IBM Power Linux Servers

At the core of any organisation are important IT systems that are vital for continued successful operation. Mission-critical applications, such as ERP, CRM, business intelligence, data warehousing, and analytics, advance and support business in many fundamental ways. In the modern, global corporate landscape, it is almost certain that users will need to access these systems at any time of day, demanding around-the-clock, 24/7 availability. Any outage of mission-critical server infrastructure directly impacts revenue and profitability, so downtime must be avoided.


These are powerful flexible servers which built with the aim to deliver value for diverse workloads and mission critical applications in Linux environments.

Key Features:

  • Investment protection
  • Energy efficient. Shared resources. System optimization. Increased utilization with less risk. 24/7 availability.
  • World-class performance
  • Built-in acceleration. Infrastructure flexibility. Continuous application availability


  • Big data and analytics. Cloud-ready. Open source for SQL and NoSQL databases.
  • Commodity architectures are limited to what they can achieve and clearly cannot meet all infrastructure needs.
  • So in today’s world companies requiring a multi cloud strategy with lower downtime, easier management, and lower licensing costs companies look to IBM’s power on Linux servers to provide what the X86 world cannot deliver.


Although big data promises to dramatically alter the business environment, technology is only a decision enabler. Many firms apply structured approaches to big data for routine operational decisions. However, making un-programmed, critical and strategic decisions usually involves unstructured data. Companies are examining the business value of using big data techniques to gather and analyse unstructured data. This approach uses critical thinking embedded in a process along with advanced servers and software to convert big data into business value. The process and results open new opportunities that require adaptive structures, culture and expertise to fulfil the promise of big data.

Both structured and unstructured data are used in decision making. Secondly we would like to give examples of how companies realized business value using unstructured data. And thirdly to show how companies can realize similar business value using unstructured data and why picking the right servers and software can make a difference

Just five of the dozens of examples of business questions successfully addressed with this process are outlined in the paragraphs below. Each example represents a different type of the most commonly asked questions. Another takeaway from these examples is that the

decisions are not based on quantitative analysis of structured data but rather on the interpretation of the facts derived from analysing unstructured data. The selection of the questions, use of terms and the creation of dictionaries and rules allows one to configure the software to answer a wide variety of critical decisions.

Temporary workforce: Kelly Services Develop new service offerings in healthcare staffing SEC, URLs, trade journals, professional journals, insurance providers Decision to move forward in an unexpected healthcare domain Industrial Gases

Air Products Find new customers and market opportunities SEC, news feeds, industry publications, building permits Identification of a new customer planning to build new facilities University

NC State Identify commercial partners for new technologies SEC, URLs, industry publications Potential partners identified for collaborations Clinical Research Organization

PRA International Provide business intelligence for new clinical trials Clintrials, PubMed Identify new physicians/hospitals with expertise in areas of clinical trials Non-Governmental Organization

Clinton Health Care Access Initiative was to ,Find the fit between new technologies and market opportunities for disease diagnostics Clintrials, PubMed VC firms Identification of research labs active in cutting edge diagnostic research.


It is critical to choose the right software to conduct unstructured text analytics. There are many commercial and open-source programs that can be used. If you do not choose a program that has a graphical user interface and the ability to seamlessly interact with large data sets, you will need data scientists just to run the technical part of the software.

IBM Content Analytics Studio (ICA) software uniquely meets all the criteria to allow business content people to engage in big data analytics of unstructured data for themselves. With a few days of training, most business people learn to use ICA well enough to apply it on a continuing basis in their area. Thus, big data can be used routinely by a wide variety of people rather than on a special-project basis.

It is also important to use the right server platform for big data. Although we seek to isolate highly specific information to make decisions rather than aggregate large data sets, we can only identify that critical piece of information if we can gather it and process it in a timely manner. Consequently, choosing the right server platform can be a crucial aspect of deploying a successful unstructured big data project.

Some companies just getting started decide to use x86 because they already have the servers installed and the big data software, including ICA, runs on x86. There are profound limitations, however, to this approach. While x86 servers may suffice for small demonstrations, the low reliability of the platform hinders adoption of big data. You may not be able to actually demonstrate the fully value of big data on x86 servers. In todays world companies use both x86 and IBM Power Systems servers for processing big data. There is no doubt that the Power servers are far superior. The x86 servers crash so often that we chose not to use them with our clients.

Power System server, with the POWER8 processor-based technology, was designed for big data to run more concurrent queries in parallel faster, across multiple cores with more threads per core. It also has increased memory bandwidth and faster IO to ingest, move and access data faster. This allows companies to run these data-hungry analytics queries faster.

Unstructured data analysis can be the source of great business value if the appropriate processes, tools, skills and structure are implemented together. Frustration and disappointment await you if you do not implement these elements together. The cost of failure is to fall behind competitors that are successful at implementing big data.

The key to a successful implementation is to establish a simple process for business content experts and decision makers to use on a regular basis. This means that both the inputs and outputs of the decision-making process must be appropriately resourced and roles and responsibilities established. You must establish a reporting relationship with decision makers to ensure accountability. Performance and outcome expectations must be created for what your company wants from big data. The right infrastructure must be used to ensure the required levels of performance and reliability and met. A proper implementation promises business value and competitive advantage by arming decision makers with more targeted information.

For more information on IBM’s Power on Linux, please contact Andy Wynne at Recarta on 07786 927254

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