Managed Services Look to Plug Big Data Skills Gap


A lack of skills in advanced real-time analytics among IT workers means some enterprise teams are moving to managed services to fill the gaps.

A shortage of advanced IT skills needed to drive adoption of big data analytics applications running at scale is driving many organizations to embrace managed services. To capitalize on that opportunity Instaclustr this week added a managed Kafka service to its existing portfolio of Cassandra Spark, Lucene, and Elassandra services.

While interest in open source big data platforms for driving next-generation applications is high, enterprise IT organizations are finding they can’t hire or retain the IT personnel that have the skills required to provision and maintain these platform, says Instaclustr CTO Ben Bromhead. As a result, many of those organizations are turning to third-party service providers that make these platforms available as a complementary set of services. The only consistent requirement organizations have to make sure the services are based on open source software that prevents them from getting locked into a specific provider, says Bromhead.

See also: Hybrid integration platforms – using the cloud and managed services

The managed services provided by Instaclustr can be deployed on cloud service from Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, or IBM Cloud to further reduce any potential lock-in concerns, adds Bromhead. Those services are supported 24/7, which Bromhead notes is another issue internal IT can’t address because at some point the IT staff goes home.

Instaclustr is looking to differentiate itself as a managed service provider by focusing solely on big data application. “We’re focused on the data layer,” says Bromhead.

Most organizations are starting to come to terms with two core IT issues. The first is that both internal and external data is a business asset that can be used to drive better decisions faster. Rather than making a business decision based on 10 percent of the available data it’s safe to assume that decisions based on 100 percent of the available data are going to accurate more often.

In fact, it’s the ability to cost-effectively aggregate data that is driving a new wave of artificial intelligence (AI) applications based on machine learning algorithms. Big data platforms can process data at scale cost-effectively address that issue.

Managed services help with growing data velocity

The second issue is the velocity at which that data is being generated. Organizations increasingly want to process data real-time, which is leading to investments in technology platforms such as Kafka and complementary streaming analytics applications. Given the amount of data involved those applications are being deployed in a hybrid fashion both at the network edge and in the cloud. In many cases, Big data platforms, however, are still too large to deploy at the network edge, so over time MSPs will need to weave together a complementary portfolio of technologies spanning everything from the network edge to the cloud.

In the meantime, Instaclustr is clearly focused on managing big data platforms hosted in the cloud. In the future, it’s even probable some organizations will have big data platforms hosted in multiple clouds. But for now, the biggest issue many organization now face when it comes to big data is not the technology itself, but rather the lack of skills needed to turn all the data into something truly approaching actionable intelligence.

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