Modern Analytics Platforms

Chapter 1. Case Studies: Modern Analytics in Action

Another finding of Experian’s 2021 Global Data Management survey: 62% of respondees claimed that a lack of agility in data processes have hurt their response to changing business needs. In other words, the majority of organizations are looking for new approaches to analytics to remain relevant in a changing economy. In this chapter, you’ll see how three organizations, in different industries and with different objectives, have benefited from modernized analytics.

Case Study: Insurance

AA Ireland specializes in home, motor, and travel insurance. In the face of declining motor insurance revenue, AA Ireland’s analytics officer was looking for new ways to optimize profits and segment customers. Typical analytics approaches to these objectives were backward-looking, slow to update, and required a great deal of technical assistance. “Insurance has always had predictive models, but we would build something, and in three months, update it,” the analytics officer explained. In this traditional framework, information technology (IT) professionals were needed to extract historical data, which was then modeled by highly trained users.

This workflow made iterative customer and pricing analytics next to impossible. In particular, it overlooked data from real-time events, such as the 140,000 car breakdowns AA responds to annually. This and all other data had to pass through static and often siloed repositories, where it could only be used in backward-facing analysis.

New streaming analytics tools now feed this real-time data in and out of models continuously, allowing for more valuable—and self-service—descriptions and predictions. An executive at AA explained the difference in workflows like this: “Rather than someone approaching my department to say, ‘Can you dive into this and come back with your findings?’ they’re going to be able to click on that metric and run a model that shows why that trend occurred.” Moreover, these new tools allow business users to mine through, model, and analyze data without the assistance of IT or other technical professionals. “You give the capability to build models to businesspeople who understand the business, modeling, and math better than IT. You don’t sit in an IT queue for a year and a half.”

Case Study: Shipping

CargoSmart is a global shipment management software provider, offering sailing schedules, documentation, contract management, and more to various shipping parties. Ralph Ho, CargoSmart’s senior manager of customer integration, needed a way to provide greater data-driven transparency to clients. “We wanted to use advanced analytics to provide unprecedented visibility so ocean carriers could plan ahead in case of disruption and make use of real-time analysis to improve decision making,” Ho explained. Traditional analytics approaches failed to accommodate the variety of data sources CargoSmart needed to track. Carriers had little ability to predict, or adapt to, changes to terminal handling fees and day-to-day freight conditions.

New analytics approaches have allowed CargoSmart to gather and blend data across the maritime supply chain and provide consolidated dashboards and analyses to clients. In particular, with Internet of Things (IoT) technologies, CargoSmart’s software has helped monitor and respond to events in real time. These changes have resulted in a 3.5% decrease in fuel consumption for carriers and a competitive advantage for CargoSmart.

Case Study: Semiconductor Manufacturing

Hemlock Semiconductor is a market-leading provider of polycrystalline silicon, a critical raw material for semiconductor manufacturing. Given increased commoditization within the polysilicon industry, Hemlock sought a way to gain important insights into costs at scale and adjust its business strategy. Keith Carey, Hemlock’s IT director, put it this way: “We needed to be able to look at our internal information to understand costs in more detail and bring in external information so we could take advantage of potential new business models.”

Hemlock’s legacy analytics systems offered siloed and limited looks into operations efficiency. These systems also made it difficult to provide customers with data-backed services and insights.

With modernized analytics, Hemlock’s record-processing time was reduced 1,000×. Analysts were able to gather and use data with powerful results for the business, constructing a portfolio of cost-saving opportunities. Hemlock customers are also able to collect and analyze data via a self-service application programming interface (API).

Takeaways of Modern Analytics

From financial services to manufacturing to logistics and supply chain—and across all data-intensive industries—modern analytics has transformed organizations for the better. For AA Ireland, this meant more predictive pricing models that didn’t require months of expert intervention to modify. For CargoSmart, blending IoT-based streaming data with conditions across the supply chain provided greater transparency and room for planning to clients. Hemlock unlocked siloed data and with it handled business needs ranging from operations efficiency to customer service.

In each of these cases, organizations used a combination of people, processes, tools, and data in a move away from legacy analytics systems. These new approaches unlocked agility. In Chapter 2, you’ll learn more about the factors at play in this progression and what the next wave of converged analytics will mean for business agility.