At Millennial, engineers handle the job of extracting data from the various sources and uploading it to Good Data. In addition, two data analysts have now created 500 reports. Around 40 additional people at Millennial have access to those reports and can combine them, drill down into them and create portfolios of reports to share.
Building tiers of users, each with different permissions, allows more people in the organization to work with the data -- but safely, Hammond says. That means business executives, who aren't necessarily trained to be data scientists, have some latitude to combine and rework reports but are less likely to make mistakes because they don't have the permission to, for instance, pull in new data from a back-end database, he says.
Speed and flexibility drive cloud adoption
Athenahealth, a provider of Web-based software and services to medical practices, had most of the data it wanted to analyze in one place internally. About a year ago, the company set out to find a better way to track the hundreds of customer implementations it might be working on at any given time, says Adam Weinstein, director of core analytics at Athenahealth.
"Because we have a cloud-based platform, we have real-time access to see what's going on," he says. The biggest challenge: "Taking the data we have about what our clients are doing and how they're progressing in the implementation process and turning that into what we call a nerve center, or a way we can actively monitor exceptions to the process."
Athenahealth wanted a system that would collect information about every point in the implementation life cycle in order to easily find problem areas. For instance, clients route their fax machines to the Athenahealth system. If no faxes are coming in for a given customer, it could mean the customer hasn't yet rerouted the fax number. Or, for a long-time customer, if the percentage of fax information coming in increases relative to electronic information, that could mean someone mistakenly changed a setting.
When Athenahealth started looking for a BI product that could meet its needs, it had a few additional requirements. The vendor "had to be able to move quickly because we had a fairly strict timeline, in the two- to three-month time frame, to deliver on this project," Weinstein says.
Also, the company wanted a product that would meet analytics needs going forward, too. "We wanted to invest in more of a platform, not just a one-time solution," he says.