The Data Maturity Model – Categorical Metrics

Categorical metrics indicate in an overall manner at which stage a project or organisation is currently in. Their primary usage is in giving qualitative indicators both to the current state of an entity as well as the possible evolution,  and the next steps to take. Categorical metrics are drawn from global notions or general impressions about certain characteristics.

Metric – Data Diffusion

This is a categorical metric, primarily used at an enterprise (organisational) level. It is drawn from the overall direction of diffusion of data within an organisation. Using the data diffusion metric one can initial indication about the current level of Data Maturity of an organisation. Following are the possible categories:

Bottom-up ⇑: When the overall flow of data is from projects level to corporate level. Then, we can infer that data is being used “à posteriori” primarily, to analyse the past performances of projects and businesses. It implies primarily read-only usage of data. Hence, the organisation is in all probabilities at the “Data-sensitive” level of maturity [level 2]. 

Top-down ⇓: When the overall diffusion of data is from a corporate level to projects level, in an organisation. Then, we can infer that data is being used globally to plan and program for future projects, in an “à priori” manner. Hence, the organisation is in all probabilities at the “Data-oriented” level of maturity [level 3].

Bidirectional⇔: When the exchange of data is in both directions, and regularly. In such an organisational scenario, data is used to project and plan future activities / businesses, as well as, the results and fruits (value-add) of data usage in projects are being recycled and fed a corporate level to develop further strategies for the future. In this process, data is being reutilised and revalued. We can then infer that the organisation has attained the ultimate level of maturity [level 4], i.e. it is Data-driven.

Metric – Data Scope

This is a categorical metric. It indicates the general scope of data usage within an organisation. Using the data diffusion metric one can initial indication about the current level of Data Maturity of an organisation. Following are the possible categories:

Bottom-up ⇑: When the overall flow of data is from projects level to corporate level. Then, we can infer that data is being used “à posteriori” primarily, to analyse the past performances of projects and businesses. It implies primarily read-only usage of data. Hence, the organisation is in all probabilities at the “Data-sensitive” level of maturity [level 2]. 

Top-down ⇓: When the overall diffusion of data is from a corporate level to projects level, in an organisation. Then, we can infer that data is being used globally to plan and program for future projects, in an “à priori” manner. Hence, the organisation is in all probabilities at the “Data-oriented” level of maturity [level 3].

Bidirectional⇔: When the exchange of data is in both directions, and regularly. In such an organisational scenario, data is used to project and plan future activities / businesses, as well as, the results and fruits (value-add) of data usage in projects are being recycled and fed a corporate level to develop further strategies for the future. In this process, data is being reutilised and revalued. We can then infer that the organisation has attained the ultimate level of maturity [level 4], i.e. it is Data-driven.

 

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Detailed Pages in this section:

« Data Maturity Model » – Part 3.1 [Categorical Metrics]

« Data Maturity Model » – Part 3.2 [Quantitative Metrics]

Next Section: « Data Maturity Model » – Part 4 [Data Neutrality]