AUTHORS

Brandon_Mills-10924_headshot_small
Brandon Mills
Manager,
ZS Associates
Jason_Bell_11099_headshot.jpg
Jason Bell
Associate Principal,
ZS Associates
John_DeSarbo_thumbnail
John DeSarbo
Principal,
ZS Associates
Kyle_Heller_thumbnail-1
Kyle Heller
Associate Principal,
ZS Associates

Latest Posts

Five Keys to Harnessing (Not Hyping) Big Data

Posted by Bhargav Mantha on Wed, Feb 13, 2013

harness big data bhargav mantha
I defined Big Data in my last post and why you should care about it.

Now, the next big question: How do we prudently invest to realize the promise of Big Data and not get overwhelmed by the hype?

Here are five critical answers:
 

1. Business case first, please: Identify which parts of the business would benefit from expanding the data set to provide more complete answers. Determine if there is a way Big Data analytics can help monetize a part of your business you previously could not. The business cases for investing in Big Data could vary. They could be business process-specific, such as:

  • Improving customer experience
  • Optimizing R&D
  • Managing IT

They could also be industry specific:

  • Optimizing price or channels for the tech industry
  • Detecting fraud for financial services
  • Managing IP for the media industry

Finding a business-driven initiative with measurable outcomes—whether improved customer retention, increased revenue from improved sales/channel productivity or even cost reduction—will improve the success rate of your Big Data initiative.
 

2. People, people, people: After you’ve developed the business case, start with a thorough skills assessment, since newer analysis and technologies may require different skills and/or talent. There are three particular roles (and associated competency models) that you could define for this initiative:

  • The data scientist, who applies her statistical, mathematical and computer science skills to work on large, complex data sets to find, interpret and distribute statistically significant information.
  • The business analyst, who blends his business understanding with data acumen to determine what information is important for the business and how to bridge the IT or data science gap.
  • The technologist, with skills to identify and assemble the right set of Big Data technologies and developers to deliver on the business initiative.

3. Technology, of course: Repeat after me, “Big Data does not equal Hadoop.” It is more than that. While Hadoop, the open-source software framework, has the greatest name recognition, Big Data is too varied and complex for a one-size-fits-all solution. Other classes of technologies are equally well suited to managing Big Data. NoSQL (not only SQL) and MPP (Massive Parallel Processing) stores come to mind. Again, it goes back to which of the four V’s pose a greater challenge for you, and which of these technologies supports the business case. In fact, there is no requirement for you to invest in your own infrastructure—you can also explore options for a cloud-based service, such as Google BigQuery, and save lots on infrastructure costs.
 

4. Start thinking social: Big Data could be an important component of your social-media strategy, especially when it comes to understanding customers, prospects and key influencers. Social media allows for ongoing engagement, which can provide near real-time insight into customer attitudes and behavior. Social data, collected and analyzed, could enable you to rapidly identify trends about who uses your solutions; what customers and prospects think about your brand and solutions, as well as your competitors’; and emerging market insight. Engage with your CMO to discuss how Big Data and social media can work together to measure and improve the ROI of your social-media efforts.


5:
Don’t treat Big Data as mission-critical right away: While veracity of Big Data will become more and more important, don’t treat social data or wiki data like mission-critical financial data right away. Apply the right level of control to their use and exposure. The initiative may well be stifled from day one if you apply the rigor of the requirements gathering and process management of traditional data warehouse projects. Instead, let the process be iterative and collaborative where business and IT explore interesting sources of data, refine what is important and apply the right algorithms. Better outcomes are driven when an organization conscientiously allows Big Data initiatives to be iterative, exploratory and even transient in some cases.

Big Data does present a growing opportunity to understand and change interactions with customers, improve existing business processes, to launch new lines of business and to reevaluate how and why data can improve decision-making processes. Using these guidelines, you should think hard about when, where and how to best realize Big Data’s value within your organization.

Topics: big data, Bhargav Mantha, data management

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AUTHORS
Brandon_Mills-10924_headshot_small
Brandon Mills
Manager,
ZS Associates
Jason_Bell_11099_headshot.jpg
Jason Bell
Associate Principal,
ZS Associates
John_DeSarbo_thumbnail
John DeSarbo
Principal,
ZS Associates
Kyle_Heller_thumbnail-1
Kyle Heller
Associate Principal,
ZS Associates
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