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When I began my journey within technology, we had a very different view of data from today. Back then, it was effectively a supporting player in the larger business’s success. Ten-plus years later, I can confidently say that data has undergone a seismic transformation.
Despite this shift, a critical question still looms large. In today's fast-paced and rapidly changing business landscape, what do enterprise leaders value more—the past or the present? Or in more relevant terms, traditional analytics or adaptive analytics?
While both provide valuable insights, I believe that real-time data is the key to success.
Traditional analytics may still have their place, but they are no longer sufficient for businesses. Data volumes are expected to reach a staggering 180 zettabytes by 2025. Turning this data deluge into actionable insights is crucial to thrive in today's dynamic environment.
In fact, 65% of modern business decisions are more complex than those made just a few years ago. This ties in neatly with how our view of data has changed, and it also highlights how the most up-to-date information is important in making informed choices. With adaptive analytics, enterprises will be able to reap the benefits of real-time data analysis, insight-based, timely decision-making.
Adaptive analytics vs traditional analytics
In line with the need for agility and responsiveness across business functions, adaptive analytics has ample opportunity to shine. Unlike traditional analytics, which can be slow, rigid, and prone to error, adaptive analytics offers a dynamic and flexible approach that enables organizations to keep pace with the ever-changing market and customer demands.
Another advantage of adaptive analytics is its ability to leverage advanced technologies like artificial intelligence and machine learning. This helps businesses unlock insights that were previously hidden, allowing them to make more informed decisions and take advantage of new opportunities faster. Real-time data analysis ensures that adaptive analytics can provide accurate and relevant insights in a matter of seconds, empowering business leaders to stay ahead of the curve.
Moreover, by automating this data analysis and reducing the need for manual intervention, adaptive analytics is a boon for businesses, saving time, reducing costs, and optimizing their resources. It also has a positive knock-on effect wherein data analysts and other experts can focus on more strategic tasks that add value to the business. Such tasks can include new product development, customer service improvement, and the identification of new growth opportunities. Offering businesses a smarter, future-ready, and disruption-proof approach to data usage, adaptive analytics has therefore emerged as a leading trend in data and analytics (D&A).
The road to proactivity: How adaptive analytics is reshaping industries
I have found the possibilities, that adaptive analytics can offer, to take on quite interesting dimensions when applied across industries. Take the banking, financial services, and insurance (BFSI) sector. In a domain where fraud and other financial crimes can have severe consequences, adaptive analytics provides real-time detection of anomalies in financial transactions. This prevents fraudulent activities and protects businesses against any losses. Similarly, in healthcare, where patient outcomes and efficient use of resources are of tremendous importance, adaptive analytics can identify patterns and predict outcomes based on patient data. This helps healthcare providers to make more informed decisions and administer better care.
For retail and e-commerce businesses, adaptive analytics can tailor product recommendations based on how customers browse and buy. Increased customer satisfaction and sales becomes a foregone conclusion. Also, it can identify purchasing patterns and forecast demand, allowing businesses to optimize their offerings and improve their inventory management.
For instance, Amazon uses adaptive analytics to recommend products to customers based on their browsing and purchase history. The algorithms continuously adapt to new data, with which the global online retail leader can make personalized recommendations to its customers in real time.
Adaptive analytics also holds immense value for manufacturers. It can optimize supply chain management and production processes, reducing costs and improving product quality. By identifying areas for improvement, forecasting demand, and predicting maintenance needs, it can also enable manufacturing companies to stay competitive in an increasingly global market.
For marketing and advertising firms, reaching the target audience may be business-critical but it also remains a key concern. Adaptive analytics can help them identify the most effective channels, messages, and offers, as well as optimize advertising spend based on real-time performance data, maximizing the return on investment.
For energy and utility companies, adaptive analytics can improve energy consumption, reduce downtime, and detect equipment failures or other anomalies in real time. All this can lead to savings in costs and an increased efficiency. Enterprises in the transportation and logistics industry can also leverage adaptive analytics. It can help them plan the best routes and schedules for their deliveries. Adaptive analytics can also allow real-time tracking of shipments and inventory, which reduces costs and improves delivery times.
A cue in this regard can be taken from a leading mobility-as-a-service provider that uses adaptive analytics to optimize its pricing strategy. The algorithms analyze real-time data on demand and supply to determine the optimal price for a ride. This helps the company increase revenue while also providing a relatively affordable and quite convenient service to its users.