Nowadays, data usage has become the ultimate practice for companies to deliver an excellent service or product and optimize it. The importance for organizations lies in making decisions based on data governance systems and comprehension of analytics.
By providing a defined approach to data management, applying the correct data maturity model help organizations to make good decisions overall.
What is a data maturity model?
A data maturity model is a structure used to determine an organization’s maturity level and data assets. Consisting of various steps, this process can define the maturity level of the information in terms of analytics, data governance, data knowledge, or management.
Starting with the entity becoming aware of the quality, type, and size of available information to have an effective system of data governance presence. Applying this model properly could provide huge insights for decision-making directions, show debilities, and low levels of maturity information so organizations can take action where needed.
To guarantee positive results, the process is divided into various steps for the organizations to follow and incorporate data governance in every practice and decision.
What are the four stages of data maturity?
- Awareness stage: the entity must become in contact with its information. The gathering period involves different techniques like surveys, interviews, or collecting for the company servers.
- User stage: persuading a data analytics maturity model, data management, data governance, and providing security measures.
- Proactive stage: data management approach and best practices that can be useful to improve your service, business, or productivity.
- Driven stage: directives and seniors are ready to set company goals. Decisions are based on a data governance maturity model.
A key feature is to comprehend the uses of the information gathered. While it can be used externally for improving client service, also can be implemented internally for work improvement and efficiency in the business.
The data maturity model generally involves four stages, but depending on the company’s needs or approach, other models could be considered with different steps and more levels. Every company or organization is different, and so are its interests.
While every model is used with the same objective, the levels and processes can show different strengths in the type of industry or goal of the organization.
What are the five levels of a maturity model?
The importance of the data maturity model focuses on the adaptability each business or company gives to solve their specific needs. Here are some examples of different models:
Capability Maturity Model Integration (CMMI)
This process helps organizations to define behaviors, organize, and categorize information for operating correctly in software development:
- Initial: no organization and poor information control.
- Managed: define characteristics for projects and often reactive data.
- Defined: define process and data becomes proactive.
- Quantitatively Managed: the process is controlled and measurable.
- Optimizing: iteration processes to improve the service.
Gartner’s Data and Analytics Maturity Model
The approach of this model, besides addressing the information, focus on the decision-making aspect:
- Basic: first interest in possibilities of AI tools.
- Opportunistic: experimentation in data science and tools.
- Systematic: developing first steps and results.
- Differentiating: AI is continuously used for digital and processing tasks.
- Transformational: AI has become part of the business operation.
Benefits of implementing a data maturity model
Not only can organizations benefit from a maturity model, but daily tasks and workflow can be impacted too. For example:
User insights. By understanding the maturity of your data you can address the correct problems of your clients and focus on improving debilities in the service.
Clear goals. Comprehending your information gives you a clear view of where you should be making decisions. Having data to support your decision-making is a crucial practice for any type of business.
Understanding priorities. Rather than spending time on every task you come across, you have an effective system of communicating the principal goals or problems in your organization.
Identifying user insights and having defined goals and clear priorities can improve workers’ skills by working towards a maturity model. Improvements can be applied in every aspect of the company.
The benefits of data maturity models are right ahead for any business or company. To stay updated on the latest benefits of the tech industry, stay tuned to our blog and our big data analytics services!
