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Why Speed, Simplicity, and Scalability are Important for Big Data Opportunities

May 19, 2017

Why Speed, Simplicity, and Scalability are Important for Big Data Opportunities

Big data has emerged as a means to gain deep insight about business challenges by analyzing structured and unstructured data from a myriad of data sources. As with many new technologies, big data requires a new architecture and a new approach to make the most of hardware, storage, and software. Creating a big data architecture that offers speed scalability, and simplicity presents real big data opportunities for VARs and system integrators.

The Need for Speed, Simplicity, and Scalability in Big Data

Today’s businesses are awash in valuable data that that can be distilled into intelligence to reduce overhead, increase operating efficiencies, and generate ROI. According to research from Dun & Bradstreet, businesses use big data analytics to manage customers, productivity, and markets. The research shows that 62.6 percent of organizations polled use big data to prioritize revenue generation, either as a top priority (24.8 percent) or as a major priority (37.8 percent.)  

The rise of mobile technology, for example, provides more aggregated statistics about consumer behavior, including mobile shopping habits, geographic data, and other details. Grocers are gathering terabytes of consumer data from customer loyalty cards. Retailers are using in-store sensors for real-time inventory management.  And the big data opportunities increase when you start correlating structured and unstructured data from multiple sources in real-time for immediate results.

Harnessing this data in real-time for immediate insights increases big data value, and requires a big data infrastructure that delivers speed, simplicity, and scalability.

The Elements of a Big Data Infrastructure

Delivering big data insights requires combining both infrastructure and analytics in a way that is scalable so you can draw from as many data sources as possible.

Hadoop was created as an open source big data management infrastructure with the capacity to distribute, catalog, manage, and query data across multiple services. Hadoop provides a framework for storing and analyzing massive amounts of distributed, unstructured data, and is the common platform that gives big data speed, simplicity, and scalability.

For IT managers, big data means more data traffic. Every data center node – servers, storage, applications – generates data streams and log files that need to be collected, collated, and analyzed. This also means more storage, although the cost of storage continues to drop so more available storage means more data to analyze.

And in big data infrastructures most of the data traffic is between machines rather than between servers and end users.  Connections need to be optimized for speed and scalability to accommodate machine-to-machine traffic. And big data does not use the same large data sets that are traditionally stored and analyzed using traditional inline transaction processing (OLTP). Big data analysis is done using small, discrete data elements processed using real-time query tools.

Where Are the Big Data Opportunities?

So where are the big data opportunities for resellers seeking to deliver speed, simplicity, and scalability to their big data customers? There are several areas where value-added expertise can mean better big data performance and bigger reseller profits:

  • Architecture – Data centers need to be optimized to handle big data, including accelerating machine-to-machine communications and integrating both on-site and cloud-based data sources for real-time analytics.
  • Hardware – Big data requires more hardware, including more processing power, a new class of servers, and certainly more data storage.
  • Software – Hadoop development is still in its infancy and 60 percent of companies seeking to make the most of big data lack the software expertise to develop the necessary applications and extension.  These companies need custom big data applications.
  • Analytics – Big data scientists who can model and analyze big data will continue to grow in demand as more companies to mine big data for business intelligence.

This is where expert knowledge of hardware, software, and infrastructure pay off. Resellers are uniquely qualified to consult on issues such as designing data centers that can deliver the speed, scalability, and simplicity needed for big data analytics. VARs also can offer the software expertise to support big data analytics simplify big data findings to deliver big insights with big paybacks.

The big data opportunities are in helping customers get the greatest returns from their big data investment. Those resellers with the expertise to show customers where and how to invest for maximum bog data ROI will be the ones who will see the biggest returns.

So do you think you can handle an end-to-end big data implementation, or will your contribution come in focused areas such as hardware, software, or architectural design? How will you support your customer’s next big data initiative?