The Data Maturity Model – Key Concepts
We start with some definitions of the elements used for introducing and explaining the different possible stages.
An entity can be a project or a subsidiary or business division, or an entire organisation.
Data can be any form of information and/or knowledge. There are different formats and forms. Below are some examples:
- File Systems and Databases: This is the most generic form of information. Files of information generated by IT systems. They store information as files – which can be anything: performance data, sales data, financial data, project data, operational data, etc. These are linear forms of data – i.e. they require simple IT systems to store and retrieve information from them.
- Knowledge and Decision-making data: These are non-linear forms of data. Part of them can be formalised and documented, while a considerable part rests in the mental understanding of personnel involved, or in general in the industry. As a consequence, such forms of data require more complex IT frameworks to store and replicate them. Applications and usage is on a case-by-case basis. Analytics tools and rule-based decision-support systems prove helpful in mapping such complex non-linear information onto linear systems (as explained above).
- Domain-specific knowhow: This is a further non-linear form of data. This is knowledge and/or intelligence that is intrinsic to a person and to a specific domain or context. For e.g. running a machine, learning a complex workflow, predicting future outcomes based on past experience, observing/analysing a sequence of steps to understand the final result. Not only is it difficult to impersonate such knowledge, it is also increasing difficult to generalise the intelligence to multiple domains. Modern Machine Learning solutions as well as Artificial Intelligence frameworks are becoming increasingly effective to support humans in this direction.
Different stages of Maturity
In the Data Maturity Model [DMM], an entity can be classified in any of the four different stages, as shown below:
Further details of these four stages are as follows:
- Data-agnostic: This is the initial stage of the DMM at which an entity can be. If an entity is in this stage, it has the following characteristics:
Data is used in a generic manner, for elementary operations.
Data usage does not affect business / project success or failure.
There are no specific tools developed for the usage of data. Any off-the- shelf tools are used for this purpose.
- Data-sensitive: At this stage of maturity, an entity – be it an organisation or a project or a business division – recognises the usage of data for explaining the current situation of the entity, primarily for reporting purposes. For e.g. the business performance of an organisation or subsidiary, the degree of success of a project, the satisfaction of users / clients, etc.
Nevertheless, data is used to explain a business scenario « à posteriori ». It is not yet used to improve the performance, etc. We refer to this type of usage as read-only usage.
Mis-usage or non-usage of data may not result directly in business or project mal-performances or failures. Nevertheless, it may lead to miscommunications, which can then be the cause of future failures.
- Data-oriented: At this stage of maturity, data is used to model and forecast business and / or project performances. Nevertheless, data is treated as a means to a business end. Data is not used to « create » new value.
Starting from this stage onwards, data is used to model business scenarios « à priori » and forecast business and project performances based on the corresponding modelisation.
As a consequence, mis-usage or non-usage of data may result in business or project mal-performances or even failures.
Data is still treated separately as an accessory, tools towards a business end. Data per se does not create any new value.
- Data-driven: In this terminal stage, data becomes a « key business driver ». It is no longer treated as an accessory, rather integrated in the business as a raw material and/or a finished product, whatever be the domaine or industry.
Decisions are made on the basis of data. Certain decision-making can even be automated because they are dependent on data and knowledge in one form or another.
Value-add using Data: In addition, data generates further value in the chain by creating supplementary businesses. Data is systematically reused / recycled / revalued in the business cycle to drive new businesses and projects.
Certain examples can be provided from projects and consulting assignments that I have led using the Data Maturity Model. For this purpose, please feel free to contact me by email.
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