Artificial Intelligence (AI) is no longer just a thing of science fiction. In fact, it has already become part and parcel of people’s everyday lives through GPS, predictive text, smart assistants, and more. This technology continues to progress, allowing for further capabilities in tackling complex computations and larger data sets.
Previously, TDAN discussed the importance of data in the world of AI. It established that the majority of businesses believe in the impact of AI more than other technologies, because its very nature allows it to consume and learn from data in a systematic way. Ayima further explains how AI works by diving into the specifics of how neural networks function.
These are statistical models inspired by the actual biological neural networks in all of us. Through initial training data, AI applications can then learn to process relationships between nonlinear inputs and outputs. Every AI application — from simple predictive text options or the more advanced voice-based virtual assistants like Alexa or Siri — works because of rich and expansive data.
Because AI uses these massive amounts of information, its success is also largely dependent on it. However, having data is wholly different from knowing how to use it right. There are many instances in which companies fail to effectively apply AI in order to solve business cases or pursue their goals. That’s because the hype surrounding the technology can lead businesses to impulsively incorporate AI into their products without extensive planning or organization. This then leads to further confusion and frustration among users, thereby threatening the future of the technology.
With that in mind, it’s vital to connect data governance to the future of AI. Data governance refers to the people, practices, and technologies involved in the formal management of data assets within an organization. While a large part of data governance includes data privacy and compliance, it also encompasses other areas of effective data management, such as data storage, data architecture, data quality, and data integration. A good data governance program is what assures a business that the information it has is consistent, trustworthy, and most importantly, optimal for AI applications.
What role would data governance play in AI, given that the technology’s algorithms are already occupying a significant chunk of the decision-making process? Traditional data governance practices allow people to determine who handled what data and when. But with AI in the picture producing outputs on its own, the audit trail then becomes a little unclear.
In this light, Andrew Burt of the FBI Cyber Division establishes a correlation between AI and data governance, explaining that it is still possible to control and monitor the process of creating AI models. What is vital in this case is to understand the data inputs that were fed to the model. This is because a clear understanding of these data sets can then offer a transparent look into how a specific AI application has arrived at certain outputs. Often, enterprises have doubts over AI’s capabilities, largely due to the results the technology has shown. In other words, companies may not trust the data outputs supplied by AI, because they have no concept of how the algorithms arrived at such decisions. This is where data governance can bridge that gap.
To illustrate, ZDNet explains that when AI makes a seemingly incorrect prediction, it often traces back to its training data. For instance, in the finance sector, a model may be only trained on data from an economic boom. This means that it may not behave correctly in the event that the economy declines. That’s simply because it is not equipped with the data to make accurate predictions in that set of circumstances. If businesses did not know the data inputs, they would naturally deem the AI model as inaccurate and a waste of investment.
This is how data governance can ensure the future of AI. Organizations today are already scrambling to integrate AI systems into their processes due to its increasing (and well-deserved) popularity. But it’s a detailed data governance program that will reinforce their AI models, thereby opening up opportunities for better business decisions. In other words, just like any other piece of revolutionary technology, AI on its own cannot be the messiah of businesses. Data governance is needed not only to make it work, but also to ensure the technology’s future and cement trust for it among organizations.
