Data is arguably the most precious resource any modern company aspires to have. The famous phrase "who owns the information, owns the world" perfectly reflects the current situation around data.
However, there is a big difference between having the data and being able to use it efficiently. And for a company to fill this gap, it's necessary to employ data specialists, such as data scientists and analysts, to process and model the data. But is it enough to make the most of the data?
In this article, we'll discuss what data maturity is, why it's important, what a data maturity model means, which data maturity models exist, and how to evaluate your company's current data maturity level to start improving it.
What is Data Maturity?
Data maturity is a multi-step process of a company's transition from data unawareness to data literacy. At each moment, a company finds itself at particular stages of this evolution. You can identify the stage your organization is at with some know-how and determine how to proceed successfully toward greater data maturity.
It's crucial to emphasize here that the process of enhancing data maturity should imply the whole company rather than only a specialized data department.
Why Data Maturity is Important
In any business or public entity, all people are somehow involved in working with data, even though they may be at different levels of seniority. For example, those with more seniority often focus on data analysis, data modeling, data management, data engineering, and similar, while those less senior are involved with data gathering, data producing, data entering, data storage, data consuming, and more.
Furthermore, roles in all kinds of industries work with data. For example, a school teacher giving marks to their pupils produces data. A doctor visiting their patients gathers data. A hotel receptionist checking the hotel guests' documents enters data.
For an organization to reach its potential, it must focus on making the most of the data they have access to. This means their data should be collected, stored, treated, accessed, and analyzed with due attention and accuracy.
Every person in a company, not just data professionals, should understand the value of their personal contribution to the organization's data stock and their responsibilities and limits concerning the company's data. Being confident to know which data is relevant to their role and being able to confidently make decisions based on that data all contribute towards a company's data maturity and hence helps its prosperity.
The secret of the success of all modern giants, such as Google, Microsoft, Meta, Amazon, or Netflix, is that they take their data seriously and leverage its full potential at all levels of their organization. Even though small companies may not aspire to become world leaders, adopting the same approach to working with data and enhancing data maturity can become a considerable step forward for them.
What is a Data Maturity Model or Framework?
A data maturity model or framework is a schematic representation of the main stages that a business should pass through to become data mature. Each pillar of such a model has its own definition, a detailed description, and the criteria for reaching that level.
Looking at a data maturity model and comparing its current relationships with data with the model's pillars, a company can locate itself on that scheme and understand its data maturity stage and the corresponding way forward.
In addition to the stages of maturity, some data maturity frameworks also offer key factors that influence the data maturity of an organization. Sometimes these factors can be referred to as characteristics, levers, elements, themes, components, and similar. Such characteristics include tools, skills, people, infrastructure, culture, and the data itself.
Take DataCamp’s Data Maturity Assessment
Understand where your team, department and organization are located on the data maturity spectrum, by taking our 10-minute maturity assessment survey.
Examples of Data Maturity Models
There are various data maturity models designed by large, medium, and small companies. Essentially, they differ in the number and names of landmarks, their descriptions, the goals and challenges of each stage, key influential factors (if provided), and how they are related to each stage. However, the core idea behind any data maturity model is the same:
- to illustrate the journey of a business "from zero to hero" in terms of data maturity
- to identify the current situation of that business on the scheme
- to outline the way forward to a greater data maturity
Let's take a look at some popular data maturity models.
Gartner Data Maturity Model
Description of the model levels:
- Basic: sparing data usage, no systematic approach to data analysis and data management, very basic data quality control.
- Opportunistic: efforts in formalizing data requirements, incentivizing data usage, developing data quality control, basic leadership.
- Systematic: stabilizing data strategy and vision, integrating various data sources, different approaches to different content types.
- Differentiating: forming a specialized data department, data starts influencing all aspects of the business, communicating data insights following best practices.
- Transformational: data management is continually improved, data analytics becomes the core of the business strategy, regular investments in data, the data department is on board.
IBM Data Maturity Model
Description of the model levels:
- Initial: poorly formalized data management (up to no data management at all), limited data usage, no systematic approach to data storage and data processing.
- Managed: increased data awareness, more attention to data-related documentation, enhancing data quality control, efforts in automating data processing, first investments in data.
- Defined: data management policies are well-defined and actively shared inside the company, data usage and data analysis are integrated into almost every project.
- Quantitatively Managed: establishing data management processes in all units of the company, following best practices of data governance and data modeling, stating and implementing quantitative goals towards the data, measuring the performance of the defined goals.
- Optimizing: data becomes a crucial asset of the company involved in all kinds of projects and operations at all levels, strictly controlled and measured data-related goals, highly automized data processing.
DataCamp Data Maturity Model
Description of the model levels:
- Data Reactive: the data isn't used either by the company's employees in their daily work or at an organizational level (in the company's reports or presentations).
- Data Scaling: there are very few people in the whole company who are able to analyze the data, extract meaningful insights from it, and present them to the concerned parties.
- Data Progressive: there is at least one person in each team (not necessarily a certified data professional) who is able to analyze the data, extract meaningful insights from it, and present them to the concerned parties.
- Data Literate: all the employees know how to access the company's data and apply it to their everyday work to be able to make data-driven decisions at all levels.
The DataCamp data maturity model also considers key levers from one data maturity stage to another: infrastructure, people, tools, organization, and processes. If you want to dive deeper and learn more about these levers and their role in enhancing the data maturity of a company, read our white paper, Your Organization's Guide to Data Maturity. For a general overview, you can find useful this data maturity infographic.
Below, you can see an extended version of the DataCamp data maturity model that includes the key levers and how they are related to each stage of data maturity:
How to Assess the Data Maturity of Your Company
Before moving towards higher data maturity, a company must understand at which stage of the data maturity evolution it is currently. To figure this factor out, a representative from your company should assess the organization’s current internal framework and try to answer various questions related to data governance and data processing in their organization.
Analyzing these answers can give you a realistic picture of your company’s current situation in terms of data maturity and, hence, define areas for improvement and the steps to take to become a data-driven business.
Below are some examples of the questions to consider:
- How would you describe data quality in your company?
- What data roles exist in your company today?
- How is the majority of data collected in your company?
- How important is data as part of your company’s overall strategy?
- Which data analysis tools are used in your company?
For a more systematic approach, you can take the 10-minute data maturity assessment survey from DataCamp and gain insight into detailed results regarding the current level of data maturity in your organization, as well as next steps on how to upgrade it.
We’ve explored many aspects of the data maturity of a business. In particular, we defined what data maturity is, discussed its importance for any modern company, took a more granular look at some popular data maturity models, and determined how to assess the current data maturity stage of your company in order to start moving forward and unlock the full potential of data.
To learn more about the key elements of data maturity of a company, consider watching the following webinars:
- The Infrastructure Component of Data Maturity
- The People & Organization Components of Data Maturity
- The Tools & Processes Components of Data Maturity
You can also listen to our episode of the DataFramed podcast on successful frameworks for scaling data maturity with special guest Ganesh Kesari.
Data Maturity FAQs
What is data maturity?
A multi-step gradual process of a company's evolution from data unawareness to data literacy. This process should involve the whole company rather than only a specialized data department.
Why does data maturity matter?
For an organization to function smoothly, the data should be collected, stored, treated, accessed, and analyzed with due attention. Hence, each employee of the company should be data-mature, meaning that they should understand the value of their personal contribution to the organization's data stock and their responsibilities and limits with respect to the company's data. Taking the data seriously at all levels and leveraging its full potential can become a considerable step ahead for any business.
Who should enhance the data maturity of a company?
All the employees of the company rather than only data professionals or managers.
What is a data maturity model?
A systematic representation of the main levels of data maturity, usually four or five of them, that a company should pass through to become data mature. Each level has a definition and a detailed description, including the criteria for reaching it. Some data maturity models also include key factors influencing the data maturity of a company, such as tools, skills, people, infrastructure, analysis, the data itself, etc.
How do various data maturity models differ?
The number and definitions of data maturity levels, their descriptions, the goals and challenges of each stage, the key influential factors (if provided), and how they are related to each stage.
What is the main idea behind any data maturity model?
To illustrate the journey of a business "from zero to hero" in terms of data maturity, identify the current situation of that business on the scheme, and outline the way forward to greater data maturity.
What are some examples of popular data maturity models?
There are various data maturity models out there, often defined by organizations themselves. Some well-known examples include companies such as Gartner, IBM, DataCamp, Dell, SAS, DAMM, Oracle, Stanford, Snowplow, and many others.
What are the key levers of data maturity?
Infrastructure, people, tools, organization, processes. Some data maturity models also mention analysis, data, leadership, skills, and culture.
How can a company understand its current data maturity level?
A representative from the company should think over and try to answer various questions related to data governance and data processing in their organization. Such questions can refer to the data roles existing in the company, data tools in use, the ways of collecting data, and controlling its quality. Taking the data maturity assessment survey of DataCamp can be a good reference point to figure out the current data maturity level of your company and start improving it.
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