It’s a cliché because it’s true − data truly is the new gold. Organisations of all types − large corporates, SMMEs, non-profits and government entities − all agree that the ability to analyse data and draw fact-based conclusions from it constitutes the basis for success in an increasingly dynamic world.
The potential value of data to drive effective decision-making has long been recognised, but business’s ability to realise that value has lagged. One of the important reasons for this inability truly to become data-driven is the fact that data itself is extremely difficult to deal with.
Our organisations generate increasing volumes of the stuff, as does the rest of the world, but how to ensure it is accurate (or clean) and accessible?
Consequently, one of the important first steps in becoming a data-driven organisation is to understand how mature your organisation is in terms of data management and capability for using data.
One of the best sources of information is to observe what organisations that successfully navigated the journey to data maturity did. A key technique is also to take advantage of the various maturity model assessments that exist to provide a structured approach to assess the current state, plan future improvements and measure progress across all relevant perspectives.
Studying the experiences of organisations that have achieved data maturity will help to benchmark progress and avoid common pitfalls.
By undertaking a formal process of assessment followed by strategising, organisations go beyond just evaluating where they are, to committing to an ongoing process of introspection, evolution, and ultimately, innovation.
Five pillars of data and analytics maturity assessment
These five elements must be rigorously assessed at the beginning of the process. The process of evaluating data maturity is greatly assisted by accessing insights from successful adopters.
1. Strategy and vision: Organisations that have successfully matured their data and analytics capabilities emphasise the importance of aligning data strategy with overall business objectives. Doing so ensures data is not only used for operational efficiency but also for strategic decision-making and innovation. A comprehensive assessment grades the organisation's alignment of data strategy with business goals and the strength of executive sponsorship.
2. Organisation, people and culture: Successful adopters recognise the need for a culture that values data-driven insights and fosters collaboration between business and analytics teams. They define data as a strategic asset, ensure strong sponsorship from executive leaders; they establish cross-functional teams where data scientists and business analysts work closely with product managers and engineers to drive data-driven decision-making at all levels of the organisation. Key assessment criteria include the presence of a centralised data and analytics team, and the level of integration between data and business functions.
3. Technology: Successful organisations invest heavily in building scalable data infrastructure and leveraging cutting-edge analytics tools to extract actionable insights from vast amounts of data. Organisations should invest in intuitive data handling and analytics tools to unlock the full potential of their data assets. Assessing the organisation’s technology will typically focus on the sophistication of the organisation's information architecture, the availability of intuitive data handling and analytics tools, and how well data science tools are used.
4. Analytics: Analytics goes beyond generating reports but seeks to provide actionable insights. Successful adopters use advanced analytics techniques, such as machine learning and predictive modelling, to anticipate customer behaviour, optimise operations and drive business growth. Analytics maturity hinges on the organisation's ability to move beyond descriptive analytics to more advanced capabilities like predictive and prescriptive analytics.
5. Data governance: Any organisation wanting to advance its data capabilities must pay keen attention to the quality of its data, and how it manages and governs it. Successful data users implement rigorous data quality processes and establish data governance frameworks to ensure data remains accurate, reliable and secure. Important factors to assess include data quality management practices, data governance policies and procedures, and the effectiveness of data stewardship processes.
This process of assessing where your organisation stands in each of the five pillars yields useful results. For example, if the strategy and vision pillar has a higher grade than technology, analytics and data management, it is likely top management has a proper vision, but IT most probably is not delivering.
Conversely, if the last three pillars are higher graded, we can infer the opposite − that IT is implementing various solutions relating to data, but there is a lack of proper adoption from the business side.
Getting help from existing frameworks and models
There are several established frameworks and models that can be used for evaluating your organisation’s data and analytics maturity. The most common of these are:
- Gartner's Data & Analytics Maturity Model.
- The Data Management Association International’s Data Management Body of Knowledge.
- The Data Maturity Model by the Data Warehousing Institute.
Other frameworks and models from MIT Sloan, Deloitte, Forrester and McKinsey exist. All of them are good guidelines, but with different emphases.
In my experience with many clients, I have found that taking a pragmatic approach is best – this needs to be based on the organisation’s needs, borrowing elements from the frameworks as makes sense and apply it wisely to the five pillars in order to deliver what the organisation considers to be business value.
Charting the path to data mastery
Armed with insights from successful adopters and a graded assessment of their current state across the five pillars, organisations can develop a roadmap tailored to their unique needs and challenges.
Additionally, studying the experiences of organisations that have achieved data maturity will help to benchmark progress and avoid common pitfalls. As always, best practices offer a structured way to address challenges and complexities and allow the organisation to grow in data maturity.
My experience is that while consulting can play a role at the beginning, with the right level of training, most organisations become both self-sufficient and successful at using their data effectively.
The data maturity journey is one towards mastery, a state in which data and analytics are integral to the way the business as a whole, and every process within it, works.
Data-driven insights will drive innovation, inform decision-making and ultimately lead to growth. However, one needs to be clear this is not a journey with a finite goal; the world we live in is highly dynamic and becoming more so every day. Technology and the business environment are in a cycle of continual change, and thus the organisation’s strategy and how it is executed have to be similarly dynamic.
It's not enough to see data as a resource, we must embrace it as a force of transformation capable of reshaping entire industries and driving growth. This shift in mindset is essential for sustained success.
Share