January 18, 2024
10 min read

How to Conduct a Data Maturity Assessment

In the ever-evolving landscape of data management, understanding and improving your organisation's data maturity is crucial. A better understanding of data allows for organisations to leverage it more effectively and improve business strategies, boosting performance and profitability. 

In this blog, we delve into the process of conducting a Data Maturity Assessment and the questions that you and your organisation need to answer to understand the state of your data.

  1. What is Data Maturity
  2. The Five Pillars of Data Maturity
    - Data Strategy & Vision
    - Organisation
    - Data Warehouse
    - Data Analytics in use
    - User adoption
  3. Stages of Data Maturity 
  4. Navigating the Journey to Data Maturity

What is Data Maturity

Before we dive into the 'how,' let's clarify the 'what.' Data maturity refers to an organisation's capability to effectively manage, utilise, and capitalise on its data. It involves bringing all of their data sources together, analysing it and extracting insights to make informed business decisions. 

According to research findings by Dataconomy, organisations with high levels of data maturity experience up to 2.5 times improvement in business outcomes, including profits, operational efficiency, and customer loyalty and value. 

How to Conduct a Data Maturity Assessment

Organisational data is complex and therefore we have created a framework to find out where is your organisation, culturally and technically, regarding data. 

This framework consists of five pillars and a series of questions to your organisation’s stakeholders that help us determine your data maturity levels and suggest the most suitable solution to your business. 

Let’s get started.

The Five Pillars of Data Maturity

Before we dive in, here is an overview of the five pillars: 

  1. Data Strategy & Vision: What are you trying to achieve?
  2. Organisation: What people do you have focused on data? What systems are you currently using and trialling?
  3. Data Warehouse: Do you have a data warehouse in place in any capacity?
  4. Analytics in use: What are the current analytical systems in use?
  5. User adoption: Are people using them? Do you have a means of measuring that?

Pillar 1: Data Strategy & Vision

The questions in this pillar assess how well an organisation has defined and communicated its vision and strategy for data analytics. They delve into clarity of vision, leadership endorsement, strategic planning, resource allocation, and adaptability. 

This is crucial for understanding how an organisation aligns its analytical objectives and resources to achieve cohesive, actionable insights. 

We also understand that this is kind of a cheat, as we lump strategy and vision under one pillar. So let’s break them down and start with vision. 


The vision for analytics at your company should focus on enabling the organisation to align on analytical objectives and iterate quickly and cohesively.

If the vision focuses on specific deliverables (i.e. dashboards or reports), you are missing the value of a data culture.

Here are the questions we ask our clients to find out more about their vision on data:

  • At a high level, what would you like your business intelligence system to achieve?
  • Is this vision defined anywhere (i.e. written down)?
  • Who has defined the vision, and how did they come to that definition?
  • Is there executive buy-in around this vision?
  • Does your company’s vision cover access to information and governance?
  • Within data/privacy regulations, will your company provide access to data by default to encourage exploration and discovery? Or will your company grant access “as needed” to control and narrow personnel’s focus on given tasks?
  • Who’s (and what teams are) responsible for delivering the vision?
  • In using the output? (or, conversely, which teams that will not use the output)
  • How often is the vision reviewed/updated?


How do you plan to achieve your vision, and who is the designated owner of this process?

  • What is your timeline for achieving the vision, and what are the expected deliverables?
  • Have you listed all necessary resources, including personnel, technologies, and processes, to achieve your vision?
  • Have you defined each component needed to achieve your vision and established a timeline for their coordinated delivery?
  • Does each strategic component have a detailed project plan?
  • Executive Sponsorship and Management Involvement: Who is the highest management level involved, and is there an executive sponsor for analytics?
  • Are senior leaders clear on the vision, and how are they informed about analytical projects?
  • Have senior leaders agreed to provide necessary support for your strategic initiatives, and how frequently are they updated on analytical projects?
  • Do high-level managers in different departments discuss and collaborate on data and analytical initiatives?
  • Is there a process for reviewing and updating the strategy as the vision evolves, and is there a balance between technical, business, and leadership resources?

Note that if you can’t comprehensively answer these questions, you can go down to the bottom of this guide to our section on Stages of Data Maturity, which will still give you a rough indicator of your organisation’s data maturity level.

Pillar 2: Organisation

Here we focus on the structure and effectiveness of the data team. The questions explore the roles, responsibilities, and skill sets within the team, as well as the process of resource allocation and team management. 

It's vital to evaluate how well the data team is organised to meet the company's analytical goals and address skill gaps or inefficiencies.

  • Who comprises your data team, and what are their roles and skill sets?
  • Who owns different parts of the analytics ecosystem?
  • What is the structure of your personnel management, and is the team effectively structured to execute your analytical vision and strategy?
  • Assessment: How is the effectiveness of the data team assessed, and who is responsible for addressing inefficiencies and skill gaps?
  • Do team members know how to request necessary resources, and is there adequate seniority to fulfil these requests?
  • Who defines the business logic for your metrics, and how is this incorporated into your data platform?
  • How is the integrity of your company’s metric definitions maintained and audited?
  • How does the team handle requests for new or updated reports from business users?
  • How is access to data controlled, and how often is this access audited?
  • How does the analytics team integrate with other business units in your organisation?

Pillar 3: Data Warehouse

The questions explore the decision-making process and operational effectiveness of the data warehouse (if there is one). They cover performance goals, scalability, ownership of key processes, data quality, and the integration of new data sources.

This pillar is key to assessing how the data warehouse supports the organisation's broader data strategy.

  • Do you have a data warehouse?
  • Do you have a clear definition of performance goals for your data warehouse?
  • Can your tech stack support scalable delivery?
  • Who is responsible for selecting the data warehousing system, and have they clearly communicated the reasons for this choice compared to other options?
  • Who manages the table and schema structure in your data warehouse?
  • Who owns the data ingestion and ETL (Extract, Transform, Load) processes?
  • Who is in charge of data quality, and how is this quality measured and audited?
  • Who is responsible for optimizing tables for storage and performance?
  • How often do you evaluate new data warehouse options?
  • What is the process for integrating a new data source into your data warehouse?

Pillar 4: Data Analytics in Use

This pillar examines the practical application and effectiveness of data analytics within the organisation. Questions probe into user accessibility, the actionability of metrics, data storytelling, and the organisation's approach to vanity metrics.

The focus here is on how analytics are used to drive decisions and the integration of analytics across different business units.

  • Are users able to find answers to their data questions using your analytics tools?
  • Can users take action based on the metrics available to them?
  • Are users capable of telling a compelling story with the data?
  • What tools does your company use to surface data, and how are users informed about and given access to these tools?
  • How many metrics in your company's reports are considered vanity metrics, and do users understand what vanity metrics are?
  • Do users know how to test if a metric is a vanity metric?
  • How are your company’s reports organised, and is this system designed to help users find relevant information?
  • Who designs the system for organising company reports?
  • What fraction of business units have an internal analytics champion, and how do business users interact with the core analytics team?
  • How do business users request analytics support, and is this process clearly defined and monitored?

Pillar 5: User Adoption

The fifth pillar evaluates the strategies and effectiveness of rolling out analytics tools and training within the organisation. We look into aspects such as the rollout roadmap, training initiatives, support for new users, and the tracking of analytics usage. 

This is crucial for understanding how the organisation facilitates and enhances user engagement with analytics tools, ensuring that the investment in data analytics is fully leveraged.

  • Does your analytics team have a clear rollout roadmap for use cases, functionalities, and targeted business units and teams?
  • How does the analytics team communicate this rollout roadmap to the relevant stakeholders?
  • Does the analytics team incorporate feedback from business units when planning the rollout timeline and approach?
  • How often is new functionality rolled out and new teams onboarded?
  • Is training mandatory for personnel in your organisation, and is analytics training included in the new hire onboarding process?
  • Who owns the training initiative, and is there an executive sponsor for this initiative across business units?
  • How do you determine who needs training, and how is this training advertised?
  • What percentage of users have attended training on your analytics systems?
  • Could you describe your training offerings in terms of channel (e.g., classroom, videos, office hours), length, and target audience?
  • Do you track analytics usage and adjust training or support initiatives to target groups with high numbers of new or untrained users or those experiencing a drop in analytics usage?

Stages of Data Maturity

Sometimes, when we run this questionnaire with organisational leaders, some of them get uncomfortable answering the questions or may not have the full answers.

It is important for these questions to be answered fully and with as much details as possible, as:

  1. It helps you get a sense of the challenge of running a data organisation (if you aren’t already aware)
  2. It helps us get an understanding of how accessible the data points are to you so we can prepare about how accessible they will be for us if the project goes ahead

Therefore, we prepared some additional stages of data maturity to help you determine the level of data maturity in your organisation without going into too many details. These focus more on stages where you may be within your company based on only a few statements.


Level 1: Manual Data Drudgery

  • Reports are being done manually.
  • Spreadsheets and slide decks communicate the status of projects and services.
  • There are frequent disagreements on how data is processed.
  • Manual processes often result in inaccurate reports.

Level 2: Death by Dashboards

  • There are shadow data teams - small pockets in the organisation that can do bits of data work and generate reports.
  • Only privileged employees can create reports.
  • There’s a level of spending on reporting, dashboarding, or BI.
  • Employees are often flooded with irrelevant data.
  • There are multiple inconsistent sources of truth.

Level 3: Invested in Data

  • You’ve started multi-source data merging.
  • You’ve moved away from spreadsheets as a primary data source.
  • There is a consistent view of information across the organisation.
  • You’ve employed modern data transformation technologies. (No spreadsheet formulas!)
  • You have a data vision written and upheld.

Level 4: Emerging Governance and Intelligence

  • Services are being deployed around insights.
  • You’ve built data into your product.
  • You have visibility around the data assets being used (and not used).
  • You have dedicated employees who focus on improving data processes.

Level 5: Intelligence and Forecasting

  • You have dedicated advanced analytical engineers
  • ML models are being deployed
  • Forecasts are being used to make decisions

Navigating the Journey to Data Maturity

Achieving a high level of data maturity is not without its challenges. Even though many companies understand that there is so much value in data, often they don’t know how to actually extract it. 

It requires a strategic approach, encompassing robust data governance, quality management, and a culture that values data-driven decision-making.

As experts in the field of data management and analysis, our teams at Cobry are equipped with the tools, expertise, and experience to guide your organisation through this critical process.

Whether your organisation is just starting on its data journey or looking to refine and advance your existing data strategies, our team is ready to assist. We offer tailored solutions that align with your unique business needs and goals, ensuring an efficient and successful journey towards data maturity and analytics. 

Get in touch with Cobry today, and take the first step towards unlocking the full potential of your data. Together, we can transform data into your most valuable asset.

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