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Interest in Microsoft big data strategy grows as customers push limits

Microsoft partners are looking to the vendor's big data tools, such as the Azure Intelligent Systems Service and Azure Machine Learning, to meet the needs of increasingly 'data-driven' customers.

Perhaps not surprisingly, Microsoft is making a big push into big data. According to the company, as much as 85% of new data is unstructured, and many businesses lack internal expertise to take advantage of the data they could derive from customer relationship management (CRM) and other systems.

"Microsoft's vision is to enable everyone -- and every company -- to have a data culture with technology accessible to all," said Herain Oberoi, director of product management for Microsoft Data Platform, in a statement. "Microsoft's data platform includes the building blocks customers need to process data anywhere it lives and in the format where it is born."

Data solutions help capture and store large volumes of diverse data, whether structured, unstructured or machine-generated. Microsoft's solutions have in-memory capabilities in SQL Server 2014, relational database capabilities in the cloud through their Azure SQL Database, and Hadoop-based solutions through Azure HDInsight in the cloud or the Analytics Platform System appliance, according to Oberoi.

Solutions include Microsoft's Internet of Things (IoT) Azure Intelligent Systems Service (AISS) "to securely connect to, manage and capture machine-generated data from sensors and devices, regardless of operating system, [as well as] Power Query in Excel to find and combine data from diverse sources while sharing those views across an organization in a shared Data Catalog," he said.

Machine learning is "a huge area of investment" for Microsoft, Oberoi added, and the company just announced the preview of Microsoft Azure Machine Learning (ML). Azure ML will include a visual design studio, application programming interface service and all the benefits of the cloud.

In a keynote address from the SQL Server 2014 launch event, Microsoft CEO Satya Nadella discussed the integral role that big data plays in Microsoft's overall strategy, and how a mobile-first, cloud-first world drives everything the company is doing. The increasing focus on intelligent computing, Nadella said, will usher in "the era of ambient intelligence" and requires a platform to bring disparate technologies together.

"Machine learning … [improves] our ability to reason using statistical methods that we have not [used] in the past," Nadella said.

Focusing on big data products

We're all-in on Microsoft's [big data] platform, and we recognize the cloud's importance.
Adam JorgensenPresident of Pragmatic Works Consulting

Microsoft's big data strategy means new business opportunities for channel partners to sell its big data products.

"We're all-in on Microsoft's platform, and we recognize the cloud's importance," said Adam Jorgensen, president of Pragmatic Works Consulting, a systems integrator and Microsoft partner. He estimated that 30% of their business is now focused on Microsoft's big data products, up from 5% a year ago.  Interest is growing among "data-focused" customers, because Pragmatic Works "ran into limitations with traditional systems," he said. "We work with companies who want to push beyond what a traditional relational database could support."

It has been "tough for average companies" to find products and staff with the expertise to do the type of deep analysis they want to do on their own, Jorgensen said. Pragmatic's sales pitch? "'I know we did a great business intelligence implementation for you a year ago. Let's do predictive analytics and see who your most valuable customers [are] and look at different things across different industries.'"

Depending on how a customer wants to deploy a big data solution, they may end up with something on-premises instead of in the cloud, he said. In that case, Pragmatic typically recommends a Hortonworks data platform implementation, which is Microsoft's Hadoop open source processing solution.

Big data, said Jorgensen, is basically just a platform that needs to be integrated with other tools to be useful. "What Microsoft has done, and I think smartly, is [it has] come out with the right suite of tools that take advantage of that Hadoop infrastructure in the cloud." The company's "forward-looking products" are Azure ML and AISS. Azure ML has proven "pretty strong" when deployed for customers focused on solving things like why their customers aren't buying as much, he said. "It's a whole different way of looking at advanced analytics, frankly," Jorgensen said of the Microsoft big data strategy.

Demands for further insight

Microsoft partner T4G Limited is also seeing more interest from customers on Azure and Hortonworks, said Stephen Giles, principal consultant at T4G.

"We're over the 'hype' stage, and we have customers now that are seeing the possibilities" in industries, including retail and financial services, Giles said. Even if they already have good business intelligence tools, there is a "whole other block of data that hasn't been accessible to them," he noted. "They want to see what further insight they can glean from this big mass of Web blogs and survey data and telemetry data … which was traditionally too hard to get at with traditional tools in a business intelligence system."

Giles, who came from Microsoft and was part of the team that brought Azure to the partner channel in Canada, said the big data product suite is a "very logical offshoot of what we're already doing [in analytics]."

It's a challenge to find data scientists. "The reason is the talent pool is very limited," Jorgensen said. Azure ML provides the abstracts of a lot of statistical analysis that would otherwise require someone with a doctorate because of the algorithms Microsoft has built, he said. "Microsoft has eliminated the need for that one giant brain persona and is giving you a suite of products that tie in well with your existing development team," he said. "You don't need a Ph.D. in quantitative statistics."

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Looking forward, can you identify potential weaknesses in Microsoft's big data strategy?