We’ve recently worked on a case study with HP for their line of ProLiant Gen9 servers.
In our testing, we found up to 40% improvements in compute performance for Gen9, so it made perfect sense for us to integrate the line into the Full Metal Cloud. It’s just one of the steps we took to ensure that the Full Metal Cloud continues to be the highest performance cloud in the world.
The management capabilities of HP hardware also enable us to provide easy orchestration and scalability. Together with our performance promise, they support us in making the Full Metal Cloud an excellent platform for getting started with big data in a fast, secure, and scalable environment.
By most reports, the healthcare industry has been slower than most in adopting the cloud. However, some key changes recently have marked a rapid acceptance of the cloud among the majority of healthcare providers, and if the pace keeps up the healthcare industry could lead the way to other industries accepting cloud services. Most notably, new studies show that 80 percent of healthcare agencies have or plan to have some type of cloud service in place, and 83 percent are using cloud-based applications. Here are the driving factors for healthcare embracing the cloud. Continue Reading
It’s hard to find an industry that big data isn’t making an impact in, but the world of manufacturing showcases the operational power of big data like no other. In manufacturing, big data analysis is being used to improve production while helping eliminate waste and lower costs. This is particularly evident in the sectors of biopharmaceutical production, chemical manufacturing, and discreet manufacturing. Here are just a few of the many ways that big data is revolutionizing the manufacturing industry. Continue Reading
The future of Big Data is even Bigger Data, according to Mark Csernica, technology analyst for Mind Commerce, a research and consulting firm that supports technology and telecommunications companies.
To clarify, Mark offered us a glimpse at the expected scope of data growth: the world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 quintillion (2.5×1019) bytes of data were created. It is estimated that by 2020, there will be 20 Zettabytes (20 x 1021 bytes) of data available.
To accommodate the information explosion, Mark says that Big Data technology and techniques will have to be continually evolving.
“The Big Data technologies that are working today might have difficulties in processing the data files/sets of the future, which will continue to grow,” he says. “I expect new data processing paradigms to be developed in order to process larger and larger data sets in reasonable time frames.”
Here, Mark offers his thoughts on why businesses should care about Big Data and what they can do
to harness it today and in the future. Read on:
Tell us about Mind Commerce … what services do you offer? Who should be using them?
Mind Commerce provides research and consulting services of digital technologies and the telecommunications industry. Our reports provide analysis on technologies and emerging business opportunities. For clients looking for more specific details, Mind Commerce provides follow-up consultation and advisory services on an as-needed basis. The Mind Commerce customer base includes technology companies, service providers, enterprise, government agencies and NGOs.
What seem to be the most common problems your clients hope you’ll help them solve?
Our clients are looking for trends, projections, and analysis on technologies, infrastructure, devices, applications, services. Our clients want to understand business models involved and emerging business opportunities that they can take advantage of.
Why is Big Data so important to the growth and survival of businesses today?
The importance of Big Data technologies is due to businesses’ need for identifying and taking advantage of business intelligence. Identifying gems of business information out there in the vast expanse of businesses’ data repository and on the Internet. This business information is on databases, blogs, articles, analyses, surveys, etc. Gems of information that are currently hidden, but if found are a source of market intelligence and customer preferences. And when used, they allow a business to spot market trends, strengths and weaknesses of one’s product line and identify tactics to gain competitive advantage.
But there are technical challenges with Big Data. The collection of data files/sets are so many, so large and complex that it becomes difficult to process them using existing database management tools and traditional data processing applications and techniques in reasonable time frames.
How can businesses better harness all the data available to them today?
For businesses to better harness the huge volumes of data available to them, they must develop the ability to discover a unique data insight. That is, a business needs to develop the ability to ask new questions, formulate new hypotheses, explore and discover how they plan to use the data available to them.
Ultimately, a big part of a business’s Big Data efforts is the use of new analytic techniques, on either new data or data that has been combined in new ways. And deciding how to analyze that data. Big Data technologies offer various approaches in capturing, storing, searching, analyzing the tremendous volumes of data available in reasonable time frames. Identifying and implementing the technology that best fits a business’s needs is a key component of a business’s Big Data strategy.
Organizations that address these areas will have the ability to take advantage of business opportunities, minimize risks and control costs.
What are some of your favorite tools, resources or techniques for managing and analyzing available data?
A technique I find interesting is using Cloud technologies and paradigms to provide the multi-processing environment needed to perform analytics on Big Data streaming data. This is achieved by setting up IaaS environments and allowing analytic programs to run on virtual machines. The objective in this approach is to define a system that offers modular growth and expansion. These techniques center on taking the data that was just generated, performing analytics on the data you need, throwing away the remainder of the data and saving the analytic results.
This technique appeals to me because the data is not saved first, then analytics performed, as is done in traditional analytic processing methods. Performing real-time analytics on your source data, saving disk storage and processing time. But there is a down side. This means your analytic programs must run 24-7. This requires system redundancy to address outages and downtime.
What do you think are the most common mistakes businesses make in managing the data they collect?
The most common mistake businesses make is not defining and maintaining a clear and precise Big Data strategy. They skip this step and just trying to implement solutions. Businesses need to define how they plan to use the data available to them to run their business. This includes what data they need, how they want to analyze it and use it.
What should businesses be doing to adapt to the age of Big Data?
Minimally, businesses need to define and maintain a clear and precise Big Data strategy to define how they plan to use the data available to them to run their business. This includes what data they need, how they want to analyze it and use it. Considering Big Data is an emerging technology, businesses need to include in that strategy a plan to migrate to Big Data technologies.
What are some of the most interesting ways you’ve seen businesses and organizations leveraging Big Data to their benefit?
ATT is implementing the Cloud technologies technique I mentioned above to make the data generated in their networks (eight to nine Terabytes daily) available internally to their operations and to clients. Making this data available to clients allows ATT to monetize this information.
You don’t need to have massive data sets in order to start harnessing the power of Big Data, says Mark van Rijmenam, hoping to clear up one of the most common misconceptions of Big Data.
The key to creating a successful Big Data strategy is to combine different data sources, which don’t have to be extremely large. “That’s why I like to call Big Data actually Mixed Data, as to truly get advantages out of Big Data you should mix different data sources,” the co-founder and CEO of DataFloq adds.
Mark is passionate about the possibilities of Big Data, which is why he helped created a site that purports to be the One-Stop Shop for Big Data.