The Data Maturity Model – Metrics
Metrics are vital to finding the current level of Data Maturity as well as understanding the dynamics of the evolution [where are we heading to, speed, next steps, etc].
In this section, we explain how to use metrics pertinent to the Data Maturity Model. Following are some important metrics that need to considered, for any Data initiative leading to a Data-driven organisation. Though they do not form an exhaustive set, these are the principal metrics and together as an ensemble, they can analyse 95% of organisational scenarios.
Different classes of metrics
We distinguish between various types of metrics such as:
Snapshot metrics vs. Dynamics metrics
Snapshot metrics give the status of an entity [project or organisation] at any instant of time. Dynamics metrics, as the name implies, give the evolution of the entity with respect to time. Time can be of the order or days, weeks, months, quarters or years.
Categorical metrics vs. Quantitative 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. On the other hand, quantitative metrics are calculated from precise information / values.
Volumetry metrics vs. Earned Business Value (EBV) metrics
This is pertaining to quantitative metrics, as well as qualitative aspects of an entity that can yield / give rise to quantised values. Certain metrics (primarily ratios) have the possibility to be measured with respect to data volumes as well as the Earned Business Value (EBV) contributed by data.
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