There is no doubt that artificial intelligence (AI) is a new frontier in today’s modern business environment. The challenge that many businesses face is having the right AI-ready data. Recent Gartner research indicates that over 60% of AI projects fail, largely due to improper data preparation.

In my experience, many CIOs underestimate the scope of data collection and management their organizations need to engage in. From the prospects that we spoke with in 2024, 30% to 50% of organizations lack sufficient data processes to embark on AI initiatives.

At DataGOL - When we engage with clients on AI Implementation, it’s mostly to try and create a journey with the right expectations. It's a journey of AI transformation but starts with data transformation and data culture first. 

The Core Issue: A Fundamental Misunderstanding of "AI-Ready Data"

The biggest barrier tends to come from miscommunication around what “AI-ready data” refers to. Having data ready for AI use does not simply mean meeting the standard quality check; this is why we at DataGOL focus on small data first rather than boiling the data ocean. Contact me and we will have a chat on this.

Traditional Vs AI-Driven Data Needs: While the data practitioner approach is to work with good data, if you want AI to learn to work in your environment, it needs to make sense of whatever your data is. The approach taken in traditional data management relies on working with cleaned, structured, and standardized data that is typically displayed in the form of reports and dashboards that are human friendly. The aim here is to reduce variation and outliers to provide the simplest form of view. On the other end of the spectrum, AI, and specifically machine learning algorithms, performs well with subtle datasets. It is essential for them to be exposed to real-life data, complete with errors, outliers, and surprising values. These deviations from an ideal line are not noise but are essential for the AI's learning and generalization process. 

Consider this: If you wish to train an AI for the purpose of detecting fraudulent transactions, then it will require contradictory data sets for authentic and fraudulent transactions, along with all the intricate details and unusual patterns that distinguish them from each other. Combining and altering the data or feeding it raw data devoid of any imperfections will not prepare AI to deal with the chaotic untidy real world. 

The "Fit-for-Purpose" Approach: Data that is AI-ready is not the same for every use case. Rather, it has to be “Fit-for-purpose,” which means it is customized to an AI use case. There are many different ways one can describe how contextual, quantitative, and qualitative data is needed and it all depends so much on the AI method and what the AI problem is.

A good example: is the case of a computer vision model that aims to detect objects in images. This model will need different data than a natural language processing model that parses texts. AI-ready data is contextually defined which means it changes with the given AI purpose and method. It can be argued that this fact pushes businesses further from the thought of a common benchmark for data quality toward an emphasis on optimal data quality relative to its purpose.

The Dynamic Nature of AI-Ready Data: Another contrast is within the data management scope. Sometimes, in classical approaches, data is considered an asset that is fixed and prepared once. However, achieving AI-ready data is not a singular endeavour; it is an ongoing, iterative one. There is a hidden governance net under the constant change and refinement of requirements that comes with the deployment and training of AI models.

In conclusion,

@DataGOL, we pay attention to the fact that “Data is not something built once, but a continuous process.” This means organizations must adopt more fluid and dynamic approaches to managing data because AI projects will always have ever-changing requirements. The dynamic nature of AI-ready data has an AI-specific focus, which means the data requirements will constantly shift based on the specific use case and type of artificial intelligence involved.