Digital Media Buzz > Digging for Database Gold: The Devil’s in the Details

Digging for Database Gold: The Devil’s in the Details

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By Ned Smith

Take away their databases and most businesses would come to a screeching halt faster than an automaker heading to bankruptcy court. Modern business floats on a sea of data — data about employees, data about inventory, data about customers — you name it, someone has figured out a way to slice it and dice it and store it in a database. That data is the touchstone for making business decisions and complying with regulations. Simply put, it’s the lifeblood of commerce and modern society.

But data in and of itself is pretty inert stuff; you’ve got to do something with it to make it pay its way. Databases are a great way to organize, store and retrieve mountains of information, but at the end of the day the 1s and 0s in a database are still just raw data. The terms we use to describe data storage speaks volumes about our expectations and how they fit into the data ecosystem — we call them data repositories and data warehouses. But regardless of database capabilities and limitations, it’s how you decide what data to store and what you do with it when you retrieve it that determines whether you’ve struck gold or been sidetracked by the false glitter of iron pyrite. This holds especially true for high-volume, customer-facing industries like healthcare and insurance.

There are, however, ways of wrestling this data tsunami to the mat to make data prospecting more productive. Like most things connected with IT, it’s the up-front investment of time and thought that pays off big throughout the business cycle. It’s all about the wetware ― human intelligence. The hardware and software come later.

“You can collect tons of data and spend millions and millions of dollars on databases and software, but if you don’t know the business question you’re solving for, it’s kind of a drop in the bucket,” says Charles Wetzel, president of Buxton, a leader in customer analytics. “The quicker you understand what you’re trying to solve for, the more economical it will be. If you don’t invest in the appropriate technology, people and processes together, you’re not going to have the appropriate solution that works for your organization.”

In terms of knowing their customers, Wetzel says, “Medical organizations and insurance carriers can do a bettter job than they’re doing right now.”

“There is a spectrum of understanding out there,” says Andrew Africa, vice president of business solutions with MaintenanceNet, a provider of warranty and service contract management services and data-driven lead generation. Service contracts, like insurance policies, are an annuity business that require periodic renewal. “Some customers are very well-versed in what it is that they need.”

Databases excel at storing huge volumes of historical data about customer transactions. “Understanding the historical activities of your customers will give you the ability to take further future actions,” Africa says. “What are the primary customer touchpoints? What are the primary customer interactions that you want to achieve? Understanding those will enable the design of data systems that will capture, manage and enable action to be taken against that data.”

Understanding the issues requires not only the awareness of problems but also the intellectual recognition of why those problems exist. Without that understanding, there is a great likelihood that companies will build solutions that don’t speak to the root causes or don’t speak to the most valuable component of the root cause. The goal is to create a master data environment steeped with business intelligence.

“Early on,” says Africa, “many people were trying to solve their service revenue issue by just putting more people on it. ‘I’m going to put a bunch of feet on the street and I’m just going to pound the phones and sell that way.’ What they found was that the data their people were trying to call on or had in their data environment really wasn’t in the state or quality that was needed. By just improving their data, we could double their effectiveness with the same set of resources.”

The web has exacerbated the data conundrum. “In the past, customers were primarily focused on internal data,” says Mike Pittaro, co-founder of Snaplogic, a company that builds software that helps companies connect to data from both internal and external sources and applications. His company’s solutions, Pittaro says, deal with files, with databases and with web data — “things like Google Calendar, XML or just pure HTML.”

Today, he says, there are two broad problem areas: getting data connected between systems and trying to do analysis across systems. Compounding the problem, data in one application doesn’t usually line up with other applications. The solution is to create a data integration layer between each source of data, which is Snaplogic’s niche.

The exploding data landscape makes this an imperative. “In most companies,” Pittaro says, “the mindset was that our data is the data that’s in our building. And that’s the important data. And then it became the data in the data center. Now it’s the data that’s in the cloud. That web data is beginning to become more significant than the internal data. The data is moving outside the building. Companies are really struggling with how to access it. The real Mt. Everest is dealing with all that data that’s not on your network.”

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