From big to better data: putting AI trends into practice

Organisations have traditionally valued volume, velocity and variety as the classic drivers of ‘big data’. However, as we start to explore the promise of increased efficiency and creativity from generative AI, we must also address the remaining two ‘Vs’ – the value and validity of our data.

How can we trust all our data, in the generative AI economy? While trends in AI growth and its applications have taken centre stage, it is important to define a roadmap to putting these trends into practice across the organisation. In this way, it holds the potential to usher in a new era of productivity and prosperity.

Let’s look at some of these trends and how best to incorporate them into our business practices:


  1. Moving from historic to predictive analytics

Thinking that generative AI will replace all previous AI tools would be a mistake. Traditional AI has the potential to bridge the maturity gap in an organisation’s generative AI strategy, especially in well-established use-cases, such as fraud analytics and churn analysis.

A hybrid AI scenario can offer the best of both worlds: the predictability of traditional AI tools, and the flexibility, scalability and adaptability of generative AI models.

Don’t, however, become overly distracted by generative AI. Any initiative should be rooted in real-life business challenges, while aligning with, and amplifying, any ongoing data and analytics efforts.


  1. Empowering non-technical workers

Generative AI plays into the hands of individuals who want answers fast, but do not have the time or skills to perform analyses. As such, we will see an increase in auto-generated visualisations and insights, enhanced with explanations in natural language.

But collaboration and data sharing are key. While this will appease consumers’ tendency to trust people more than data, generative AI will also allow them to explore freely, and interact conversationally within the systems where they operate.


  1. Curating knowledge from unstructured data

Generative AI techniques will allow organisations to unlock the potential and value of unstructured data.  In fact, Forrester estimates that 80 percent of the world’s data is unstructured. Generative AI models, however, adapt to different data types and do not rely on predefined rules.

As such, the opportunities for combining structured and unstructured data in a trusted way will be endless. For example, using chatbots like ChatGPT for external use cases alongside more private chatbots where trusted enterprise data has been secured.


  1. From BI to AI, and back again

Generative AI is bringing about a change in the way businesses analyse their information. It can, for example, take advantage of solutions that place analytics needs where and how people work, and provide interactive analyses within workflows.

Individuals might start their analytical journey in generative AI tools, using them for simple data visualisation and business projections. As a next step, they may want to tap into enterprise-grade tools for deeper analysis, bringing the benefits of generative AI to their trusted tools.


  1. Data quality matters even more

While data quality and lineage mattered in the past, they have become non-negotiable in the AI world. Companies must be able to create the equivalent of a ‘DNA test’ for their data to trust its origin.

Solutions are emerging that offer pervasive data quality across platforms, the ability to continuously observe and quickly quantify data quality across datasets, data transformation and cleansing functions, and the ability to track data flows from source to use.


  1. AI bots must pass a ‘driver’s test’

Just as data literacy has been crucial in the last few years, we now need to turn our attention to AI literacy to improve standards, avoid governance chaos and application glut from ‘everyday’ developers. Power has shifted into the hands of the many and, as such, organisations must take steps to educate their workforce on the benefits and pitfalls of generative AI. 


  1. Data democracy and the end-to-end story

Innovation often comes from convergence between unexpected parts of the organisation. More powerful AI and automation capabilities can remove the need for advanced knowledge and tools that can shift a new breed of users out of their comfort zones.

By merging the roles and capabilities of data engineering, data science and analysis, for example, organisations can solve tougher problems with fewer resources. This also eliminates barriers between previously siloed functions to solve business problems.


  1. From analysis to execution

Connecting generative AI to automation shifts human focus to action. As data can be transformed in near-real time, and in the right place, new ways of using generative AI will emerge.

This reduces the burden of manual work, such as building workflows, and instead allows humans to focus on decision-making processes. However, automate incrementally as comfort grows by monitoring progress, adjusting and then automating more. And always keep humans in the loop.


  1. AI customisation

Applications of generative AI are currently generic. Over time, however, we’ll see more customisation around industry and business-to-business (B2B) use cases, with multi-cloud environments providing greater efficiency and stability.

With less effort and hours, as well as the ability to build customisations on a common foundation, greater speed and efficiencies can be realised to address specific industry needs.


  1. The data marketplace

We can apply the same principles of product management to data by asking questions about what problems we are solving, best use-case scenarios and by whom. This will emphasise the importance of data quality, governance and usability for end users.

Only then can we start transforming data assets into products for reuse internally as well as externally. In this way, our most valuable asset can also become a tradeable good.

In conclusion, our focus should always be on how well trust, security and regulatory pressure holds up in a new data paradigm. Better, more trusted data will ensure that the output of generative AI is not compromised by a lack of data traceability and quality.