Even though cloud-based business intelligence has been around for nearly a decade, a recent trend is driving renewed interest: Companies are generating and storing more data in the cloud.
"What I think will happen is people will move the analytics app closer to the data," says Joao Tapadinhas, a Gartner analyst. "As more data sources move to the cloud, it makes more sense to also adopt cloud BI solutions because that's where the data is. It's easier to connect to cloud data using a cloud solution."
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Researchers at Gartner say that 2014 may be the tipping point for cloud BI. In each of the last four years, around 30% of respondents to a Gartner survey said they'd run their mission-critical BI in the cloud. This year, however, nearly half -- 45% -- said they would adopt cloud BI.
Historically, cloud BI products have been most appealing to smaller businesses, in part because those are less likely to have an IT department that can manage an on-premises product. However, analysts are starting to see larger companies adopting cloud BI, typically starting with individual groups or departments.
Shifting data analytics to the cloud doesn't come without its challenges, though. For example, it's unlikely that all corporate data will move to the cloud, particularly in larger enterprises. That means many businesses will have to map data from both cloud and on-premises sources to the BI software, whether that software itself is on-premises or in the cloud. Also, bandwidth constraints may slow down data transfers and can lead to increased costs, if a business must upgrade its connectivity to improve data transfer.
Nevertheless, some businesses have already adopted cloud BI services, analysts report anecdotally, though specific figures aren't available. Many companies that have made the move say that the benefits -- including fast time to market, no need to maintain on-premises software and simplicity of use -- outweigh any downsides.
Mixing up data sources
Take Millennial Media, which sells a mobile advertising platform. It needed to pull together data from disparate sources, both on site and in the cloud.
Around two and a half years ago, Bob Hammond, CTO for Millennial, began looking into BI as a way to marry data from Salesforce with transactional and financial information from in-house systems and then let decision makers at the company visualize it.
"No human I know of can . . . make business decisions based on data that hasn't been brought together into a single source," he says. The company needed BI, he says, because "we weren't able to take data from multiple systems and connect that data logically and view that data in a UI so that we could understand what was going on."
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," 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.
Weinstein quickly found that some of the large, traditional BI vendors were not going to be able to roll out Athenahealth's initial project quickly enough. In addition, some were too complicated to use, potentially limiting future projects. Athenahealth considered products from both IBM and Oracle, and then moved on to the cloud BI offerings, ultimately choosing Birst.
Athenahealth didn't run into problems with having most of its data stored on-premises and not in a cloud environment. The company has over 50,000 provider clients and tracks more than 100 metrics about each one every day, Weinstein says. That data is pulled from an internal data center into a separate internal data warehouse. From there, the relevant data is uploaded to Birst.
The data uploads happen automatically, several times each day, as part of a process that the company built using tools and scripts, some of which were provided by Birst, he says. "It doesn't keep me up at night," Weinstein says of the process. He has to intervene only if there's an error. "But that is part of our standard monitoring and would be expected as part of a complex data warehouse environment."
Millennial Media, Athenahealth and DMA (see "Early adopter") all say that using a cloud BI service meets their needs. But there are a few roadblocks that companies should look out for when considering cloud BI.
One is "cloud washing." Some vendors say they offer a cloud BI product but in fact may still require software that runs on users' computers or may offer only cloud storage, says Gartner's Tapadinhas. In that case, users may not get all the benefits of a true cloud offering, like offloading software maintenance.
A cloud BI service might also not be as flexible as an on-premises offering. "Although they are quick to deploy, in some cases cloud BI solutions don't offer enough customizations or at least not as much as we have now on-premises," Tapadinhas says.
On-premises products might also offer more possibilities for integration with third party-products, he says. Good Data, for one, has made some strides to allow third-party tools to access data repositories stored with Good, but even its openness is limited, he says.
Plus, traditional BI tools typically have a broader feature set and may make a better option depending on what a company is trying to achieve, says Carsten Bange, founder and CEO of Business Application Research Center, an analyst firm that specializes in enterprise software.
There's also the chance that, like any cloud offering, a particular cloud BI service might be slow. "There are other issues, like performance and latency of cloud solutions," Tapadinhas says.
The transfer speed of data could be slow too. That could impact the reliability of the data analytics if users end up making decisions based on old data because the latest data hasn't made it to the cloud BI tool. "This could be a real bottleneck," Bange says. "Upload speeds are often not really good."
One common reason that companies give for passing up cloud BI -- concern over privacy and security, given that BI products tend to analyze a company's most important data -- actually isn't worth worrying about, some experts say. "Most cloud vendors tend to have more strict security processes and follow security certificates that are more advanced than most companies have internally," Tapadinhas says.
Whether a business goes with cloud BI or an on-premises product, Athenahealth's Weinstein offers valuable advice. Once Athenahealth implemented Birst and workers were able to quickly access useful information, they were spotting a lot more issues than they used to. The company had to figure out how to respond to the increased number of problems that it found. "Net net it's a good thing," Weinstein says. "Just be prepared for what the transparency is going to bring."
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This story, "Cloud BI: Going where the data lives" was originally published by Computerworld.