The Business Case for Big Data


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How To Take Advantage of the Big Business Opportunity of Big Data

Table of Contents

  1. The Business Problem
  2. The Business Solution
  3. How to Implement Big Data
  4. Big Data Use Cases
  5. Big Data Risks & Challenges
  6. Big Data ROI & Payback
  7. Final Thoughts and Next Steps

I’ve been fortunate to complete some big data projects in conjunction with ERP deployments that have achieved remarkable ROI. Interestingly, my results with Big Data make it all the more frustrating when I see business leaders sit on the sidelines and somehow wait for this opportunity to make itself happen. In many talks with many smart folks, it’s apparent that most everybody gets that fact that better information leads to better decisions, and that alone can and will grow businesses. But it’s also become apparent that these business leaders need more information support before they can consider such a move, and compare it to other projects vying for their attention. To that end, I’ve put together sort of an executive’s guide to making the business case for Big Data.

The Business Problem

The transformation of business data into business intelligence is a costly and technical process, and consequently limited in scope and beneficiaries. In almost all businesses, data is stored in many data siloes, and getting it through the ETL (Extract Transform Load) process and into data visualization tools is slow, costly and limited to few decision makers in the company.

An IBM research survey found that over half of businesses leaders say they don’t have access to the insights they need to do their jobs. Clearly, this problem isn’t a lack of data availability, but an inability to transform data into the intelligence and insights needed by decision makers.

The challenge is further exacerbated as data increasingly no longer resides in nicely formatted relational databases on company servers. Now to outperform competitors and to achieve the intelligence needed to meet revenue, customer affinity and other business goals, business leaders and line staff must tap into data that resides outside the company and in unstructured mediums, such as online social media, email, audio, video, images, streaming data and more. Data is further being multiplied by machine automation such as sensors, RFID, cameras, programs, phones and other smart devices.

Consider these data facts.

  • 80 percent of the world’s information is unstructured.*
  • Unstructured data is growing 15 times faster than structured data.*
  • Raw computational power is growing so rapidly that a person with a PC has the power of a supercomputer from about a decade ago—and when combined with the new democratization of freely available data this power is available to anybody that chooses to leverage it for business or other opportunities.

* Source: Understanding Big Data, McGraw Hill

The increasing rate of data volumes, velocities and varieties have defined the concept of Big Data, and more importantly a new opportunity to better transform data from raw form into business intelligence.

The Solution

Big Data has emerged as a response to the challenge of accessing and synchronizing more and different types of data across disparate sources in order to achieve holistic views that deliver insights to solve problems.

Unlike traditional business intelligence or analytics solutions which convert data into a common format for subsequent analysis, Big Data normally leaves the data in its native form and instead provides flexible access or synchronization tools to bring data types together for analysis when needed.

At a basic level, Big Data tools empower data access, sync, search, visualization, analytics and mining.

Consider Big Data when:

  • Data obtained from structured, semi-structured and non-structured sources would contribute to better decision making.
  • Data helpful for decision making resides in locations outside the company servers.
  • Data is needed for early exploration analysis and before parameters which define decision making boundaries are known; essentially to discover and then refine data sets later.
  • Solving problems that incur infrequently and thereby benefit from more volumes of data to determine patterns, or correlating or causal relationships, or historical occurrences or trends that are otherwise difficult to detect.
  • Aiding new decision making where the organization lacks internal data needed to make first time business decisions within a reasonable confidence level.
  • Existing data is unusable by inflexible analytics tools because the source data is in a raw, unstructured or denormalized format.
  • Searching for anomalies, or things that didn’t happen as opposed to searching for known events or things that did happen.

Business leaders know the competitive value of information and many are now tapping into this information explosion for profit-driven objectives. Big data is about to become the new normal with regard to decision support. Your choice of when and how to leverage this asset and transform it into intelligence for specific business objectives will determine how and when you are more or less successful than your competitors.

Next – Big Data Implementation >>

Republished with author's permission from original post.

Chuck Schaeffer
Chuck is the North America Go-to-Market Leader for IBM's CRM and ERP consulting practice. He is also enjoys contributing to his blog at


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