AI and the importance of data readiness
The last few years has seen enormous buzz around the capabilities of generative AI–and the benefits for companies who use it right—with some coining it the next industrial revolution.
Analysts predict that the cumulative volume of spending on AI will reach 19.9 trillion dollars worldwide between 2024 and 2030 (source: IDC 2024). However, there is one important element that can dampen all that excitement: your data. The impact of your generative AI solution will live and die by the quality of data it is provided. Therefore, the foundation to a successful generative AI initiative is a well-formed and governed data strategy that determines how data is collected, stored, managed, and used to achieve your business goals.
Foundations for success
Every company has unique data, along with unique goals, visions, objectives and needs, and therefore there is no cookie-cutter data strategy that can be successfully applied. However, a fundamental principle is that bringing together your data into a well-governed and consumable format is essential to for reaping the benefits of your data and generative AI initiatives. Many organisations see data governance initiatives as an unnecessary burden rather than a strategic advantage. This means proper data governance is deprioritised or lacking in most cases. The result of this is inconsistent and inaccurate data, potential compliance breaches, operational inefficiencies and, overall, a reduced trust in the available data. Companies have gotten by and lived with poor data governance for a long time. However, those companies that have implemented measures to improve the quality of their data have found it to be a key component in their AI strategy.
Assessing and improving data quality
The first step is understanding the current state of your data which is typically siloed, in many different formats, fragmented, and inconsistent. In some cases, the data is simply incorrect. Additionally, companies must also comply with regulations that governs how organisations handle and process different types of data including how they consume, produce and distribute it. Poor quality and unreliable data ultimately leads to poor AI outputs: garbage in, garbage out.
Worse still, companies have to grapple with a skills gap in data and AI. A recent study by Deloitte on the integration of generative artificial intelligence in German companies states: While 91 per cent of the managers surveyed forecast a noticeable increase in productivity as a result of AI, 41 per cent are confronted with a serious shortage of qualified specialists. Whilst some companies attempt to upskill internally, others turn to Managed Service Providers (MSPs), like Claranet, who have the required expertise and skills to fill the gap.
Moving forward
Despite these challenges, effective data management is going to be a critical element for any business aspiring to adopt an AI-first approach. Prioritising data readiness and making the necessary investments in people and infrastructure will cultivate more data-driven businesses that will pave the way for both successful data-initiatives and successful AI implementations. Whilst generative AI holds a lot of promise and potential, its success is linked to the quality and readiness of the underlying data it will be using. By addressing the data governance and quality issues head-on, organisations can begin to unlock the full potential of their data and AI initiatives.
