<img height="1" width="1" style="display:none;" alt="" src="https://px.ads.linkedin.com/collect/?pid=4958233&amp;fmt=gif">
RSS Feed

Insights Through Data | Adina Gabriela Stavar |
12 January 2021

It has generally been acknowledged that data is everything. And while it may be everything, it needs time to mature within an organisation, so that it can grow from just an asset into a competitive advantage. Thus, it needs a model to rely on in the process.

A Data Maturity Model is the foundational guideline that establishes the best practices of enterprise data management, with a focus on defining, implementing, improving, and evaluating data across an organisation.

Defined by different layers of maturity, the approach towards data starts with building awareness that data has become paramount to running even the most isolated business processes and ends with extracting the most relevant insights from data, with a view to improving business performance.

Data Maturity Model


At the first layer of maturity, the approach to data is rather reactive and isolated inside each specific project. At this point, data may exist in a raw state, unprocessed, uncleaned, unconsolidated, or not taken care of. Some data processes may be well defined inside each project, but they are not transmitted across the organisation, resulting in a non-strategic view of the data. This could result in suboptimal decisions on the implementation of goals to be achieved, workflows to be enforced, and tools to be used, which could impact the organisation in the long term.

Understanding the data, gathering the relevant business requirements and criteria, and documenting the entities, processes and their relationships into data models and diagrams will enable the organisation to define the role that data has for it as well as a long-term data plan.


After raising awareness of the importance of data, data becomes a critical asset, and defining a policy becomes imperative to manage it in a systematic manner. At this point, having consolidated data is the main objective. Involving relevant data roles and building capabilities empowers the organisation to establish practices and processes in a transparent manner. Architecture, analysis, engineering, integration, governance, and quality assurance of data constitute the recipe for the data management strategy. It is only through the cross-functional dependencies of these areas that data processes can improve and become optimal.

This entire layer of data maturity encompasses projects around the consolidation and integration of different data sources, leading to the creation of data warehouses, data lakes, and big data platforms, depending on the business needs. Data lakes and data warehouses are both widely used for storing big data, but with different objectives. A data lake is a vast pool of raw data, the purpose of which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose, one of them being reporting. As the variety of data types increases, along with the data volume within an organisation, it becomes imperative to increase the velocity at which the data must be processed. Thus, big data platforms with cloud computing capabilities should be explored at this stage.

All these solutions to store data and treat it properly as an asset constitute the solid basis for the next maturity layers.


After data consolidation, another shift happens at the third level of maturity where data management guidelines become the norm. Following them consistently allows for implementing the mindset at an organisational level, with data being treated as a scope in itself to ensure long-term success. At this point, having accurate, relevant, and clean data is the key.

Metadata management becomes mandatory, with data dictionaries, data catalogues, and business rules enabling the consolidation of key business terms, metrics, and data assets. Accompanied by data lineage and keeping traceability for audit trails, the organisation will be ready to implement data regulation practices. The entire data lifecycle is thus managed in a transparent manner, with clear documentation of how the data was created, used, and archived. On top of that, access rules close the data security gap, which is essential for how the data is valued at the next maturity levels.

By profiling the data, problems can be discovered early, and costs can be avoided in the long run. Issues with the uniqueness of keys, duplicated data, lack of data validation upon input, and missing, misleading or improperly formatted data could harm the next stages of generating knowledge out of data.


As data now has value beyond the asset that it represents, it can become the source of insights that leverage the ultimate advantage in a competitive context. At the fourth layer of maturity, analysing the data through statistical and quantitative techniques uncovers paths unforeseen before.

At this level, it is very important to make the data visible to the relevant audience within the organisation. Focusing on both analytic and operational reports, generating relevant insights is the main objective.

Descriptive analytics is leading the way now, be it custom or self-serviced, in the form of consolidated reports or ad-hoc analyses. Backing up these visualisations with a solid reporting strategy will allow the relevant business stakeholders to make the right decision at the right moment.


At the fifth level of maturity, data is seen as essential for identifying opportunities to improve processes in a competitive market.

Structured or unstructured, the data holds knowledge which can be extracted through scientific methods and algorithms. Predictive algorithms, such as regression and classification, can be used to predict future events and trends. Supervised algorithms can use prior knowledge to learn the relationship between the input and the output which can be observed in data. Unsupervised learning, while not relying on labelled output, must infer the natural structure already existent in the data.

Data needs that may arise at this maturity level can be related to predictive analytics, data classification and segmentation, and anomaly detection. Further insights can be extracted with natural language processing techniques. Any question that can be answered through machine learning, deep learning, and AI techniques sits within this ultimate layer.

Of course, sharing your best practices with the industry is also part of this final level of maturity.

As could be seen, a Data Maturity Model holds the journey from first encountering data to developing a long-term relationship with it that is here to stay.

Adina Gabriela Stavar

Senior Data Analysis Consultant

Adina is a highly experienced Data Analysis Consultant with 9+ years of experience in the Business & Data Analysis and Data Science fields, conducting and assessing the full data analysis flow: from business and data requirements to data exploration, modelling, mapping and documentation, from data quality assessment, profiling and cleaning to feature selection and engineering, model selection, evaluation and report creation. She coordinates data-driven initiatives, such as data migration, analytics, reporting, data science projects, translating data insights into business decisions and business decisions into data insights. When she is not searching for patterns everywhere, Adina enjoys enriching her database with new languages, psychological knowledge, handmade items and building houses.


From This Author



  • 13 November 2023

    Delving Deeper Into Generative AI: Unlocking Benefits and Opportunities

  • 07 November 2023

    Retrieval Augmented Generation: Combining LLMs, Task-chaining and Vector Databases

  • 19 September 2023

    The Rise of Vector Databases

  • 27 July 2023

    Large Language Models Automating the Enterprise – Part 2

  • 20 July 2023

    Large Language Models Automating the Enterprise – Part 1

  • 11 July 2023

    Boost Your Game’s Success with Tools – Part 2

  • 04 July 2023

    Boost Your Game’s Success with Tools – Part 1

  • 01 June 2023

    Challenges for Adopting AI Systems in Software Development

  • 07 March 2023

    Will AI Transform Even The Most Creative Professions?

  • 14 February 2023

    Generative AI: Technology of Tomorrow, Today

  • 25 January 2023

    The Joy and Challenge of being a Video Game Tester

  • 14 November 2022

    Can Software Really Be Green

  • 26 July 2022

    Is Data Mesh Going to Replace Centralised Repositories?

  • 09 June 2022

    A Spatial Analysis of the Covid-19 Infection and Its Determinants

  • 17 May 2022

    An R&D Project on AI in 3D Asset Creation for Games

  • 07 February 2022

    Using Two Cloud Vendors Side by Side – a Survey of Cost and Effort

  • 25 January 2022

    Scalable Microservices Architecture with .NET Made Easy – a Tutorial

  • 04 January 2022

    Create Production-Ready, Automated Deliverables Using a Build Pipeline for Games – Part 2

  • 23 November 2021

    How User Experience Design is Increasing ROI

  • 16 November 2021

    Create Production-Ready, Automated Deliverables Using a Build Pipeline for Games – Part 1

  • 19 October 2021

    A Basic Setup for Mass-Testing a Multiplayer Online Board Game

  • 24 August 2021

    EHR to HL7 FHIR Integration: The Software Developer’s Guide – Part 3

  • 20 July 2021

    EHR to HL7 FHIR Integration: The Software Developer’s Guide – Part 2

  • 29 June 2021

    EHR to HL7 FHIR Integration: The Software Developer’s Guide – Part 1

  • 08 June 2021

    Elasticsearch and Apache Lucene: Fundamentals Behind the Relevance Score

  • 27 May 2021

    Endava at NASA’s 2020 Space Apps Challenge

  • 27 January 2021

    Following the Patterns – The Rise of Neo4j and Graph Databases

  • 12 January 2021

    Data is Everything

  • 05 January 2021

    Distributed Agile – Closing the Gap Between the Product Owner and the Team – Part 3

  • 02 December 2020

    8 Tips for Sharing Technical Knowledge – Part 2

  • 12 November 2020

    8 Tips for Sharing Technical Knowledge – Part 1

  • 30 October 2020

    API Management

  • 22 September 2020

    Distributed Agile – Closing the Gap Between the Product Owner and the Team – Part 2

  • 25 August 2020

    Cloud Maturity Level: IaaS vs PaaS and SaaS – Part 2

  • 18 August 2020

    Cloud Maturity Level: IaaS vs PaaS and SaaS – Part 1

  • 08 July 2020

    A Virtual Hackathon Together with Microsoft

  • 30 June 2020

    Distributed safe PI planning

  • 09 June 2020

    The Twisted Concept of Securing Kubernetes Clusters – Part 2

  • 15 May 2020

    Performance and security testing shifting left

  • 30 April 2020

    AR & ML deployment in the wild – a story about friendly animals

  • 16 April 2020

    Cucumber: Automation Framework or Collaboration Tool?

  • 25 February 2020

    Challenges in creating relevant test data without using personally identifiable information

  • 04 January 2020

    Service Meshes – from Kubernetes service management to universal compute fabric

  • 10 December 2019

    AWS Serverless with Terraform – Best Practices

  • 05 November 2019

    The Twisted Concept of Securing Kubernetes Clusters

  • 01 October 2019

    Cognitive Computing Using Cloud-Based Resources II

  • 17 September 2019

    Cognitive Computing Using Cloud-Based Resources

  • 03 September 2019

    Creating A Visual Culture

  • 20 August 2019

    Extracting Data from Images in Presentations

  • 06 August 2019

    Evaluating the current testing trends

  • 23 July 2019

    11 Things I wish I knew before working with Terraform – part 2

  • 12 July 2019

    The Rising Cost of Poor Software Security

  • 09 July 2019

    Developing your Product Owner mindset

  • 25 June 2019

    11 Things I wish I knew before working with Terraform – part 1

  • 30 May 2019

    Microservices and Serverless Computing

  • 14 May 2019

    Edge Services

  • 30 April 2019

    Kubernetes Design Principles Part 1

  • 09 April 2019

    Keeping Up With The Norm In An Era Of Software Defined Everything

  • 25 February 2019

    Infrastructure as Code with Terraform

  • 11 February 2019

    Distributed Agile – Closing the Gap Between the Product Owner and the Team

  • 28 January 2019

    Internet Scale Architecture