Data analytics has rapidly evolved from advanced to transformative. Today, organizations have the potential to harness deep insights from their data assets to inform their decision-making and get ahead of the competition – but only if they have the right data infrastructure in place to support transformative analytics.
But what is it? Transformative analytics goes beyond traditional business intelligence. Used alongside technologies such as generative AI, transformative analytics has the power to uncover deep insights, patterns, and predictions that inform strategic vision. It essentially gives organizations the power to ‘see’ into the future, helping them forecast, improve customer satisfaction and relationships, gain customer and market insights, and improve risk management.
Use Cases for Transformative Analytics
Transformative analytics has the potential to make a massive difference in every industry. Here are just a few examples:
- Automating mundane tasks: Transformative analytics can combine with other innovative tech. For example, commands generated by outputs from transformative analytics can prompt generative AI to automate everyday tasks and support further analyses. By harnessing the power of generative AI, teams can effortlessly connect their daily analytical systems, streamlining workflows.
- Preclinical drug discovery: During the preclinical drug discovery stage of drug development, researchers screen thousands of compounds to identify which ones show potential. Transformative analytics can optimize this process by helping researchers analyze vast volumes of biological and genetic data.
- Marketing-mix modeling: Marketing teams can use transformative analytics to link marketing investments to other sales drivers and, including variables like seasonality, promotional activity, and competitor campaigns, determine the effectiveness of marketing spend by channel and forecast the success of future marketing tactics.
- Propensity to buy in eCommerce: By combining data sets on purchases with online behavior (e.g., browsing history or social media activity), organizations can use transformative analytics to determine which customers are most likely to buy products or services – and how best to reach them.
- Predictive maintenance in manufacturing: By analyzing performance metrics and data from equipment, manufacturers can identify where in the lifecycle any given machine is through transformative analytics, enabling them to forecast maintenance or replacement needs proactively. This allows them to plan ahead and minimize delays in production due to machine downtime.
- Predictive planning in logistics: Using transformative analytics, logistics providers can analyze years’ worth of data on routes, fuel consumption, and vehicle usage to plan efficient routes in real-time, eliminating wasted capacity and determining if additional drivers or vehicles are required.
Challenges Facing Enterprises
It’s clear that transformative analytics offers enormous potential for enterprises – yet many are struggling to take advantage of it. This is because organizations face multiple challenges that limit transformative analytics, such as data integration friction, ineffective data transformations, and a lack of data literacy.
What organizations need is an agile data infrastructure to help manage and power transformative analytics while also making sense of complexity and removing data integration friction. To help do this, our team of StreamSets experts has written a new eBook that outlines the key challenges and how agile data infrastructure can help overcome them.
Are you an IT or data professional responsible for managing data infrastructure? To learn more about how you can unlock the value of transformative analytics, download your eBook here.
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