About the Book
One of the secrets to using data, analytics, and
systems to drive business impacts is: "No matter how good
analytics are, analytical solutions only add value if they
can be consumed." While I have observed average analytics
that had hundred of millions of dollars of impacts on their
originations' P&L, I have also observed brilliant, world-
class analytics that have sat on a shelf never been used.
In today’s Big Data Crazed-world, I too many companies are
repeating the same mistakes I have seen made throughout my
career: Before launching an analytic, they don't consider
all the barriers that need to be dismantled in order to
make their analytics usable. |
For example, I was once asked to manage an analytical team for a large global bank. When I took over the team, I reviewed its recent work. I was surprised to find that, had over the past couple of years, the team developed over a dozen analytical solutions that no one was using. I initially assumed quality issues must have been involved— and was take aback when I learned they were excellent analytics, many exceptionally ingenious.
I soon realized, though, that the team had not considered how these analytical solutions were going to be consumed. Those very smart Ph.D.’s were focused on developing elegant, powerful analytics; they failed to consider how to develop the analytical solutions in order to ensure that they could be implemented and used to drive business impacts.
Many barriers exist to making analytical solutions consumable. Failing to even identify those barriers can be very, very expensive. This book provides a concise guide to recognizing them and describes effective ways to surmount them.
Chapter 1: Analytics In The Real World|
Chapter 2: What’s "Analytics"?
Chapter 3: The Atom Method Of Developing Analytics Capabilities
Chapter 4: Access To Data
Chapter 5: Talent
Chapter 6: Operational Knowledge
Chapter 7: Maintenance
Chapter 8: Analytical Organizations Come In All Shapes And Sizes
Chapter 9: Trade-Offs Between Efficiency And Creativity
Chapter 10: Organize To Ensure Continuity
Chapter 11: The Great Debate—"Which Expertise?"
Chapter 12: Size Of The Analytical Team
Chapter 13: Analytics Resource Functions
Chapter 14: Deep Analytical Talent
Chapter 15: Analytical Maintenance Teams
Chapter 16: Research And Development Teams
Chapter 17: Step One—Defining The Problem
Chapter 18: Step Two—Identifying Touch Points
Chapter 19: Step Three—Understanding Your Touch Points
Chapter 20: Step Four—Selecting Data
Chapter 21: Step Five—Analyzing The Data
Chapter 22: The Future