Assessing your company’s data maturity – a key consideration for your data journey 

Introduction 

A growing number of businesses realize the importance of using data in their decision-making process. However, only a fraction can call themselves data-mature. What does it mean, and how does data maturity pave the road towards business success? We dive into this below.

What is data maturity?

Data maturity is the extent to which a company is capable of effectively using its data. To master data literacy and become a data-mature organization, the data needs to be deeply ingrained in all areas of the business. In particular, it must be a driving force in the organization’s decision-making processes.

Why is it important to run a data maturity assessment for your company?

To understand why it makes sense to measure your organization’s level of data maturity, it’s important to consider the following:

• Data maturity affects the entire organization. It’s about far more than checking if your company knows where to find data or how capable they are, for instance, at processing or storing it within databases. It’s a critical business metric, which indicates the company’s ability to derive insights from data. Organizations that operate on a high level of maturity can find answers to business questions, validate hypotheses, and assess risks. This can lead to a variety of benefits – higher profitability, team productivity, and even better talent retention.
• You start noticing a link between data and how it translates to driving value (or generating waste). While you can expect good-quality data to bring the anticipated results, poor data is nothing but a cost, leading to inefficiencies. That’s why it’s so important to have the right approach to data management as it helps you evaluate whether the data you use is fit for its purpose, i.e., if it’s accurate, relevant, and timely. By assessing your company’s data maturity you can get a good grasp of if your data positively contributes to building business value or if it’s a burden. 

Data maturity levels 

The data maturity model helps you verify how advanced your organization is when it comes to data management. It includes four levels, let’s take a look at each one now. 

Level 1: Explorer

Initially, you need to gain a good understanding of what data you have. While you’re aware of how crucial it is to collect information to make smart business decisions, you lack processes and tools that would allow you to gather relevant data. You also don’t have any strict rules or guidelines around data collection and use. Deciding who’ll get access to what data and ensuring compliance are among the issues that you still must tackle. 

Since there are no rules around data usage, various teams use data differently and collect it using multiple tools. This can lead to data errors, duplication, missing data, etc., all of which negatively impact data quality. At this stage, decisions are either made without any data or based on limited information.  

You are at Level 1 if:

• You realize the importance of data, but you don’t use it on a daily basis
• You lack clear rules on data usage (no data governance)
• You make decisions based on your intuition and experience, rather than your organization’s data needs

Level 2: Picking up the pace

At the second stage of the data maturity model, the ball starts rolling. Business leaders begin to understand the value behind good quality data and feel the need to invest in tools and processes that would enable effective data collection and analysis. They start to think of the best practices and rules they could put in place to better evaluate projects after they’re launched. 

It becomes natural for every new project to include success metrics, and to continuously review what worked and what didn’t. Teams become more familiar with how to use analytics and have access to tools that gather and store data. This makes finding answers to their questions easier. 

You’ve reached Level 2 if:

• Leadership acknowledges that data analytics is crucial for the well-being of the organization 
• Your team starts investigating analytics tools and understands that they can help them answer business questions quickly
• Your team starts measuring (and improving) the effectiveness of their work by diving into data

Level 3: Data comes forward in decision-making

This is the stage at which each department knows how to find and use data for their purposes. Here’s where your company starts breaking the mental barrier, i.e., the conviction that data is an asset exclusive to technical teams. Everyone at your business can consult verifiable, complete data and know where to find it. 

You can think of it as a more sophisticated ‘awareness’ stage, where teams see how using data helps the company reach its goals. It becomes a driving force in the organization’s decisions, both those affecting teamwork and market strategy. 

Now that the team has access to data analytics tools, they start applying them in their project planning and daily work. Your teams can, for instance, look into historical data to see what has worked for the organization in the past, but also cross-reference it with fresh, operational data. 

You are at this data maturity level if:

• All employees have access to data and they know where to find it
• Everyone in the organization knows how to use data to improve their decision-making
• Both internal and external projects are carried out based on data 

Level 4: Data mastery, where no decision is made without looking at data

At this final stage, data runs in your organization’s veins. Firstly, you’ve created and closely followed data governance practices, and have educated everyone about what it takes to stay compliant.

Every decision within your business is heavily based on insights from your data. It’s also common for your team members to share their findings to further boost their data driven decision capabilities. 

Data-mature organizations don’t need to worry about contradicting data – everyone knows how to use your analytics tools, and sees the same charts and numbers. 

At this level, your RevOps teams have all they need to build out strong go-to-market strategies and continuously improve company profitability.  

Due to your data sharing culture and accessible analytics tools, it’s also easy to bring your new hires up to speed about how data is accessed and leveraged across the organization.

To stay at this data maturity level and maintain cross-team data literacy, you must now continue educating your staff on the importance of data. If you make any amendments or improvements to your existing data analytics tools, then you’ll have to ensure that everyone is aware of the changes. All so that data remains available and discoverable for all.

You are at the highest level if:

• You have clear data governance practices in place which guarantee data security 
• Teams are using a single source of truth in their decision-making 
• Every single business decision is based on data 

How to determine the level of your data maturity? 

The best way to verify your data maturity level is by working with a data management consultant. However, here are some indicators that might be helpful in getting a rough assessment:

• There is a direct link between data maturity, strategy, and business performance. This allows company leaders to spot any data that generates costs instead of creating value. As a result, they can adjust their investments. 

• Data maturity calls for all hands on board. To precisely assess how data mature the organization is, it has to be compared across business units, locations, and tenure of an organization. By gaining this information, company leaders can identify data immature areas, and find ways for improvement. 

• You have access to granular insights across multiple data management disciplines. Knowing your organization’s maturity score isn’t enough as it doesn’t tell you which data management disciplines have a negative effect on your data maturity. You need to get a more granular overview of all your data management disciplines to spot problems. This way you can find out that your immaturity might be caused by your governance practices (or the lack thereof), poor data strategy, or analytics capabilities. 

How to build up your data maturity?

Once you’ve determined your company’s data maturity level, it’s time to create an improvement plan. Here are a few processes that will help you enhance your organization’s data proficiency and decision-making capabilities. 

• Identify the obstacles that are blocking your company from data mastery. Is it the lack of internal buy-in from certain teams? Or, maybe, it’s the absence of data standards or the use of multiple tools instead of a single source of truth?
• Set up a team that will be responsible for improving the company’s data maturity. Don’t fall into the common trap of selecting technical experts only. Data must serve all staff, not only data professionals. That’s why you should include representatives of all departments.
• Select a realistic data maturity strategy. Build your data strategy around your company’s unique factors, such as its structure, industry, and the products/services you render. The data usage guidelines and the analytics tools you select must support your company’s objectives.
• Put effective communication first. As you’ve seen in the data maturity model shared above, getting buy-in from the entire organization is key. Communicate to your team why you need to become a data-mature organization, and what steps it will entail. Make sure that everyone can ask questions and raise any concerns they might have, for example, those relating to data security or specific data sources.
• Pay attention to your data quality. The data you bring into your analytics tools needs to be consistent and standardized. It must be legible to everyone in the business. Without the right data management practices, all of your efforts in building a data culture will go in vain.
• Work with data management consultants. This will be the best way to proceed if you don’t have an internal data strategy. By working with a team like C&F, you’ll get all the help you need in assessing your organization’s data maturity level and selecting the best data maturity model.

Data quality plays a major role in effective data management

Reaching data mastery involves a number of steps. Before applying any changes to your current data strategy, however, it’s important to understand where your organization stands. Among others:

• Identify the gaps and any blockers that are keeping you from upgrading to a higher level of data maturity
• Come up with a plan to mitigate any issues hindering your data maturity. Treat it as a project – determine a specific timeline and allocate the required resources, i.e., people and budget.
• Regardless of the level you’re at, build a mindset that each business decision needs to be based on data. This includes yourself and your team. 

If you want to gain a broader understanding of the role that data maturity plays in an organization’s productivity, profitability, and internal collaboration capabilities, give our data journey guide a read.