The Evolution Of AI In Analytics

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Naveen Edapurath Vijayan is a Sr Manager of Data Engineering at AWS , specializing in data analytics and large-scale data systems.

Artificial intelligence (AI) is transforming the way businesses analyze data, shifting from traditional business intelligence (BI) dashboards to real-time, automated decision making. Organizations are increasingly leveraging AI-powered analytics to enhance operational efficiency, reduce manual intervention and improve decision making at scale. However, while AI offers significant advantages, challenges such as data bias, governance and legacy system integration remain key hurdles.

Throughout my extensive career spanning more than a decade in analytics, data engineering and machine learning at organizations ranging from global enterprises to agile tech startups, I've witnessed the evolution in how businesses leverage data. Initially focused on building traditional BI dashboards and transformation and loading (ETL) pipelines for retrospective analyses, my role evolved into predictive modeling and leading machine learning projects.

With specialized experience in HR and finance, I've seen AI transform data-driven decision making from reactive to proactive—predicting attrition risks, forecasting staffing needs, identifying financial risks early and optimizing resource allocation. This shift empowered these functions as strategic organizational partners and demonstrated AI's potential across various industries.

Businesses are leveraging AI to generate real-time insights, automate data processing, enhance transparency and integrate intelligent decision making across various functions. Below are some of the key trends shaping the future of AI-driven analytics.

• Explainable AI (XAI): Transparency and explainability have become crucial as businesses increasingly rely on AI for critical decision making. Every successful model I have built emphasizes explainability—models need to clearly articulate why specific predictions or recommendations are made. For example, an attrition prediction system becomes significantly more valuable if it not only identifies who might leave the company but also explains the factors driving that risk, thus empowering targeted interventions. Going further, models that provide actionable recommendations based on these insights are particularly impactful.

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