You are here:  Home  >  Data Education  >  Big Data News, Articles, & Education  >  Big Data Blogs  >  Current Article

The Future of IT: Big Data and the Expert Integrated System

By   /  February 25, 2013  /  1 Comment

by Anjul Bhambhri

Data: the plural of datum; individual facts, statistics, or items of information.

Big data: a collection of large, complex data sets varying in type, velocity and veracity.

By now, we’ve all heard the statistics: data will experience a 29 fold increase in volume, reaching 35,000 exabytes, by 2020 (IDC); enterprise data growth over the next five years is estimated to increase by more than 650 percent (Gartner); and data use is expected to grow by as much as 44 times, amounting to some 35.2 zettabytes globally (IDC). This blog post itself represents thousands of bytes of data, adding to the near 2.5 quintillion bytes created everyday by online purchases, text messages, traffic cameras and an endless array of other activities and devices that produce data.

In this new era of “data, data, everywhere,” the key to business success is the ability to make sense of Big Data. But businesses are struggling to better manage and analyze this data and intelligently use those insights to support specific business goals. For example, in a study conducted by Forrester and IBM, 25 percent of IT projects are over budget and 34 percent behind schedule. As data grows more and more complex, traditional data analysis simply can’t keep up – these “one size fits all” systems run more slowly and can lead to possible errors or biased conclusions. A simpler, more integrated approach to managing IT is needed.

Enter expert integrated systems. Driven by the need for a more comprehensive, optimized big data platform, expert integrated systems fundamentally change the IT lifecycle by performing complex analytics on big data quickly and efficiently, thereby reducing costs, saving time and resources and speeding innovation for the enterprise. These systems also provide increased flexibility, integrity, availability and scalability for any transactional workload – from managing large financial sets to seamlessly detecting fraud in real-time. With expert integrated systems, businesses can tackle traditional IT pains by making components work together as one system, combining the flexibility of a general purpose system, the elasticity of the cloud and the simplicity of an appliance tuned to the workload.

But don’t let this description fool you – expert integrated systems are far more than “just an appliance.” These systems can incorporate several major components designed to allow businesses to reduce the high costs and increasing complexity associated with managing information technology:

  • “Scale-In” System Design: a new concept in system design that integrates the server, storage, and networking into a highly automated, simple-to-manage machine. Scale-in design provides for increased density at all layers of processing needs, namely compute, storage, and network bandwidth, while providing for ease of administration, upgrades, and maintenance. It provides a way to allow applications to maximize the data used in analysis as opposed to worrying about data placement and transformations.
  • Patterns of Expertise: proven best practices and experiences that are captured into repeatable, automated form, to allow organizations to automatically deploy, manage and optimize systems by configuring, mixing and matching IT resources.
  • Cloud Ready integration: built for the cloud, enabling corporations to quickly create private, self-service cloud offerings that can elastically scale based on demand, and help optimize resource usage, as needed.
  • Clean-slate designs for optimal performance: allow organizations to optimize and innovate in the internal design of each new integrated solution, improving performance, scalability and resiliency, and eliminating the dependence on and use of antiquated architecture.
  • Integrated management for maximum administrator productivity: incorporated unified management tooling and expertise patterns to enable low lifecycle cost of ownership and high administrator productivity. As workload-optimized systems, these solutions embed integrated expertise patterns that help automate and optimize the work of human administrators.

Keeping these principles in mind, managing big data and deploying the IT infrastructure to support it, should no longer be a cumbersome process. In this new era, simple, easy-to-use tools and platforms can help organizations make sense of the ever growing data deluge. For instance, The New York Stock Exchange (NYSE) is applying the expert integrated system approach to store and analyze seven years of historical trading data systems and identify and investigate trading anomalies faster and easier, translating to nearly one terabyte of data per day. As a result, the company has been able to improve simplicity and performance, in turn cutting data analysis time by eight hours.

Organizations today have sophisticated big data challenges that require a systems approach that can provide specific big data analytic workloads depending on the business requirement. Alternative “one-size-fits-all” approaches of applying the same analytics to different challenges is a flawed strategy that will not yield the level of insight needed to make better, faster, more accurate decisions to gain a competitive edge, and sustain success.  Expert integrated systems are the future of IT, helping integrate operations and strive towards one common business goal.


About the author

Anjul Bhambhri has 23 years of experience in the database industry with engineering and management positions at IBM, Informix and Sybase. She is currently IBM’s Vice President of Big Data Products, overseeing product strategy and business partnerships. Previously at IBM, Anjul focused on application and data lifecycle management tools and spearheaded the development of XML capabilities in DB2 database server. In 2009, she received the YWCA of Silicon Valley’s “Tribute to Women in Technology” Award.

You might also like...

Webinar: How to Consume Your Data for AI

Read More →