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September 8, 2018 - June 16, 2020
I’m convinced that complacency poses one of the most serious threats to any business. Companies so intent on staying the course that they don’t hear the footsteps behind them pay a high price for misguided satisfaction.
advocated for what he called “constructive dissatisfaction.” Time and again, Casey restructured and reinvented UPS to counter a host of competitive threats.
In the process, we have realized the data we collect as a package flows through our network is often as valuable to the customer as the package itself.
show how analytics continues to transform technology from a supporting tool to a strategic advantage.
Extracting value from information is
Instead, it’s how aggressively you exploit these resources and how much you use them to create new or better approaches to doing business.
Hadoop, an open-source program for storing large amounts of data across distributed servers. Hadoop doesn’t do analytics, but it can do minimal processing of data, and it’s an inexpensive and flexible way to store big data.
Patil kept telling Tom that they want to be “on the bridge”—next to the CEO or some other senior executive, helping to guide the ship.
Facebook, a major employer of this new profession, referred to data scientists and developers as “hackers” and had the motto, “Move fast and break things.”
the 3.0 world, analytics no longer stand alone. They become integrated with production processes and systems—what
Supply chain optimization doesn’t happen in a separate analytics run; instead it is incorporated into a supply chain management system, so that the right number of products is always held in the warehouse.
data and analytics have become mainstream business resources.
Your company’s analytics experts will need some new skills. Instead of slowly and painstakingly identifying variables and hypothesizing models, machine learning analysts or data scientists need to assemble large volumes of data and monitor the outputs of machine learning for relevance and reasonability.
Achieving this level of granularity with traditional approaches to propensity modeling would require thousands of human analysts if it were possible at all.
First of all, so much change in such a short time means that organizations wanting to compete on analytics have to be very nimble.
They also require an understanding of the business, an ability to communicate effectively about data and analytics, and a talent for inspiring trust among decision-makers.
operational analytics means that data and analytics will be embedded into key business processes, there’s going to be a great need for change management skills.
At UPS, for example, the most expensive and time-consuming factor by far in the ORION project was change management—teaching about and getting drivers to accept the new way of routing.
But the organizational and human transitions were less successful. Forty-three percent mentioned “lack of organizational alignment” as an impediment to their big data initiatives: forty-one percent pointed specifically to middle management as the culprit; the same percentage faulted “business resistance or lack of understanding.”
Eighty-six percent say their companies have tried to create a data-driven culture, but only 37 percent say they’ve been successful at it.
The problem, we believe, is that most organizations lack strong leadership on these topics. Middle managers can’t be expected to jump on the analytics bandwagon if no one is setting the overall t...
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In chapter 1, we’ve attempted to lay out the general outlines of analytical competition and to provide a few examples in the worlds of business and sports. Chapter 2 describes the specific attributes of firms that compete on analytics and lays out a five-stage model of just how analytically oriented an organization is. Chapter 3 describes how analytics contribute to better business performance, and includes some data and analysis on that topic. Chapters 4 and 5 describe a number of applications of analytics in business; they are grouped into internally oriented applications and those primarily
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Embedding analytics within operational systems Visual analytics
Like it or not, some things in the world of analytics haven’t changed much. Issues like developing an analytical culture, the important role of leadership, and the critical need to focus your analytics on a pressing business problem, are all pretty similar to what they were in 2007. We’ve left all those lessons pretty constant in this edition, other than trying to find new examples of how important they are. They were the hardest things to pull off successfully a decade ago, and they’re still the hardest today.
fact-based management to drive decisions and actions
The analytics may be input for human decisions or may drive fully automated decisions.
Analytical competitor: An organization that uses analytics extensively and systematically to outthink and outexecute the competition.
Analytics: The extensive use of data, statistical and quantitative analysis, explanatory and predictive
Descriptive analytics (aka business intelligence [BI] or performance reporting) provides access to historical and current data. It provides the ability to alert, explore, and report using both internal and external data from a variety of sources.
Predictive analytics uses quantitative techniques (e.g., propensity, segmentation, network analysis and econometric
Potential competitive advantage increases with more sophisticated analytics In principle, analytics could be performed using paper, pencil, and perhaps a slide rule, but any sane person using analytics today would employ a computer and software.
it’s the human and organizational aspects of analytical competition that are truly differentiating.
high-performance business processes are among the last remaining points of differentiation.
What’s left as a basis for competition is to execute your business with maximum efficiency and effectiveness, and to make the smartest business decisions possible.
Good decisions usually have systematically assembled data and analysis behind them.
Analytical competitors, then, are organizations that have selected one or a few distinctive capabilities on which to base their strategies, and then have applied extensive data, statistical and quantitative analysis, and fact-based decision making to support the selected capabilities.
Whatever the capabilities emphasized in a strategy, analytics can propel them to a higher level.
“information-based strategy.”
GE is differentiating its industrial services processes by using sensor data to identify problems and maintenance needs before they cause unscheduled downtime.
If your business generates lots of transaction data—such as in financial services, travel and transportation, or gaming—competing on analytics is a natural strategy
analytical competition by organizations. At the same time that executives have been looking for new sources
As we describe in the introduction, about a decade ago, Silicon Valley firms such as Google and LinkedIn developed new ways to process and make sense of all the data they capture. Once they made those tools publically available, big data and machine learning began to infiltrate analytical enterprises in other industries, too.
Gartner’s 2016 survey of nearly three thousand chief information officers from eighty-four countries found that business intelligence and data analytics are the number-one technology priority for IT organizations for the fifth consecutive year.
analytics was the technology most sought to take advantage of the ERP data.
Our focus in this book, however, is on companies that have elevated data management, statistical and quantitative analysis, predictive modeling, and fact-based decision making to a high art.
Its M&A Pro tool both speeds deals and eliminates what the company views as the greatest source of problems in M&A work—human error.15
descriptive, predictive, and prescriptive analytics
The bigger issue will be how organizations control their data and analysis, and ensure that individual users make decisions on correct analyses and assumptions.
In order for quantitative decisions to be implemented effectively, analysis will have to be a broad capability of employees, rather than the province of a few “rocket scientists” with quantitative expertise.
For now, we simply want to emphasize that although analytics seem to be dispassionate and computer based, the most important factors leading to success involve passionate people.
