Analytics Across the Enterprise: An interview with the authors
The following question and answer session conducted with the authors of Analytics Across the Enterprise, Dr. Brenda L. Dietrich, Dr. Emily C. Plachy and Maureen Fitzgerald Norton, covers the motivation, purpose and lessons learned from writing the book, which is now available from IBM Press and Pearson Education, Inc.
What motivated you to write the book? How did things get started?
When it comes to analytics, clients always ask: “Do you eat your own cooking?” Based on repetitive client inquiries such as this, the original idea for the book was born from the mind of Doug Dow (manager of two of the book’s three authors).
Another driving factor was that we didn’t know everything that was going on at IBM related to analytics. Writing the book would help us learn more (and there’s still much more to learn).
Also, we wanted to support all the work being done at universities implementing analytic programs based on the skills gap in analytics talent.
Who is the audience for the book?
The three audiences for this book are:
- Current business leaders across industries
- Students and faculty
- IBM itself
Can you provide a few examples of how IBM’s application of analytics in-house has been successful?
IBM developed a big data analytics solution that detects and prioritizes quality problems weeks or even months sooner than traditional statistical methods. The solution, named the Quality Early Warning System (QEWS), uses a new analytic technique developed by a statistics researcher at IBM known as cumulative sum. This solution is now deployed upstream at suppliers and applied to IBM’s own operations, as well as to many products operating in the field.
To clarify, QEWS:
- Ingests attribute and parametric data from thousands and thousands of point sources from across the supply chain
- Identifies quality trends well before they are detectable using traditional methods
- Prioritizes problems with very few false alarms
- Creates alerts that enable IBM and its suppliers to proactively detect and manage quality
Also of note, QEWS was recently incorporated into IBM Predictive Maintenance and Quality 2.0.
Another example of successful in-house analytics implementation is IBM’s Coverage Optimization with Profitability (COP) solution. COP is a “recommendation” engine that analyzes optimal sales coverage by matching the right resources to the right accounts. COP started with a request from senior executives who wanted to understand the sales, general and administrative (SG&A) expense at a client level in order to make coverage recommendations.
Big data is used to create a view of the revenue, gross margin and profitability of the IBM sales team at an account level. The data comes together in the research cloud, using IBM SPSS and Cognos for analysis. With COP, sales managers were able to understand the data, make it consumable and adjust their focus and resources. From 2011 to 2013, COP produced millions in revenue growth, while pilot teams aligned with the recommendations from COP saw a 90 percent increase in revenue and 70 percent increase in productivity.
What advice would you give to customers for success in using analytics?
Deliver results interactively: don’t define a multi-year project without any checkpoints, prototypes or deliverables for several years. You’ll get a faster time-to-value if you do an early prototype and show it to stakeholders. This is key for actions and decisions that generate value.
Don’t give up on driving an analytics culture—you’ve got the facts behind you and, quite simply, analytics works. Several studies have highlighted the value of analytics, and companies that use predictive analytics are outperforming those that do not by a factor of five.
In a 2012 joint survey by the IBM Institute of Business Value and the Said Business School at the University of Oxford of more than 1,000 professionals around the world, 63 percent of respondents reported that the use of information (including big data and analytics) is creating a competitive advantage for their organizations.
The bottom line is that analytics helps the bottom line.
Your competition will not be waiting to take advantage of the new insights from big data—will you?
Can you provide a macro view on IBM’s analytics strategy over the years?
IBM has been using what we now call analytics in manufacturing and product design since the late 1950s and in supply chain operations since the 1980s. A pivotal meeting took place in 2004 between IBM’s Brenda Dietrich and Linda Sanford, then IBM VP of enterprise transformation. They realized you didn’t have to limit analytics to physical processes like manufacturing and development. “Soft” processes such as human resources, sales and services could also benefit from analytics, and IBM’s enterprise-wide transformation journey to use analytics was launched.
Now the use of analytics has spread from engineering-based processes (such as product design) through logistics processes (such as supply chain operations) to human-centric processes (such as sales and workforce management). Seeing the cultural shift in the receptiveness to the use of analytics has been amazing.
IBM has been at this a long time; we’ve learned lessons along the way so our clients can learn from us. IBM has an advantage and a great champion in Ginni Rometty. She is driving us to do more with analytics. As she’s said, “Analytics is the silver thread that weaves into everything we do in the future and data is becoming a new natural resource of the current time.” If you don’t have that support from the top, you won’t have the kind of momentum that IBM has.
- Learn about IBM Big Data & Analytics and
- Read the latest Smarter Planet blog on Analytics Across the Enterprise
- Watch this live video interview with the authors