Data Maturity for SaaS: Model, Stages, and Benefits
Company data is one of the most valuable assets that a SaaS business can have. As such, implementing a data maturity model to get the most out of new insights will help you act on your product analytics instead of becoming distracted by vanity metrics.
This guide will go over what data maturity is, which factors affect it, and walk you through the four stages of data maturity. Be sure to read to the end to see our list of best practices and tool recommendations!
- The data maturity model evaluates how well a business leverages the organization’s data across different stages of the decision-making process.
- Internal culture, data quality, analysis tools, usage strategies, analysis methods, team skills, and strong leadership all impact the data maturity of an organization.
- Data maturity models help you make data-driven decisions across different departments, set better goals, and act on customer insights.
- The four stages of data maturity are awareness, proficiency, savviness, and uncompromising data-driven decision-making.
- Stage one is data awareness which centers around exploring available analytics.
- Stage two is data proficiency which serves as the starting point for implementing data management best practices.
- Stage three is data savviness where data is used for key decisions.
- Stage four is data-driven where data is fully incorporated into daily workflows.
- To become data mature, you must define clear objectives and assess your current level of data maturity. You’ll also need to establish data governance policies and invest in the right tools for the job.
- Full-suite product growth tools like Userpilot help you analyze various insights and track business, behavioral, or product-related metrics. Get your free Userpilot demo today!
What is data maturity?
Data maturity is a framework that evaluates the maturity of a company’s data usage and how well it leverages data when making decisions. Instead of relying on instinct or lore, data-mature companies utilize the organization’s data to promote data-informed decision-making.
What factors affect data maturity?
There are different models for measuring the data maturity of organizations but there are a few internal and external factors that have an outsized impact:
- Culture. The company’s culture will be a big determiner of an organization’s data maturity since cultures that encourage data usage are bound to achieve data maturity faster.
- Data. The quality of the data an organization has access to, how many sources are available, and the type of data that the business tracks will all contribute to a high or low data maturity.
- Tools. Which tools an organization uses for data analysis will determine how effectively they’re incorporating data into their decision-making and business goals.
- Uses. Having a defined strategy for how to use data effectively and what purpose it serves contributes to high data maturity.
- Analysis. The techniques used to analyze your own data, feed insights into larger data projects, and employ data science best practices to avoid bias are all crucial factors.
- Skills. The personal skill set of your team members will be another determiner of how fast your organization can become data mature.
- Leadership. Because data-driven decision-making is a top-down process for most organizations, enthusiastic leaders are essential for an organization to reach its full potential.
Why do you need data maturity models?
Having a data maturity model is crucial for quite a few key reasons:
- Data-driven decision-making. Data maturity makes it easier for each team within an organization to leverage data. This includes marketers improving the targeting of a campaign, sales representatives addressing common objections, or support agents identifying recurring problems.
- Smart goal planning. Companies on the higher end of the data maturity scale are bound to use better goal-setting frameworks that use a company’s own data to prioritize objectives and create a detailed roadmap.
- Customer data analysis. Increasing the level of data maturity within an organization will make gathering customer insights and acting on user analytics a lot easier. Facilitating the collection and analysis of customer data will offer more insights that can skyrocket product growth.
4 stages of the data maturity model
Measuring the data maturity level of an organization isn’t as complicated as you might think. The data maturity model breaks things down into four stages:
- Data proficient
Remember, reaching stage four isn’t the right goal for all organizations, but understanding the stages of such models is crucial when trying to measure your own proficiency.
1. Data-aware: Explore all the data you have
The first stage on the data maturity scale is all about an organization understanding the data they possess. Organizations must identify, explore, and recognize the various sources of data (as well as each channel’s strengths or weaknesses).
2. Data proficient: Start to implement data management practices
Organizations that understand what data can do and begin implementing data management practices are in the second stage of data maturity — proficiency. Strategies employed will include data collection, integration, and quality control.
Organizations in the second stage of their data maturity journey usually also begin to invest in data-driven tools. For example, a business comfortable accessing analytics and insights might get a solution like Userpilot to help them collect product usage data:
3. Data-savvy: Use data for critical decision-making processes
The third stage of data literacy contains companies that use data to make the most important business decisions. For instance, a business in the third maturity stage would likely look at a lot of data before adjusting pricing plans.
This could include analyzing usage patterns across different plans or seeing which features free users use the most. You’ll also be able to use this data to compare power users or paid customers, explore usage limits, and drive account expansion through upsell opportunities.
4. Data-driven: Incorporate data analysis into daily workflows
A business with a no data, no decision approach falls into the fourth maturity stage. Companies like these use data on a daily basis and rarely make any decisions without first referencing the most relevant analytics.
One example would be seeing a drop in conversion rates, using data analytics to uncover the underlying reasons, and conducting funnel analysis to identify sources of friction. Userpilot lets you conduct funnel analysis to identify friction points where most users begin to drop off.
Note: Unwavering adherence to data-driven decisions could reduce productivity levels by creating roadblocks whenever team members need to wait for data before proceeding with their workflow.
How to become data mature?
Now that you understand how data maturity assessment works and what the stages of data maturity are, it’s time to improve data maturity to drive business success. The steps below will help you move through the stages of data maturity for yourself.
Define your data maturity objectives and goals
Your data strategy should always align with your overarching business strategy. This is why establishing goals for developing a data maturity framework and achieving other business goals will help you find areas where these objectives overlap or conflict.
Developing SMART goals is a good start for how to identify core business/data goals:
Perform a data maturity assessment to understand your current maturity level
Finding out which stage your company falls into (per the data maturity models and stages described above) will help you figure out what the next step should be. There are a few questions you can ask to assess your organization such as:
- How is data currently collected and managed within the organization?
- What are the existing data management processes and tools in place?
- How effectively is data being utilized to drive decision-making processes?
- What are the strengths and weaknesses of the current data management practices?
- Are there any gaps in data governance, data quality, or data accessibility that need to be addressed?
These questions will help you identify weaknesses, areas for improvement, and opportunities for better data tracking.
Establish procedures around data usage and accessibility
Establishing data governance procedures for how data should be collected and used will make insights more accessible during cross-team collaboration. In other words, a clear data-tracking plan will help with data democratization to avoid the occurrence of data silos within companies.
Tip: Data governance policies should reflect actual usage patterns, incorporate team feedback, and prioritize progress over tradition.
Invest in tools to foster data sharing across your organization
When investing in tools, it’s important to make technical considerations, such as how easy it will be for team members to use the solution. In general, no-code tools make it easier for multiple individuals/teams to collaborate, even if they have varying levels of tech-savviness or coding knowledge.
You should also think about what the purpose of the tools is to ensure you pick the right software.
Are you simply trying to collect more data, extract growth insights, drive adoption, or achieve a combination of the three?
If tracking subscription insights is your only goal, then a specialized tool like Baremetrics would be sufficient. However, broader use cases require a full-suite platform like Userpilot:
As you can see, getting to the third or fourth stage of the data maturity scale can help your business better utilize the insights it already has access to. This will let you set better goals, make smarter decisions, and drive growth across all aspects of the company.
If you’re ready to reap the benefits of advanced analytics and growth insights, then it’s time to get your free Userpilot demo today!