business operations. This is a tech and data-first organization.
Consider how your organization might answer these questions, and we'll explore typical responses at different organizational stages.
Question 1: How would you describe your data storage?
A) Our what now?
B) Spreadsheets! Just need to figure out where they are on the shared drive.
C) We definitely have a database. It’s not pretty but it works. Joanne is not allowed to use it, she always deletes something by accident.
D) Thank you for asking about our well-organized, centralized data warehouse. In this essay,
At the Ad Hoc stage, data might be anywhere. In emails, on paper, in Word documents, maybe in the cloud, more likely on someone’s computer. There’s no structured process or system for storing data.
At the Defined stage, data is collected manually and entered into an isolated spreadsheet every so often.
At the Scalable stage, data exists in a range of systems or tools that may or may not connect to each other. Systems are unconnected by default, rather than by design, but they’re generally secure and cloud-based.
At the Strategic & Innovative stages, data is collected, cleaned, and stored digitally and automatically from multiple sources. Information silos have been broken down implementing interlinked systems and tools with appropriately managed access.
Question 2: How do you handle data from multiple sources within your organization?
A) There’s data in five different places and no way to look at it together and make comparisons. Best not to think about it!
B) We get by, but when we need to pull reports together with data from different sources, it’s a manual and time-consuming process. Someone usually loses half a day that would be better spent doing something else.
C) We’ve got some automation between systems, but it could be improved.. Sometimes hiccups or sync delays cause confusion about which dataset is fresher.
D) We’ve got a thoroughly integrated, seamless and automated system for bringing data together to a shared system where everyone can use it. Teams always have access to up-to-date, consistent information across the organization.
At the Ad Hoc stage, finding data is inefficient and heavily reliant on an individual employee’s knowledge.
At the Defined stage, there are designated places (hopefully shared, digital) for storing data. There are still ad hoc processes required for making the data available.
At the Scalable stage, we’re starting to see automated data processing for high-profile or business-critical areas, but it may be inconsistent elsewhere.
At the Strategic & Innovative stages, data is automatically ingested from a number of sources, cleaned and stored in a centralized location, ready for further analysis.
Question 3: How often do you use data in decision-making?
A) We’re more what you’d call “gut-based decision-makers.” If what we did last time didn’t work, we do the opposite.
B) We have Sales pull together some figures. Decisions are about what will make the line go up.
C) We hired some consultants to set up some dashboards. They’re great! Kinda wish we could update them or create some new ones ourselves.
D) Every major decision is data-driven, and our data is configured for accessibility and self-service. People across departments are able to generate the visualizations that matter most to them.
At the Ad Hoc stage, decisions rely on the personal experience of leadership, or what “seems” to be working.
At the Defined stage, data and some verbal accounts can be combined to look at things that happened recently.
At the Scalable stage, organizations are able to use past and current data to see and understand trends and support decision making.
At the Strategic & Innovative stages, teams across the organization regularly use not just past and present data, but forward-looking analysis (forecasting, predictive analytics) for business planning and decision-making.
Question 4: How would you describe your current data quality?
A) Uh. Next question please.
B) Sometimes when we’re not sure what to have the summer interns do, we tell them to clean up the database. No idea what that even means but they seem busy enough?
C) Generally good, with occasional weird issues
D) We’ve got robust data quality processes and procedures in place. They’re well-publicized and everyone is educated about what their role is in following them and why it matters.
At the Ad Hoc stage, data quality is a concern when it’s mandated by legal requirements. No sense of how data quality is changing over time.
At the Defined stage, data quality is managed ad hoc. Someone notices an issue and a human goes into the data to clean it up.
At the Scalable stage, companies are beginning to manage quality for the most high-priority data. They are starting to consider a variety of data quality factors beyond simply “is it correct right now.”
At the Strategic & Innovative stages, there is automated moderating in place, and well-governed rules for accessing, maintaining, and archiving data. Data quality is consistently managed across all stages of the data lifecycle.
Question 5: How advanced is your data analysis?
A) Hardly anyone ever asks, so I don’t have to think about it. Under extreme duress I can use Excel to create a pie chart.
B) We hired a new grad with an analytics degree. She uses some other software to make pie charts for different departments.
C) Everyone generally understands the importance of using data, not just to make decisions but to ensure we’re tackling the best business goals.
D) Our Data team has set up ways for us to use machine learning regularly in our work. Now we’re even looking at using AI to automate some processes. Data Science is fully integrated with our entire business lifecycle.
At the Ad Hoc stage, leadership does not see data as a priority, so data analysis is not a valued concern throughout the org.
At the Defined stage, leaders may recognize the importance of data, but don’t prioritize engaging with it.
At the Scalable stage, leaders are curious about potential uses & benefits of data, so a good data culture is starting to emerge.
At the Strategic & Innovative stages, data is seen as a major organizational priority, crucial to all aspects of the business strategy. There’s a strong data culture and a sense of its value throughout the entire organization.
So, how'd you do? Hopefully, these questions got your gears turning and gave you a taste of the many ways to think about your organization's data maturity. As you can probably imagine, this is a huge topic, and we've only scratched the surface here.
Now, you might be wondering, "Should we always be pushing up to the next level up the data maturity pyramid?" The answer is: it depends, and not necessarily!
Improving data maturity takes time, money, and effort (sometimes a lot of each).
Your current level might be just right for your business needs right now.
The timing might not be ideal for a big data overhaul.
Remember, every organization is unique. It's all about finding that sweet spot between what you need, what you can do, and how fast you want to get there.
Schedule some time with us if you want help assessing your organization's data maturity. Whether you're just starting to think about data maturity or ready to dive in and make improvements, we're here to help. Don't let your data potential go untapped – reach out and let's chat about how we can supercharge your organization's data game together.