He also wanted to let more people in the organization, like data analysts, assemble reports, rather than limiting report-making to technologists who know how to code and interact with back-end databases. Plus, he needed a system that was flexible so the software would be easy to maintain and it would be easy to create new use cases.
Hammond eliminated on-premises BI software options in part because he didn't want to incur the costs associated with managing and maintaining it. Time to market was also important.
Millennial ended up choosing Good Data's cloud BI offering and had its initial project in place in about three months. Subsequent projects have taken closer to a month to get up and running, Hammond says.
Sending on-premises data to Good didn't turn out to be much of a problem for Millennial. Each day the company generates around 10TB of raw data but transfers only around 18MB of compressed data to Good. "We do all the transformation of raw data into only the specific data we want in our systems before we transfer it into the cloud," he says.
Not all businesses do such a great job of managing that data transfer, though. "What we tend to see is it's rather difficult to keep the amount of data moving between the database and the analytics tool small," says Gartner's Tapadinhas. In other words, keeping data transfers small is important in cloud BI to manage both costs and upload/download bandwidth issues.
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. Adam Weinstein, director of core analytics, Athenahealth
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