As the importance of AI grows, so does the clamour for improvements in data quality. You’ve probably heard:

‘If you want to get the best out of AI, then you need to get your data in order”

“Your output from AI is only as good as the data you put in”.

Of course, all of this is true, the symbiotic relationship between Data and AI is absolute. However, this strong co-dependency between data and AI can also work in the other direction because AI itself can be used to fix your data.

Data is the lifeblood of AI (Depending)

AI is fundamentally dependent on data because of the core element of Machine Learning that underpins every AI application. Data provides the raw material from which AI systems learn, adapt, and evolve through reinforcement training and additions to the underlying model’s parameters. Without large amounts of high-quality data, AI algorithms cannot perform their tasks effectively, so this high dependency means the quality and quantity of your data really does matter.

Cleaning Up Muddy Data Puddles (Mending)

But what if your data isn’t in good shape? You certainly aren’t alone! There are very few organisations with perfect sets of data ready to feed new AI systems because most data is generated from a system of complex interactions, so much so that it’s difficult to understand how the whole ecosystem can function. ‘Bad’ data can be generated from broken or inadequate systems or processes therefore remodelling may be required so that AI algorithms are not trained incorrectly.

To clean up your data and improve the quality, consistency, and accessibility it’s important to address any ’muddy data puddles’ – situations where data is incomplete, inconsistent, or erroneous, because this muddy data can significantly impair the effectiveness of AI models, leading to flawed insights and decisions. In our view, not mitigating this risk is unacceptable given that data regulations are clear on the importance of avoiding bias, discrimination and other online ‘harms’, be they financial, legal, medical or otherwise. Whichever of these apply could also have a drastic and detrimental impact on your reputation.

Cleaning up these data puddles involves several steps including: data profiling to understand the scope of issues, data cleansing to correct errors and inconsistencies, and data standardisation to ensure uniformity across datasets. Often this process requires multifunctional ‘treatment’ teams who can resolve the various operational issues that data treatment may cause.

Using Synthetic Data (Appending)

Here’s where AI can help. You can flow a model piece of data through a Generative AI system to teach it what ‘good’ data looks like. Via learning the AI can create and add synthetic data to your data to match or partially match against the good model and ultimately replace the bits of your data that are broken, allowing you to redeem these muddy puddles and make the data clean enough to be used.

“Our data modelling specialist partner, DesignChain see great potential in this use case for AI.” Damon Harding

Synthetic data can also be used to test and experiment with a new product / service scenario on existing operating and business systems. The days of releasing new things without being able to predict the impact on the incumbent environment need no longer be a headache for operational leaders. AI’s ability to quickly make predictions and forecasts allows for faster decision making and enables innovation via rapid release and test cycles.

Cycle of improvement (Trending)

Whilst we know that good data fuels AI through ongoing learning, AI also enhances how we utilise data and this co-dependent relationship creates a cycle of continuous improvement.

AI, which can now be embedded within individual laptops or PCs, can process and analyse vast datasets far more quickly and accurately than human capabilities allow, uncovering patterns, trends, and insights that would otherwise remain hidden. This enhanced data utilisation brings you valuable new insight, informed decision-making and ultimately better outcomes.

For example, in the legal sector, AI-powered tools can analyse past case outcomes to predict the likelihood of success in litigation, enabling lawyers to advise clients more accurately. Government authorities and bodies can use AI to analyse public feedback and social media data to identify community needs and tailor services accordingly or analyse economic data to forecast trends and inform policy decisions.

Data and AI Ethics (Intending)

The relationship between data and AI is both profound and transformative and for senior leaders the ability to leverage this relationship can unlock significant opportunities for innovation, productivity and efficiency, as well as some risks to be avoided.

However, it is important to have a plan to not only clean up your data but also navigate the ethical and practical challenges that arise, ensuring that AI is implemented responsibly, effectively and with full accountability. Therefore, developing robust data management and governance practices are a foundational element if you intend to let AI use your data.

Bias needs to be addressed via impact assessments, balancing exercises, and built-in human checks. It’s important that you can explain the rationale for every AI-made decision in straightforward language to your customers and the public.

Good preparation is worth it (Transcending)

Investing in high-quality data and promoting transparency and accountability are the keys to harnessing the full strategic potential of AI. By doing so, you can not only enhance your operations and services but also build trust and confidence with your customers and other stakeholders, paving the way for a better experience and relationship.