Rethink BI : Business Insights over Business Intelligence
The purpose of this business insights thought leadership blog is to share the HP point of view on industry trends such as Big Data and Real Time Analytics, and provide updates on key innovations and solutions.

Four key considerations for keeping up with Big Data overload

Despite the numerous offerings introduced this past year, Enterprise IT is still struggling with efforts to control Big Data. To get a glimpse how HP is meeting the Big Data challenge, we asked Manoj Suvarna, Director of Product Management for HP Converged Systems to give us an overview of the current landscape.

 

How do you feel enterprises are keeping up with Big Data overload?

Suvarna: When we talk with clients even today, what we’re noticing is that Enterprise IT is at an inflection point when it comes to Big Data. Many enterprises continue to struggle to evolve from the pilot to production phase successfully. Much of that can be attributed to the fact that they failed to plan in advance. HP has found four key considerations enterprises need to keep in mind as companies start building the foundation for Big Data. Each area poses unique challenges for IT managers and the systems they support:

 

  1. datacenter.pngCost – While there are a lot of Open Stack solutions that are available in the market, dealing with Big Data continues to requires a different skill set. This skill set includes process management with corresponding  resources necessary to incorporate the solution into the existing architecture, which is  critical.
  2. Complexity – Integrating the Big Data solutions into the corporate environment, or establishing them as a formal IT project, is something that is fairly complex. This becomes more challenging when existing solutions are expanded or integrating into an overarching enterprise environement.
  3. Speed – IT managers are concerned about how quickly Big Data solutions can be deployed. Some architectures take a matter of weeks, and some a number ofmonths. Time-to-completion for integration into these larger enterprise environments is really determined by the complexity of the solution.
  4. Risk – Big Data is typically sourced from a number of different origins, which leads to security and compliance issues needing to be addressed. Compliance and security are increasily important, especially when consolidating data into a single pool—be it text data, social media data or traditional email. This leads to supporting both integration and processing software that rides over the top of these massive data pools.

  

To understand more about the Big Data opportunity, check out this whitepaper with a point of view on information management.

 

About the contributor

Manoj Suvarna is director of HP Converged Systems, part of the Enterprise Group at HP. In this role, he is responsible for a portfolio of purpose-built appliance solutions for Big Data (including Hadoop) and SAP HANA.  Manoj’s responsibilities span across solutions development, product marketing, value-chain development and go-to-market activities, as well as sales enablement and strategic business planning with key alliance partners for joint research and development.

 

Leave a Comment

We encourage you to share your comments on this post. Comments are moderated and will be reviewed
and posted as promptly as possible during regular business hours

To ensure your comment is published, be sure to follow the community guidelines.

Be sure to enter a unique name. You can't reuse a name that's already in use.
Be sure to enter a unique email address. You can't reuse an email address that's already in use.
Type the characters you see in the picture above.Type the words you hear.
Search
About the Author
About the Author(s)
  • Technology marketing professional with over 25 years of experience in energy, semiconductor and IT industries.
  • hp.com/go/convergedsystems
  • Jeff Spiller has over 30 years experience in architecting highly available and scalable multi-tier platforms for a variety of Fortune 500 companies. He is currently HP’s technical lead for the Enterprise Data Warehouse (EDW) Appliance, optimized for Microsoft Parallel Data Warehouse (PDW) software. As a member of HP's ESS Performance and Solutions Engineering COE (Center of Excellence), Jeff has a proven track record for designing, tuning and performing capacity planning in OLAP, ROLAP, OLTP and consolidated environments.
  • Focused on cloud, virtualization and appliance solutions for HP technology.
  • HP Servers, Converged Infrastructure, Converged Systems and ExpertOne


Follow Us