{"id":48936,"date":"2023-11-30T07:21:59","date_gmt":"2023-11-30T12:21:59","guid":{"rendered":"https:\/\/centricconsulting.com\/?p=48936"},"modified":"2023-11-29T09:22:43","modified_gmt":"2023-11-29T14:22:43","slug":"fix-your-data-win-at-ai-heres-how-to-get-started","status":"publish","type":"post","link":"https:\/\/centricconsulting.com\/blog\/fix-your-data-win-at-ai-heres-how-to-get-started\/","title":{"rendered":"Fix Your Data, Win At AI. Here\u2019s How To Get Started."},"content":{"rendered":"

In this segment of \u201cOffice Optional with Larry English<\/a>,\u201d Larry discusses why having good data is a key part of your AI strategy.<\/h2>\n
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A few years ago, one of Uber\u2019s self-driving<\/a> cars hit and killed a pedestrian crossing a street outside a crosswalk. What went wrong? When the technologists trained the car to recognize pedestrians, they mostly used images containing a crosswalk. They had inadvertently \u201ctaught\u201d the AI system that the crosswalk was the important part.<\/p>\n

While most companies implementing AI into their operations aren\u2019t dealing with anything so important as human life, there\u2019s a salient lesson here: Feed AI systems bad data, you\u2019ll get bad results. AI will undoubtedly become the next big business differentiator, but only for companies that can get their data under control.<\/strong><\/p>\n

Bad Data, Bad AI<\/h2>\n

Responsible AI is such a buzzword these days because so many companies have a serious data problem \u2014 they don\u2019t know what data they have. It\u2019s inconsistent and unsecure. And feeding unknown, unmanaged data into an AI system is just asking for a data breach, regulatory violation, misinformed strategic decisions, unintended bias, or reputational damage to happen.<\/p>\n

The problem is many companies have a data mess on their hands. Either they have a haphazard strategy or no strategy at all for data governance<\/a>, the rules and processes for collecting, using and storing data.<\/p>\n

Organizations don\u2019t pause to figure out their data strategy, intent on chasing after flashier, revenue-generating projects.<\/strong> However, when companies want to put that data together \u2014 say, for an AI tool \u2014 they can\u2019t do it because there aren\u2019t any overarching rules around how to handle data. They\u2019re left with a big mess that takes a hefty amount of time and investment to untangle.<\/p>\n

Retroactively applying data governance to all the data in an organization is a colossal undertaking. Thankfully, it\u2019s not necessary to go that big to embark on your next AI project<\/a>.<\/p>\n

A Pragmatic Approach To Fixing Your Data<\/h2>\n

Here\u2019s a pragmatic, just-in-time approach to fixing your data, leveraging the power of AI<\/a>, and creating value incrementally along the way:<\/p>\n

Pick a use case.<\/h3>\n

Start out by picking a single use case for AI.<\/strong> What\u2019s a major business mandate AI can help with? Where do you know you have proprietary or third-party data that can be mined for AI? You\u2019ll want to channel Goldilocks here, picking a use case that\u2019s neither too big nor too small, ideally something that\u2019s internal. Your first use case should also have limited data domain requirements\u2014in other words, a use case that only requires data from one source.<\/p>\n

Then, figure out the state of the data you\u2019ll be working with. What do you need to correct before feeding that data into an AI system?<\/p>\n

Fix the data required for that use case.<\/h3>\n

Once you have a feasible use case and have assessed the state of the data needed to move forward, it\u2019s time for a clean-up job. Your data doesn\u2019t have to be perfect to start creating value from an AI tool, but you do need to understand its flaws before you leverage it.<\/p>\n

You\u2019ll need to put as much governance and strategy<\/a> in place as needed for that single-use case. Non-negotiable data governance components include:<\/p>\n