Making the world a smarter place with agile analytics

Posted on: February 21, 2017 by

In every sector of business, we have seen a previously unimagined explosion in information; and across every field, businesses are looking to turn these great volumes of digital data into prescriptive insights. From here, leaders can gain the insight needed to define winning strategies, improve customer experience and optimize their operations. Over the past five years, we’ve focused our efforts on bringing together different competencies, technologies and resources that are designed to help organizations embrace this data-driven revolution. Here, I explore how it is being applied to different fields of business.

From Business Intelligence to agile analytics

In the new data-driven landscape, the ability to derive insight from mass volumes of structured and unstructured data is made possible by systems which learn as they perform. Using these systems, organizations can make the leap from simple Business Intelligence to agile analytics, while preparing for a future built on Robotic Automation and Cognitive Computing.

Every enterprise in every sector will soon come to regard agile analytics as an essential business tool. To truly benefit however, a collaborative approach is needed. In the new world of agile data analytics, there are more opportunities than ever before to take advantage of cross-industry collaboration – insurance, automotive and transport companies for instance, have much to gain from working together and sharing insights. For instance, new insurance products could be brought to market, using IoT data from connected cars and fleets to inform insurance risk models.

Real-life applications

  • Smart manufacturing - In these uncertain times, manufacturers are under more pressure to optimize their supply chains and bring products to market as quickly as possible. By using the suite of tools in Atos Codex however, they can improve demand forecasting and speed up production by exploiting comprehensive real-time intelligence.
  • Energy - With the smart meter roll-out across Europe well underway, energy suppliers can use agile data analytics to give them unprecedented clarity on customers’ energy use, in turn enabling them to optimize load forecast to ensure effective continuity of supply.
  • Retail - The use of data analytics in retail is nothing new – along with financial services, the sector was one of the first to realize the benefits of using big data to improve the customer experience. By combining data from multiple sources – including weather forecasts and even school holiday times – retailers can improve forecasting to optimize stock levels and meet shifting consumer demands.
  • Telecoms - Consumer viewing habits are increasingly shifting to digital channels, with more bandwidth required to support these routes. Only by using Network Function Virtualization (NFV) can telcos provide affordable scalability, and this in turn is only possible through predictive analytics.

Big data should not just be a series of siloed projects within a business, but rather underpinned by a sustainable and concerted strategy to drive value. As demonstrated here, enterprises using business-driven analytics can make a genuine long-term investment in Big Data, in turn maximizing the value of their most critical assets, across their organization.

For more information, read my previous post, in which I explore why big data projects typically fail and how businesses can make more of a success of them.

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