A Journey to Data and AI Maturity

Data and AI Maturity is a journey that every organisation is on, whether they are aware or not. Data & AI Maturity is a spectrum which you can use to judge where you are on the journey from limited use of data, largely siloed and in Excel, to an accurate, enterprise wide Data platform that supports Data Analytics and Artificial Intelligence practices which seek to illuminate insights and enable every user to make a positive data-driven impact in their role.

The pains

"I struggle to make sense of our data because it's scattered across multiple systems, making it hard to access and analyse efficiently."

"Cleaning and preparing data is a never-ending battle, and it often feels like we spend more time on this than on actual analysis."

"I'm overwhelmed by the sheer volume of data we collect, and I'm not sure which metrics are truly valuable for our decision-making."

"We face challenges in integrating data from various departments, resulting in silos and a lack of cross-functional insights."

"Data governance is a headache, with no clear ownership or policies in place, making it difficult to maintain data quality and compliance."

“I’m not confident using our data analysis tool, I was given a day’s training a year ago and have no one to ask for help. I just export the data to excel and do my analysis there.”

"I'm concerned about the ethics of our data practices and whether we're using customer data responsibly and ethically."

If you have heard, or said, any of the above then you are on a journey with Data and AI maturity. All organisations are striving to be efficient, digitally capable and innovative in their field. Data is the backbone of this. We frequently talk about Data Driven Decisions, this is not a new term, but certainly one that has gained momentum year after year.

Data Driven

Data Driven businesses are simply businesses that are putting emphasis on using data to make better decisions in (and for) the future. This can be when a business uses yesterday’s sales figures to decide what products to put in an endcap. Or use prediction models to run scenarios against the A/W24 products available to work out which products would sell best against their customer base.

Using data available within your organization to make smarter choices is a journey. Some companies/departments/individuals are on different levels of that journey, but where you are on the spectrum isn’t important. What is valuable, is to understand WHY you are where you are and what the steps are to improve your maturity.

For those lower down the maturity scale, it may be that technology choices prevent adoption of newer, more powerful techniques. It could be that you don’t have skilled people in roles that would expedite data usage. Or, and more than likely, Data Governance is lacking within the organisation. If there are not organisationally aligned policies and processes in place to govern data, you will not be on the mature end of the data and analytics maturity spectrum.

Data Governance is about bringing together the crucial elements of successful Data and AI enablement.

What does Maturity look like?

Measuring Data and AI maturity is a proactive approach to managing and improving performance, reducing risks, and ensuring that the organisation is well-prepared to meet its strategic objectives in a competitive and evolving business environment. If we consider the technological advances and ever changing skillsets over the last 20 years, it becomes apparent how rapidly EVERY business has had to adapt.

Naturally, not ever change to ways of working will have been diligently planned, delivered and monitored. Now is the time to assess how your organisation works with Data and AI and how it can be improved as the rate of change is only ever increasing.

What happened?

When utilising data within a business, the first step is to have clean data, available and accessible. This will typically look like descriptive reporting – making available insight in to what has happened in your organisation in the previous day. It may also take the form of people exporting raw data from business applications and completing their own analysis in Excel. This stage of the maturity curve is about the ability of a business to analyse it’s historical data, meaning that the events leading to performance have already occurred and cannot be change to improve that performance.

Symptoms of descriptive reporting:

  • Data siloes – Individuals use the data that they can get to, using raw data exports to complete their own analysis. This leads to a lack of cross-functional reporting.

  • Skills gaps – Individuals in the organisation are forced to become data engineers/analysts, spending a large amount of time crafting data for analysis instead of analysing and acting upon insights.

  • No single version of the truth – As individuals use raw data exports to craft analysis, there is no governance to align KPI definitions or a consistent data transformation process, leading to conflicting versions of the truth.

What will happen?

When an organisation knows what has happened, the next step in maturity is to be able to use historical data to predict what will happen in future. This organisation has a firm grip on understanding patterns, identifying potential opportunities, and mitigating risks before they even materialize. Data is used to leverage business intelligence tools, machine learning and statistical models.

Symptoms of predictive modelling:

  • Reduce Risk – an organisation that can leverage data to predict patterns and trends before they happen, can mitigate risk. This can more specifically be applied to fraud detection, early detection and risk assessment.

  • Increase Efficiency – by leveraging predictive modelling, an organisation can identify inefficiencies and optimise processes quickly. It can also redirect resources or amend supply chain with advanced warning to improve efficiencies.

How should we respond and automate the decision making process?

Once an organisation has a strong data foundation and is leveraging predictive analytics, the next step is to use that data to start predicting the best way the organisation can responds to what will happen. By considering what has happened, what will happen and business processes, prescriptive analytics can offer scenarios to respond to what is soon to happen. This reduces the time that teams need to spend consider scenarios, talking them through and mapping out solutions. Instead, that time can be spent enacting the most logical solution.

The next step in the curve is to automate the decision making process, allowing our data platform to decide on the best solution to a problem, rather than choosing from a variety of scenarios. This removes possible human error or bias by simplifying the process and allowing the data of what has happened, what will happen influence how we respond.

Generative AI

Generative AI is at the pinnacle of the Data and AI maturity curve as it is the ability to use our data to create new content. This can be marketing materials, product designs or software code. However, to leverage our data to do these incredible things, it has to be trusted, accurate and ethically governed. As we cycle through an AI goldrush, it’s important not to jump in without considering if the organisation is ready.

Advancing Analytics can help you understand your data health and fuel innovation with our Data and Analytics Maturity Assessment. Get in touch to find out more!


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Nikki Kelly