“It is a capital mistake to theorize
before one has data.”
More than 100 years after it was written, this observation by fictional sleuth Sherlock
Holmes still rings true. Businesses today rarely act – or even contemplate an action –
without analyzing troves of data.
They can make good use of data, in part, because technology has democratized the pro-
cesses that bring information to the end user. Data is no longer gathered only by
research companies and delivered in expensive, structured reports. Now, data gathering
has been commoditized to the point where small, as well as large, companies can ingest
huge amounts of unstructured data from mobile, web and IoT interfaces, and manage it
in the cloud.
Historically, the major big data vendors (e.g., Cloudera) have had their own stacks to
help companies capture, process and manage data – usually on an Infrastructure as a
Service (IaaS) cloud platform. AWS has case studies showing companies using their data
analytics – for example, GE Power, to help power plant customers save money; and
C-SPAN, to identify when individual speakers appear on screen. Lahey Health uses Goo-
gle Cloud analytics to help improve patient outcomes.
In recent months we have been working on a number of big data projects on the Micro-
soft Azure platform. Microsoft has been aggressive on the data front, creating a range
of products tailored for specific customer groups – small, midsize and enterprise busi-
nesses. The big difference is in the shape of the offerings. Microsoft is providing Azure
cloud-based data capabilities through a stack that resembles a Platform as a Service
(PaaS). It has forged tight integrations, right out of the box, between the different data
sources and the stack itself, giving customers a lot of flexibility in their deployments.
With Data Comes Flexibility
Cloud-based data projects are gaining momentum in the marketplace. Once set up, they
are information powerhouses – capturing, storing and processing volumes of data com-
panies can use to gain a business advantage. Organizations can dial the systems up or
down, to ingest big chunks of information, or stream it in little by little. They can do cus-
tom projects on demand, and save time and money on bigger projects they previously
outsourced to data providers.
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