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Data Mining Authors: William Schmarzo, Robin Miller, Progress Blog, Rostyslav Demush, Jnan Dash

Related Topics: Data Mining, Big Data on Ulitzer, Internet of Things Journal

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Demystifying Big Data | @BigDataExpo #IoT #Cloud #BigData

You don’t need a Big Data strategy; you need a business strategy that incorporates Big Data

The Dean of the University of San Francisco School of Management, Elizabeth Davis, recently asked me to sit on a Big Data panel at the Direct Sales Association conference. I was given a 5-minute slot to “demystify” Big Data to a non-technical group of about 1,000 people; to help them understand where and how this thing called “Big Data” could help them.

Well if you know me, I can barely introduce myself in 5 minutes. But this was particularly challenging for me, as I’m used to talking about Big Data with organizations with at least some level of Big Data experience or understanding (maybe they should get my second book – the “Big Data MBA” – and start there!).

So I accepted the challenge, and here is what I said (and yes, I did it within the 5-minute window).

Myth #1: Every Business needs a Big Data strategy.

Reality #1: You don’t need a Big Data strategy; you need a business strategy that incorporates Big Data.

While that may sound like a “play on words,” to be success with Big Data requires a different perspective – a business-first perspective.  Whether that’s optimizing a key business process or uncovering new monetization opportunities or creating a more compelling customer experience, the opportunities to drive and derive business value with Big Data are everywhere. In fact, organizations do not fail with Big Data because of a lack of business opportunities; they fail because they have too many. It’s a focus and prioritization problem.

Myth #2: Investing in Big Data capabilities is the first step to surfacing valuable insights from your data.

Reality #2: Begin with a business end in mind.

In order to address the focus and prioritization problem, start your Big Data journey by focusing on one of the organization’s key business initiative. Focus on something that is important to the business; something the business is trying to accomplish over the next 9 to 12 months. It might be something like customer acquisition or quality of care or predictive maintenance or network capacity optimization. But pick something and get going!

Myth #3: Big Data is only for Big Business.

Reality #3: All types of businesses can and should embrace the economics of Big Data.

Using Big Data technologies (scale out hardware architectures built on commodity processors, open source software tools and platforms), it is 20x to 50x cheaper (and maybe even more today) to store, manage and analyze data versus traditional data technologies. The “economics of Big Data” enables four (4) key capabilities that can be applied to your business initiatives to deliver business value. These 4 Big Data economic value drivers are:

  1. Access to the organization’s complete history of detailed transactional and operational data at the level of the individual – whether that’s an individual human, machine or device.
  2. Access to the growing wealth of unstructured data, whether internal to the organization (consumer comments, sales teams notes, email conversations, product spec’s, work orders, clinical studies) or external to the organization (traffic, weather, home values, building permits, patent filings, research studies).
  3. “Right-time” analysis to identify in a more timely manner, a data-driven event (customer engagement, device reading, machine log) that provides an opportunity for action; to “catch the customer in the act” of engaging with your organization.
  4. Apply predictive analytics (data mining, machine learning, cognitive computing) across the wealth of internal and external data to uncover areas of “unusualness” (problems or opportunities) at the level of the individual.

Myth #4: Focus on the data to identify opportunities for valuable analytics.

Reality #4: Focus on the decisions that stakeholders need to make in support of the targeted business initiative.

Leverage envisioning and facilitation techniques to brainstorm with key business stakeholders to identify the decisions that these stakeholders need to make in support of the targeted business initiative. The decisions are critical, as that is the linkage point between the business and the data science.

Myth #5: Data Science is an enigma of technical complexity.

Reality #5: Data science is about identifying those variables and metrics that might be better predictors of performance.

Understand what Data Science can do for you, but what is data science? Data science is about identifying those variables and metrics that might be better predictors of performance. Might. Might is a valuable word because it gives the business users and the data scientists the license to explore any and all ideas to uncover those variables and metrics that might be better predictors of performance. Might. Remember, “If you do not have enough “might” ideas, you’ll never have any breakthrough ideas.”

Myth #6: Data science is only for data scientists.

Reality #6: Business users need to think like data scientists.

Who are the best sources of those variables and metrics that might be better predictors of performance? The people who live the jobs day in and day out – the business users (e.g., store managers, physicians, nurses, teachers, operators, technicians, mechanics, marketing manager, sales agent, call service rep). In order to get the business users to start thinking more broadly and creatively about those potential variables and metrics, it’s necessary to take the business users through a series of “thinking like a data scientist” exercises that include:

  • Transitioning from asking descriptive questions about what has happened, to asking predictive questions about what is likely to happen and prescriptive questions about what should one do.
  • Understanding the power of scores to aggregate a wide variety of variables and metrics around a common domain to create a score. Think FICO score and its ability to leverage 36 different variables and metrics to create a score that measures the likelihood of someone repaying a loan.
  • Understanding the user experience ramifications; that is, how will the analytic results and insights be delivered to the front-line employees, managers and customers in a manner that they can understand and act?

Based upon the feedback from some of the audience, this was a pretty good and straight forward list. Yes, we can demystify this Big Data discussion in order to help organizations understand where and how to start their Big Data journeys today.

The post Demystifying Big Data appeared first on InFocus.

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.