The concept of artificial intelligence (AI), which was popularized in science fiction and once portrayed as sentient robots with the ability to think and rationalize, has now found more realistic and practical applications.
AI, as we know it today, is the ability of computer systems to perform tasks that normally require human intelligence, such as decision-making or pattern recognition. Coupled with machine learning, which allows programs to access data and use it to learn and improve upon themselves, these technological advancements have the potential to take businesses to the next level in terms of efficiency and process improvement.
Over the past few years, AI and machine learning have made their way into marketing and sales departments, helping them segment and analyze customer data to inform and adjust marketing campaigns, predict customer needs, and improve customer experience with customized solutions and value-added services. AI and machine learning have provided businesses with a unique edge – the ability to quickly analyze reams of data in previously unimaginable ways and use those insights to transform their strategies with the end goal of greater profitability.
However, the output of these measures depends greatly on the quality of data being fed into the system. Imagine the havoc that would be wreaked within an organization if inventory forecasting estimates were based on incorrectly input sales data or if marketing advertisements were targeted at the wrong audience because of a data mix up! Losses would pile up, marketing spends would be wasted and the organization may end up losing prized customers.
This is where the process of data governance comes into play. Data governance is an ongoing process that ensures that your data is understood, verifiable, accessible, and secure, in order to insulate your organization from the risks that arise from bad decision-making based on inaccurate and inconsistent data. Moreover, governance ensures that every outcome, decision and automation of your AI/machine learning model is explainable, traceable, auditable, and potentially reversible, thereby building trust into your models. Experts recommend having a data governance process in place well before the launch of any AI or machine learning solution, as understandably, it might be difficult to pinpoint where the error lies or fix it before it is too late.
Our in-depth article on the importance of governance in AI and machine learning solutions touches upon some of the challenges that can arise if these technologies are implemented without utmost care. It also delves into the various checks your data needs to undergo for it to be deemed trustworthy, accurate and appropriate for use in AI and machine learning applications.
