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 Channel Futures

Cloud


At What Point is Big Data Too Much of a Good Thing?

  • Written by Michael Brown 1
  • November 5, 2014
When data sets become too large, here’s a look at how MSPs can teach businesses how to use it properly and prevent companies from feeling overwhelmed and halting company progress.

There’s no arguing that big data is already making big strides in a variety of fields and industries. From sophisticated predictive analytics, highly intelligent risk analysis and deep insights into consumer behavior, it can play a large role in almost any sector. But when data sets become too large and complex, it can also cause you to think yourself into oblivion.                               

“Analysis Paralysis” is a term used to describe when an individual or company becomes so lost in the analytical process that decisions are never fully made and progress comes to a halt. Although big data algorithms and software have the capacity to group and analyze data in a way that humans can’t, it’s still easy to become overwhelmed no matter what field you’re in.

When data sets become too large, here’s a look at how MSPs can teach businesses how to use it properly and prevent companies from feeling overwhelmed and halting company progress.

Take the life sciences field for example. While other fields such as astronomy, physics and computer sciences have already been dealing with complex, massive datasets for decades, biology was more of a “descriptive science” and is now left with little time to adapt to the massive data sets being generated.

David Relman, physician and microbiologist at Stanford University studies the human microbiome, and describes how the biologists are now generating data at rates that are difficult to keep up with.

“10 years ago, the field was relatively data poor, and scientist could easily keep up with the data they generated. But with advances in genomics, imaging and other technologies, biologists are not generating data at crushing speeds.”

MSPs can help organizations struggling with similar hurdles in the following ways

  • Moving and integrating data – Work closely with clients to find the right cloud model that suits their business goals, map an entire data landscape, and prepare old applications for integration.
  • Storing data – Help clients prioritize data so that data that needs to be accessed frequently is readily accessible and sensitive data that doesn’t is protected and backed up.
  • Analytics – In order to extract full value from data as it becomes available, help clients look at the full picture by setting up analytic and visualization tools so that they can see the full picture before making any critical business decisions.
  • Educate – Educate your clients on factors that influence data, such as social trends, traffic data, season changes and emerging technology. They will better be able to find correlations and relationships between their data sets.

The ability to create entirely new revenue streams, roll out new services based on consumer behavior, or customize websites in real-time are all exciting benefits of cloud services and an MSP can help clients reach those kind of goals quicker than if they were to go it alone. But organization, planning and taking small steps will prevent clients from getting ahead of themselves.

It’s critical to understand it how to harness it correctly for effective results. How else might MSPs help clients understand how to use Big Data properly? Leave a comment in the section below.

Tags: Agents Cloud Service Providers MSPs VARs/SIs Cloud

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