Maturity Models: Analytics Capabilities and Data Maturity as the Foundation of a Data Centre of Excellence.

Over the past few weeks, we have been working on refining the distinctions within maturity models, specifically in the context of using them as a framework for assessing a range of organisational capabilities. Our focus has been to draw out the distinctions between Analytics Maturity Models and Data Maturity Models, both to clarify their differences and terminology, so we may better understand how these models serve separate yet interconnected purposes.

Our exploration is building on the foundational work of Becker et al. (2009), Davenport and Prusak (2000), Raber et al. (2012) and Spruit and Pietzka (2014) to name a few, and the many factors that need to be considered when building and using maturity modelling as an assessment of organisational analytics capability or data quality. Emerging insights from these papers are guiding us in how we think about mapping distinct maturity pathways, and how we articulate analytics maturity as being that which focuses on an organisation's ability to derive insights and data maturity as the essential foundation upon which those insights are constructed.

A Model for Qualitative Insights

Our goal is to develop a cohesive methodology and a comprehensive maturity model that integrates both analytic and data maturity. While achieving this may be ambitious, it remains a clear goal. In this way we have been discussing how we might build a common framework of understanding, both in the terminology and responsibilities assigned to human capital intelligence functions, such as People Analytics, Talent Intelligence and Strategic Workforce Planning, to name a few. In the first instance we need to research analytics and data maturity as distinct categories so that we might logically progress to building out the theoretical argument for our Centralised Data Centre of Excellence methodology.

Analytics Maturity: Clarifying Functions

We are in the early stages of developing a comprehensive glossary of key terms for organisational analytics. This is a significant undertaking, and without a structured framework to guide our thinking, it would be easy to get stuck in repetitive details and disconnected themes. These themes need to be anchored and systematically organised to avoid a fragmented approach. To support this effort, we are exploring existing analytics maturity models as a foundation for creating a unified language. This work is still in progress and is being steadily shaped through a literature review, which outlines each pillar of analytics intelligence and the associated methodologies.

Data Maturity: Core Conditions of Data Quality

As part of our research into data maturity, we have been examining the fundamental principles of data quality and the challenges organisations face in maintaining it. Our findings highlight a number of critical conditions that underpin data quality and common pitfalls that can undermine it. These include, but are not limited to:

1. Inconsistent data, which often arises from the use of multiple sources, processes, or storage locations, as ensuring uniform updates across copies is difficult.

2. Subjective judgement performed as data.

3. Challenges of balancing security, privacy, and accessibility, in particular when enhancing one results in compromising the others.

4. Interpreting data coded for different disciplines.

5. Managing complex non-numeric data (see 2).

6. Overly restrictive input rules that may exclude valuable data, while bypassing such rules can lead to inaccuracies.

7. Distributed systems lacking integration, producing inconsistencies in data definitions, formats, and values, distorting the original meaning of information.

These conditions highlight the foundational challenge of building a Centralised Data Centre of Excellence, as without taking these factors into consideration organisations will not be able to effectively mature and centralise their analytics functions. Tackling this problem academically, in the first instance, is vital for advancing the data and analytics maturity knowledge and understanding that is needed, so we might provide a solid foundation for effective action.

In the coming weeks, we will delve deeper into these conditions of data quality, unpacking their implications and exploring how they might interact with broader organisational challenges. By analysing each condition in detail, we aim to gain a richer understanding of their role in shaping data maturity and their critical impact on analytics maturity. Through this exploration, we hope to uncover actionable insights that will inform our methodology and help organisations build stronger, more integrated foundations data and analytics functions.

We are loving this opportunity to collectively research this complex yet essential landscape, both to systematically refine our approach to these interesting questions and to contribute to advancing the field.

Join us in this work - all voices are welcome! :)