There is a famous scene in Austin Powers: International Man of Mystery, where Dr. Evil asks for “one meeeellion dollars.” Then, realizing that the number isn’t quite high enough, he changes his mind and asks for “one… hundred… billion dollars!” It seems like quite the escalation from one number to the other, but in this era of big data, that kind of increase can happen in no time. Such rapid growth in data amounts spurred Quotient Solutions to focus its efforts on data warehousing in order to address clients’ concerns over big data.
In an exclusive interview, John Curtis, president and CEO of Quotient Solutions, spoke to TMC (News - Alert) about his company’s data warehousing solutions and how they can help with the complications of big data. Curtis stated that his company saw client records rising from hundreds of thousands of lines to hundreds of millions of lines and more. The issue first came to Quotient’s attention years ago when it was building an app for a company in the trade show business. The team from Quotient was shocked to see that they were dealing with nine million records a day during trade show season, a number far greater than they had anticipated. Nowadays, Quotient’s solutions are used by financial and cable companies that deal with millions of records per day, and billions of records over time. The issue then becomes what to do with that data, and whether the information needs to be addressed immediately or if it can be handled in due time.
One solution to deal with this massive influx of information is to utilize data warehousing techniques. Curtis likened this strategy to a library. Quotient acts as a curator of information, not an interpreter, storing lots of information for a long period of time. Just as librarians are in charge of storage and care of books, Quotient provides the same service for data. The key, according to Curtis, is to figure out how to store the information and for how long. Often, the length of storage is dictated by compliance requirements. Either way, it was important to look at new ways to store large volumes of data very quickly and to provide a stable way to have redundancy.
According to Curtis, it comes down to an issue of volume versus speed. When dealing with millions of pieces of information, does it matter if, say, 10,000 are missed? What type of data is involved? Is it critical to get everything right immediately, or can it be processed later? In some cases, like CRM data, salesperson number two wants to look at a sales record immediately after salesperson number one enters the information. In other cases, the data can be stored on a server and can be processed at a later time. For many companies, especially smaller ones, cost is a concern. Quotient prides itself in not over architecting because customers need these solutions right away.
Lots of companies deal with big data, but Curtis says that Quotient provides great value as it has experience dealing with data that numbers in the billions, and is capable of handling transaction volumes that other companies cannot. Quotient is staffed by smart people who can make technology approachable by non-tech people, helping clients understand risks and walking them through what they need and can afford. In short, Quotient can make the “beeelions” of lines of data seem a bit less Evil. Groovy, baby!
Edited by Jamie Epstein