In a world where data plays a crucial role in decision-making, there is a need to assess and improve the capacity of government institutions to manage data effectively. The Data Maturity Matrix is a web-based application that assesses the current state of data capabilities in government areas and provides recommendations for improvement.
Illustration: Micaela Nanni
Data maturity matrix
What does this involve?
1
online questionnaire
10
minutes
41
questions
10
dimensions of analysis
6
niveles de madurez
1
downloadable report
From the processing of the questionnaire responses, a downloadable data maturity report is automatically generated with metrics and recommendations on steps the organisation could take to move forward on data governance.
This report derived from the matrix provides quantitative and qualitative results.
- Quantitative results:
- Summary graph describing the degree of maturity in each dimension.
- Detailed numerical scores in each dimension assessed provide an objective view of the current situation.
- Qualitative results:
- Qualitative analysis interprets these scores, contextualises the information, identifies achievements and areas for improvement, and provides a broader and more contextualised understanding of the particularities of the assessed area.
- Recommendations and suggested actions, based on the detailed results of the matrix, become a precise roadmap for implementing changes and enhancing the performance of the assessed area.
This process is supported by Fundar, both in the application of the matrix and in the review of the results.
Why use the data maturity matrix?
The purpose is to provide an accurate tool to assess maturity related to the different dimensions of data governance.
It is a diagnostic instrument, i.e. it is not intended to control processes or assess the performance of work teams. Its main function is to serve as a starting point for the areas involved to identify possible improvements and act on the recommendations provided.
It can be applied for
descriptive if used as a diagnostic tool so that assigned maturity levels can be reported to stakeholders;
prescriptive if used to identify desirable maturity levels and to provide guidelines on what improvement measures to implement;
both.
Who can apply the data maturity matrix?
- Ideally, respondents should be technical data managers with knowledge of governance definitions.
- This matrix is specially designed for public sector institutions, be they government agencies or non-profit organisations.
- It is designed to be adaptable to the diverse existing realities and the heterogeneity of data areas in different public organisations. It can be applied at different levels of government (municipal, provincial, national and international), different areas (ministries, secretariats, sub-secretariats, government areas, etc.), beyond the areas of work (health, environment, tourism, etc.).
Assessing the level of data maturity
Our data maturity matrix consists of three components: 1) the questions asked, 2) the various dimensions assessed and 3) the scores assigned to the answers provided. These scores determine the maturity levels achieved in each dimension.
41 questions asked
The test consists of 41 questions, all of which have pre-determined answer options. Each answer option is associated with a specific score.
10 dimensions assessed
The questions are further divided into 10 dimensions to be surveyed:
- Source of information and data integration: Data sources used and integrations necessary for the generation of information/reports.
- Data science: Products developed from different data science techniques ranging from transformation, analysis and visualisation to advanced techniques such as machine learning.
- Report updating: Modality and frequency of updating of the products and services generated.
- Data availability: Modality for making information, products and services available to the rest of the government areas and the community.
- Data protection: Level of regulatory compliance concerning data protection and the area’s proactivity in the matter.
- Data governance: Knowledge and mastery over the lifecycle of own and transferred data.
- Data access management: Administration and monitoring of accesses and users to common repositories.
- Data quality: Data quality controls and analysis that allow the usefulness of the data for decision-making.
- Data reuse: The degree of data sharing within an organisation.
- Data model: The level of progress an organisation has made in the design of the data it holds.
6 levels of data maturity (and assigned scores)
Six maturity levels are established and each maturity level has an associated score.
- Level 0 (Incipient): data exists, but does not represent an asset for decision-making within the organisation.
- Level 1 (Initial): data is manipulated as a requirement to do projects.
- Level 2 (Basic): there is an awareness of the importance of manipulating data as a key asset.
- Level 3 (Intermediate): data is considered at an organisational level as highly relevant to the organisation for a successful outcome.
- Level 4 (Advanced): the possession of information is an advantage in decision-making.
- Level 5 (Expert): includes knowledge management mechanisms, ways of learning from actions, audit and process review mechanisms, as well as dissemination of best practices to foster continuous improvement.
In turn, each question is weighted according to its importance within the corresponding dimension. The dimensions are independent of each other. Therefore, a governmental area or organisation can show a higher level of progress in one dimension and a lower level in another, without this being contradictory.
The sum of the scores of the responses in each dimension determines the level of that dimension. In addition, the matrix calculates an overall result by adding up the scores obtained in all dimensions. Furthermore, the application generates descriptive texts based on these scores, thus providing a detailed picture of maturity in each assessed area.
