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Utilizing Better and Faster Intelligence to Improve Strategic Decision Making
The insurance company Companhia de Seguros Tranquilidade was founded in 1871 and has played a leading role in the Portuguese economy. Its distribution network encompasses 38 corporate stores, more than 78 franchise stores and over 270 agent stores nationwide. The company’s more than 700 employees offer Tranquilidade’s full range of non-life solutions to over 600,000 active clients.
Streamlining Reporting and Analytics
To stay ahead of the competition and stimulate business, Companhia de Seguros Tranquilidade needed an alternative to its legacy data architecture. The company was burdened with a complex layered architecture in which the data were stored in SAS files and spread over eight separate data marts of 25 terabytes, each dedicated to a different corporate department. Reporting and analytics could only be handled by highly technical experts.
“Our former data architecture was so complicated that only advanced technical profiles were able to understand it and turn the data into any kind of intelligence, even for the most basic kind of querying and reporting,” recalls José Manuel Vera, CIO of Companhia de Seguros Tranquilidade. “Not only did this influence the delivery speed of reporting but it left the door open for mistakes. Worse, the reports that were made were often so intricate that it was nearly impossible for untrained business users to interpret them correctly.”
Tranquilidade was struggling with the severe effects of data explosion on their business agility. Not only were they getting close to a point at which their data warehouse would no longer be able to hold all of the fast-growing data, their reporting and analytics processes were continuously increasing in complexity and decreasing in speed, while the delivered documents were getting more difficult to interpret. It was time for a change.
Pivotal Greenplum Handles Massive Data Volume
Tranquilidade selected Pivotal Greenplum because of its scalable shared-nothing, massively parallel processing (MPP) architecture. “We strategically opted for this kind of structure because it enables us to scale horizontally and stimulates faster analytics than the more common OLTP anatomy of the solutions of the competition,” Vera explains.
“Fed up with our labyrinth-like data architecture, we were immediately attracted to the crystal-clear model and infrastructure of Pivotal Greenplum – that is built to manage, store and support analytics of massive amounts of data,” he continues. “It allows us to consolidate all the raw data into one single data warehouse and have all the separate data marts communicating.”
Even the process of moving the data from the old to the new data architecture of Pivotal Greenplum – a very complex and elaborate operation – took much less time than expected.
Easier Access to Information
Pivotal Greenplum enables department-wide access to the reports and employees each handle their own queries. In addition, the company’s external partners have near-real-time access to relevant reports.
“There is no point in having powerful tools, if the information is inaccessible to the business users,” Vera says. “The more people who receive the reports and intelligence at the right time, the more we can use this information to inspire successful decisions and follow the right strategies.”
Pivotal Greenplum has dramatically increased query speed for the company’s reports. Monthly reports that used to take 48 hours of straight processing now run in less than an hour.
“With Pivotal Greenplum, the speed at which we now can gain access to information is incomparable to anything I have known before,” Vera confirms.
“On the basic level of reporting, we were able to boost the delivery time from a monthly to a daily basis,” explains Vera. “Knowing each day, for example, how many policies we are selling and differentiating between the separate divisions has made a huge difference for the commercial department.”
“As to the less prioritized and more technical reporting, for regulators and internal use, we moved from once a month to once a week,” he adds. “This is the kind of information that, for instance, gives us a much better perception of the contracts we have with companies that work in the claims management segment.”
Leveraging Big Data
“One of the main benefits of Pivotal Greenplum is that it allows us to move much deeper into analytics and even tap into the goldmine of Big Data,” says Vera. “In addition to asking better questions and organizing more complex ETL processes, we can now take full advantage of predictive analytics.”
Pivotal Greenplum facilitates high-performance, parallel import and export of compressed and uncompressed data from Hadoop clusters. “So, not only are our structured data now better structured, we can also integrate them with unstructured data from Hadoop sources, like clickstreams, to extract deeper insight,” Vera notes. “Our direct line insurance company was, for example, able to find out why there was such a discrepancy between the amount of online simulations and actually-concluded contracts and successfully adjust its strategy in the matter.”
Ultimately, Pivotal Greenplum enables better decision making which leads to higher productivity and efficiency – saving money and driving profitability.
“When it comes to basic intelligence, we are now spending a lot less per report – in man-hours and because we are able to do a great many more than before,” Vera concluded. “And, more importantly, the business collaborators now receive all the intelligence they need at the right time, so that they can focus even more efficiently on their core activities and drive revenue.”