As organizations embrace the reality of a future powered by artificial intelligence (AI), there’s a common assumption that the data they have accumulated over the years is AI-ready. But that\u2019s not the case. The reality is that no one has truly AI-ready data, at least not yet.<\/p>\n
The outcome of this reality spans from suboptimal AI-generated information to an outright failure of AI to produce anything of value for your organization.<\/strong> But this does not have to remain your reality. To overcome it, you must work to understand the nuances and gaps in your data and then fill in what\u2019s missing. Doing so is crucial to maximizing the value of your data and AI use, managing the risk of your AI models and tools, and informing your overall data acquisition strategy<\/a>.<\/p>\n Traditionally, companies have collected and used data primarily to serve immediate operational needs and human-driven analyses. This approach, while practical, often leads to limited and gap-filled datasets as they lack the foresight of AI\u2019s extensive analytical capabilities<\/a>.<\/p>\n Data has traditionally reflected our past and present operations, not necessarily a comprehensive blueprint for the future, especially in an AI-driven world<\/a>. In short, the data might be rich in specific operational aspects but is missing various other potential dimensions that AI could explore for deeper insights. Consider these three examples:<\/strong><\/p>\n AI\u2019s ability to provide valuable insights and predictive behavior is somewhat limited without additional data dimensionality.<\/strong> This can impact everything from over- or under-ordering to over- or under-staffing to loss of revenue or risk of employee safety. Even worse, your AI’s limited insights may suggest a strategic recommendation that does not manifest until it’s too late for your company.<\/p>\n Rushing into AI projects with incomplete data can be a recipe for disappointment. The power of AI lies in its ability to find patterns and insights humans might overlook. But if the necessary data is unavailable, even the most sophisticated AI cannot generate the insights organizations want most.<\/strong><\/p>\n The risk isn’t only subpar results. It’s potentially drawing incorrect conclusions from the data you do have. These incorrect conclusions mean your AI models are at risk for bias. AI works by processing large amounts of data, and if that data contains holes, it can be difficult to identify patterns and trends. Consider the impacts of missing data on two example systems:<\/p>\n If data is incomplete and dimensions are missing, then how can you get value from AI down the road while also maximizing the value you can get from your data today? Start by ensuring your data is ready before deploying your AI models.<\/p>\n No dataset is perfect. But recognizing and addressing the gaps is half the battle. The rest is acting on what you find. Here are some recommended actions for remedying incomplete data.<\/p>\n If you take away nothing else from this, consider this final thought: to become AI-ready, you must be data-greedy.<\/p>\n While the data you collect today might seem excessive or irrelevant, storing it comes at a mere fraction of a penny.<\/strong> Yet, this information could become valuable in an AI-powered future, turning those tiny fractional pennies into substantial dividends. With AI readiness, there is no such thing as too much data, but there is such a thing as too little.<\/p>\n Temper the enthusiasm for using AI with the reality of your current data. Embarking on the AI journey<\/a> prematurely can lead to suboptimal outcomes, but with proper due diligence and planful thinking, you can find ways to get AI value from your data today. By looking at what is missing from your data today and improving upon it, you\u2019re enabling a true AI-powered future for your organization.<\/p>\n \n AI Readiness Risk No. 1: Your (Missing) Current Data<\/h2>\n
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AI Readiness Risk No. 2: Incomplete Data<\/h2>\n
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Evaluate Your Company Data for AI Readiness<\/h2>\n
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Managing the Risk of Incomplete Data<\/h2>\n
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Get Greedy About Your Data<\/h2>\n