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The New Era of Big Data Management: Where to Start

By   /  September 5, 2012  /  2 Comments

by Anjul Bhambhri

The onset of big data has created an evolution that is shifting how organizations manage their data. Where the relational database once ruled as the primary data management solution, the era of big data is changing the data that business look at, the people working with it, and the technology and skills needed to manage and understand it. Most importantly, it presents new opportunities for businesses that are able to gain valuable insights from this data to inform their decisions.

In today’s business environment, it is ’all hands on deck’ to drive business outcomes, therefore there is a growing need for everyone within an organization to understand and apply big data.  Students are currently taking data management and analytics courses to prepare for the work force, data scientists are learning which questions to ask of the data and chief marketing officers (CMOs) are learning to make strategic business decisions based on new insights.

Data that used to reside solely in the IT department is now being discovered by the C-suite as providing additional business possibilities, and this trend will continue as more companies look for new ways to transform within their industries and redefine their success metrics.

With this new era of big data management, it is becoming imperative for all organizations to develop a program to not only capture all of this available data, but pull out valuable insights that can reduce costs, manage risk, and better serve customers.

Here are five key steps businesses in any industry can take to get started with big data today:

  • Step 1:  Define the challenge and understand the opportunity that big data presents for the organization. Enterprises have so much data at their fingertips – from online transactions to videos to social media data. Being able to comb through this data and pull out patterns to gain insights and make critical decisions will give them a true competitive advantage. Organizations need to define these opportunities, and establish the larger end goal – whether it is cost savings, increased ROI, or reduced risk – to first begin to establish a big data program.
  • Step 2: Discover the data that needs to be analyzed and where it is located. Depending on the industry, different types of data are more critical to the organization than others. These various types of data need to be located and analyzed in order to obtain critical insights. For example, Pacific Northwest National Laboratory (PNNL) must analyze terabytes of smart grid and climate data, while simultaneously detecting error and cyber security threats. On the other hand, Trident Marketing is increasing retention by looking at an array of customer data as well as social media data to help CMOs get closer to customers. I recommend a federated approach to big data, taking the analytics where the data resides. It’s faster and more cost effective than stuffing all the data inside a data warehouse.
  • Step 3: Obtain the business buy-in. As big data is no longer just an IT issue and affects the entire business as a whole, it is necessary to start with the C-suite and get them to apply big data to growth opportunities. Show them the larger implications that their data presents for the organization. Map out potential savings and increased ROI that will impact the bottom line and future growth of the business. This is the secret to getting buy-in.
  • Step 4: Establish the right technology. A critical step in the development of a big data program is investing in a big data platform that is scalable and can take information from any data source. This technology should provide a single view of the data by taking disparate systems and bringing them together, simplifying access to trusted data and keeping information out of silos.  Companies need the core analytics capabilities to be able to ask the right questions of their data and build a strong foundation for their program. The platform can be reused for future growth.
  • Step 5: Ensure the right team and skills are in place. Having people with the right skills is equally as important as having the right technology. Building out a data scientist role or data science team will foster collaboration among the organization by working directly with the CMO or CIO to advise them on how to derive the maximum business value from their organization’s data. Skills must be a mix of business (or domain) and technical expertise. Aim for a balance in these skills.

Once the big data program has been implemented, the organization can then leverage the program for new opportunities and look for ways to expand it. It is important to monitor progress consistently to examine areas of success, as well as areas that need improvement in order to remain competitive. In an era where data management is constantly evolving, setting the foundation for big data today will help ensure an organization will thrive and produce actionable results in the future.

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.

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