| | JULY 202519CIOReviewIn the long run, this investment in structured, codified business logic has paid off significantly. It allows us to adapt seamlessly to changes like shifting marketing attribution models. With the right governance, tools and foundational knowledge captured within our systems, we can pivot or scale as the business evolves, efficiently and effectively.Ensuring Fair and Unbiased AI in Critical ApplicationsBias in AI typically falls into two categories - regulatory and data-related. Regulatory bias refers to biases subject to legal requirements, which demand strict compliance with industry-specific rules. For example, fields like financial services, insurance or hiring must align with relevant national, state or local laws. To stay compliant, companies need to collaborate with legal counsel and monitor any legislative changes that may impact their AI models. The second type, data bias, arises when models are trained on incomplete or irrelevant data, affecting accuracy and outcomes. Even when regulations don't apply, the quality of data remains critical. For instance, a churn prediction model without data on call center interactions (like call duration, hold times or customer sentiment) might accurately predict customer churn but fail to pinpoint the real reasons, leaving businesses without effective strategies to reduce churn. Addressing AI bias, therefore, requires legal compliance and ensuring comprehensive, well-governed data that aligns closely with the business outcomes you aim to influence. The Future of Analytics in Enterprise Tech The future of AI in business hinges on data quality. Companies investing in AI without first ensuring robust, well-governed data foundations are unlikely to see meaningful returns. As businesses increasingly recognize this, we'll see significant investments in data enrichment, governance and cleansing. With high-quality data, AI models--especially mixed models like large language models (LLMs)--will soon be capable of answering complex business questions in minutes, a task that traditionally required days or weeks of analysis.In the next five to ten years, businesses with strong data foundations will gain a substantial edge. Executives, marketers and analysts will be able to ask AI engines targeted questions like identifying causes of a customer segment's decline or spotting inefficiencies and receive immediate, data-backed insights. This quick access to insights will accelerate the test-and-learn cycles essential to problem-solving and strategy.In short, AI's potential to drive business transformation depends on data. Companies building clean, structured, and well-maintained data infrastructure today will be the ones reaping the rewards of advanced, actionable AI insights tomorrow.Aligning Analytics with Business Goals across Departments Effectively supporting business operations requires an analytics team embedded within the business to participate in reviews and align closely with business goals--much like a marketing department. Being involved in quarterly or monthly reviews allows the team to understand business needs, helping drive actionable insights that align with operator goals.On a tactical level, I've found setting up regular "study halls" is invaluable. These sessions, held bi-weekly or monthly, give business partners an open space to ask any data-related questions. It could range from basic Excel functions, like VLOOKUPs, to more complex analytics for new product launches. This accessibility builds trust and reinforces the analytics team as a valuable partner.Building trust is essential, as analytics often require insights into operational details. With established trust, the team is kept informed about key changes, like power outages affecting store performance, reducing lag in understanding data anomalies. By participating in regular business reviews and holding open sessions, analytics teams can stay connected, anticipate issues and provide more responsive, insightful support.Advice for Senior Leaders and Upcoming ProfessionalsThe internet is a powerful resource for learning coding and data analytics, even if you have no formal background. Online coding classes, forums and tools like ChatGPT can quickly help you go from zero to proficient. My own journey is proof--I was a philosophy professor with no meaningful analytics/coding background, yet I learned much of the technical skills I needed quickly from with the availability of online resources, help from colleagues, and applying logic from my teaching experience. There's a thriving online community where you can learn and grow by accessing real-world data sets (like public health or sports data) for hands-on practice, which is typically messier than classroom examples, and thus closer to business data sets. As you build your skills and career, remember that business impact is the key. Whether you're saving costs, generating revenue or saving time, always link your work to business value. While you may not directly create or sell a product, your role is to support those who do, driving business success through data and analytics. This focus on delivering measurable results is essential for career growth. Embedding analytics within the business isn't just a best practice--it's how insights become action. Proximity to the teams that act on data ensures relevance and speed
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