Avoid turbulence when shifting to data analytics in the cloud

Migration of BI and data analytics to the cloud needs to proceed with caution. That’s because so many issues have to be taken into account: examining existing analytics processes, choosing the right cloud tools, protecting information, ensuring data quality and, most importantly, establishing well-conceived goals.

The cloud offers the kind of benefits that on-premises alternatives are hard-pressed to match — more agility, faster development and deployment of new technologies, and greater potential cost savings. Most successful organizations migrating to the cloud have well-defined visions and strategies for the role they want BI and analytics to play in their intelligent enterprise, said Steve McHugh, director of product marketing for BI and hybrid analytics at SAP. “It is a move of innovation for them,” he added, “not necessarily a ‘lift and shift’ of what they currently have on premises to the cloud, although some companies are interested in doing so.”

Companies need to be aware that data analytics in the cloud is not just about cost savings, but also about new possibilities. “With access to scalable, stable infrastructures without the overhead of maintaining them, you can scale to the level of analysis needed for that moment, with minimal startup cost for experimentation,” said Mitch Gibbs, a consultant at Candid Partners, a cloud services firm. He recommended that analytics managers devote resources to the process of experimentation to determine the analytics approach that offers the most return and to invest in it. “Don’t think of analytics as a one-time build of an application,” Gibbs advised.

“Instead, design your system and process to evolve as the business needs change.” The next step is to set valuable, achievable goals for data analytics in the cloud, such as cutting BI and analytics costs, accelerating queries, boosting user concurrency, improving the quality of decision support and automating delivery of data-driven insights into business processes.

“Don’t migrate your BI/analytics away from your on-premises platforms if you don’t have a good handle on what you’re trying to achieve,” said James Kobielus, Wikibon lead analyst at SiliconAngle Media.

Don’t think of analytics as a one-time build of an application. There are many SaaS-based BI and analytics tools to consider, and they range widely in features, price, performance, geographic availability, industry and applications. Setting goals can help create a shortlist of providers to target early in the migration initiative.

“Perform a due diligence comparative evaluation of these [providers and product features] before deciding which one will be your migration target,” Kobielus said. It’s important to decide whether your company is just moving operational reporting or if it’s also migrating predictive modeling, data mining, machine learning and other advanced analytics applications to the cloud.

Prepare for a migration project that may take longer and cost more than expected, Kobielus noted. The project is likely to be more complex if migrating many databases and a huge collection of analytics that need to be rebuilt essentially from scratch for the cloud. Here are some important considerations when identifying migration expertise and selecting tooling: Are you migrating every legacy BI and analytics app, or are you planning to decommission many of the underutilized ones as part of the migration? Do you have the requisite in-house expertise and tooling to do the migration properly, or do you need to bring in a consultant? Does the provider of the target cloud have professional services and tools to assist your migration?

It’s important to assess the data management infrastructure and security around the existing data.