Innovation Through Infrastructure

May 3 2017   3:06PM GMT

Four key mistakes to avoid in implementing big data analytics

Michael Tidmarsh Michael Tidmarsh Profile: Michael Tidmarsh

Big Data

Competitive advantage is increasingly determined by how quickly and effectively organizations can leverage advanced analytics and insights to drive measurable results. McKinsey describes this as “The Age of Analytics” and says the critical question facing companies today is how to “position themselves in a world where analytics can upend entire industries.”[1]

Despite the growing importance of big data analytics, many organizations are still figuring out how to maximize the value it can deliver across the enterprise. According to one survey, nearly 50% of big data decision-makers said they are not leveraging big data extensively across all business units.[2]

Where are they coming up short? Here are four mistakes that can impede big data analytics efforts:

  1. Not having a plan. Big data analytics is not just a technology issue; it is also cultural. Management needs to buy in, and new processes have to be baked into the culture. If you don’t have a strategic plan in place, you won’t know which technologies to invest in or be able to establish the necessary governance and data management policies and practices.
  1. Not focusing on talent. Big data analytics requires specific skill sets. The Harvard Business review has called data scientist “The sexiest job of the 21st century,” but, in reality, there is still a shortage of individuals with the knowledge and experience to drive enterprise-wide deployments. Make sure you either have the talent in house or are working with technology partners that can supplement the skills and experience of your own teams.
  1. Not modernizing your infrastructure. The need for speed and accuracy in analytics puts intense pressure on the underlying infrastructure, particularly for workloads that have larger datasets and growing volumes and varieties of data. This is one of the reasons why companies are rushing to embrace All Flash storage and hybrid cloud storage solutions.
  1. Not upgrading the server platform. Because the storage infrastructure is so central to the delivery of big data analytics, many IT leaders believe that if they invest in the right storage platform their infrastructure challenges will be addressed. This is not the case. Big data analytics also puts enormous pressure on the compute infrastructure for processing speed, reliability, operational simplicity and resiliency.

Leveraging modern servers for analytics workloads

Modernizing the server platform is one of the first steps that organizations can take to support big data analytics. As you build your strategic plan and bring on the requisite talent, having a modern server infrastructure in place will accelerate your ability to deliver real-time actionable insight to your managers, employees and customers.

Many organizations are finding that they can drive immediate performance gains in their analytics workloads by using integrated server and software platforms that have been designed specifically for big data environments.

As an example, IBM’s high-performance Power Systems Linux-based servers are now available in configurations designed specifically for big data and analytics workloads, including packages for SAP HANA, NoSQL, operational analytics, data warehouses and several others. Research shows that there are clear advantages to modernizing your analytics infrastructure with these types of solutions, including:

  • Increased performance: The IBM Power8 server has demonstrated 1.8x faster per-core performance for SAP HANA compared to x86 platforms, resulting in faster and more efficient analytics of business data and setting a world record in the 2-billion record category.
  • Accelerated time to value: Organizations can save setup time and maintenance costs by utilizing a complete pre-assembled infrastructure that has been designed specifically for analytics workloads with pre-installed and tested software.
  • High reliability and resiliency: As companies increase their reliance on analytics to drive business initiatives, uptime becomes more important than ever. IBM Power Systems are designed for 99.997% uptime and use self-monitoring and predictive failure alerts to pre-emptively migrate applications before failures occur.
  • Flexibility and agility: Organizations should be able to leverage either a scale-out or scale-up architecture for their analytics workloads, while also incorporating features such as server virtualization and support for multi-tenant cloud functionality.

Turning information into power

In the years ahead, competitive advantage will increasingly go to those organizations that are best able to use their information to create business value and drive innovation through real-time analytics and insight. The foundation IT puts in place today will go a long way in determining the success of the business in the future.

In order to put that foundation in place, IT leaders must address the potential pitfalls discussed in this article, namely:

  • Put a strategic plan in place.
  • Make sure you have the right talent, either on staff or through your tech partners.
  • Modernize your infrastructure.
  • Upgrade your server platform.

Any successful analytics initiative will be built on an infrastructure foundation that can deliver the requisite performance, capacity, scalability, reliability, resiliency and agility. As you’re building your plan and hiring the right talent, make sure you are investing in infrastructure solutions that will give you the best chance of success.

[1]The age of analytics: Competing in a data-driven world,” McKinsey& Company, December 2016

[2]Survey of Big Data Decision-Makers,” Attivio, May 25, 2016

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