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High-quality, accurate data is the foundation for all the growth and innovation we’re seeing in the digital environment. Many people are now using the metaphor “Data is the oil of the digital age”, because it’s the inherent quality of data that creates a rich foundation for all the systems, platforms, and applications we’re all using in our digital transformation strategies.
At the outset of any digital transformation program, it’s standard practice to ensure the data is accurate, complete, consistent, and relevant. This includes verifying the sources of data, addressing any data entry errors, removing duplicate or irrelevant data, and performing data cleansing and validation processes.
This is even more critical if you are looking to embrace generative AI technologies to use your own data, because if you skip this step, the responses given by your very expensive corporate GenAI model will be inaccurate, outdated, and inconsistent.
But, as important as this ‘data validation’ process is, it doesn’t deliver insight to the maturity of your organisation in relation to your ability to leverage data for strategic decision-making, innovation, operational efficiency, and competitive advantage.
To do this, we need a broader understanding of the organisation’s effectiveness in using data. And we do this through a Data Landscape Maturity Assessment (DLMA).
Unlike other maturity assessments, which can be purely technology focused, the DLMA provides a 360-degree view on the intended use of data within your organisation. By considering the people in your organisation who are using your data, how they’re doing this within their roles, and the level of sophistication and efficacy in the processes employed in leveraging the data, we get a more user-based view of the data.
The DLMA also provides a comprehensive review of the data landscape. This includes examining the data source catalogue and its descriptions, evaluating data quality, assessing the ETL (Extract, Transform, Load) pipeline catalogue, and analysing data usage patterns.
Delving deeper into the technology provides an assessment of the systems, products, and tools that utilise the data, and examines the specific purposes we place on the technology in relation to the data. This evaluation delivers deep insight to the intricate relationship between data and the technology.
Finally, a review of the strategic goals the organisation has with its data gives us an end-goal or future state we can head towards. This forms the foundation of our strategic transformational roadmap, which aligns our people, data, and technology with our organisation’s future-state objectives. More on this later.
There are five levels of data maturity around which we can build some clear definitions.
This is the minimal level of data maturity for an organisation to function. It indicates large gaps in accuracy, quality, fidelity, and availability. If the data is left in this state, these gaps will hinder aspects of the organisation’s operations.
A pretty good result, showing there’s a sufficient level of maturity to perform business operational tasks and assessments with reasonable accuracy. There are still some gaps in data, along with some availability, quality, and fidelity issues, but they’re not going to interfere with operations.
This indicates that an increased state of automation, data accuracy, and accessibility is beginning to drive business efficiencies within data gathering and decision making.
Although some level of subjective analysis is required, the core systems are integrated, and data sources can be easily accessed from a central location.
At this level, things are really starting to look good. We’re seeing a ‘high’ to ‘very high’ level of automation, data accuracy, and accessibility. Also, we’re seeing the use of tools performing analysis autonomously.
Core systems, secondary systems, and public data sources are integrated and centrally available. Decision making can rely almost entirely on the data that’s available, with actions being executed with a good level of confidence.
This stage has minimal reliance on subjective analysis and a limited predictive view of future events.
This is as good as it gets. We’re seeing a very high level of automation, data accuracy, and accessibility with full trust in the data that’s available.
Existing data sources can be extrapolated into new data sources for the purposes of predicting future events, both abstract and direct relationships.
Larger amounts of data are being processed with Machine Learning technologies that can indicate many future event patterns.
For automation, data, and AI to deliver meaningful outcomes, we need to see the organisation maturing in its ability to leverage data for strategic decision-making, innovation, operational efficiency, and competitive advantage-related programs.
This level of maturity isn’t just about technology. It requires end-to-end collaboration and cooperation throughout the business, which means getting everyone in your organisation aligned with your long-term technology, operational, and cultural objectives around automation, data, and AI.
The output of the data maturity assessment, therefore, is delivered in the form of a roadmap of projects, with cost and duration estimates required to transform and elevate your organisation.
This roadmap shows where your data can be used to deliver the outcomes you need now, as well as what your organisation needs to deliver the desired outcomes in the future. And because all this needs to happen while complying to your own governance standards and relevant regulatory requirements, the roadmap covers these important issues as well.
What this roadmap looks like in practice is different for every organisation. But if an organisation is currently assessed to be at a ‘minimum’ level of data maturity, to get it to ‘streamlined’ we might be looking at robotic process automation bots to automate manual processes, or the integration of core systems.
If an organisation is already ‘Streamlined’, we might look at predictive analytics, modern data management, and improved customer services through automated online experiences to bring it towards a ‘Data Driven’ level of maturity.
The Data Landscape Maturity Assessment is the starting block for digital transformation projects. In relation to automation and generative AI, because this has an ‘innovative and transformative’ level of data maturity associated with it, organisations need to ensure they’ve evolved their data to this level of maturity before they start leveraging it.
Taking this step-by-step approach, you’ll establish an interconnected relationship between your people, processes, systems, and the culture your organisation is creating around the use of automation, data, and AI. With the right infrastructure in place and the right level of data quality, you’ll be able start down the road of data-led digital transformation with more confidence and certainty.
As always, the team at Tecala is ready to partner with you as you commence this exciting journey.
Tecala is leading the way in intelligent automation in the mid-market space. Most of the leadership teams we’re talking to are truly committed to ensuring that technology does, in all honesty, empower people and enrich their daily lives.
We believe that at the intersection of automation, data, and AI, there are solutions for mid-market organisations that truly amplify human potential. Using intelligent automation, AI, and high-integrity data, we are helping our clients drive efficiency, performance, and profitability at every level of their operations.
Tecala's Data Landscape and Maturity Assessment (DLMA) is the first step in ensuring effective use of your data. In a three-step approach we assess the current state of your data, formulate the future state based on your goals, and provide a gap analysis between where you are now and where you need to go.
In this update, we provide an overview of the changes to the Essential Eight Maturity Model, and provide you with an effective roadmap to maintaining compliance.
Introducing Tecala GPT – our AI as a Service platform that enables the mid-market to embrace generative AI in a safe and secure way.
