Analytics at Work: Smarter Decisions, Better Results

by Thomas H. Davenport, Jeanne G. Harris, Robert Morison

Most companies have massive amounts of data at their disposal, yet fail to utilize it in any meaningful way. But a powerful new business tool - analytics - is enabling many firms to aggressively leverage their data in key business decisions and processes, with impressive results.

In their previous book, Competing on Analytics , Thomas Davenport and Jeanne Harris showed how pioneering firms were building their entire strategies around their analytical capabilities. Rather than "going with the gut" when pricing products, maintaining inventory, or hiring talent, managers in these firms use data, analysis, and systematic reasoning to make decisions that improve efficiency, risk-management, and profits.

Now, in Analytics at Work , Davenport, Harris, and coauthor Robert Morison reveal how any manager can effectively deploy analytics in day-to-day operations—one business decision at a time. They show how many types of analytical tools, from statistical analysis to qualitative measures like systematic behavior coding, can improve decisions about everything from what new product offering might interest customers to whether marketing dollars are being most effectively deployed.

Based on all-new research and illustrated with examples from companies including Humana, Best Buy, Progressive Insurance, and Hotels.com, this implementation-focused guide outlines the five-step DELTA model for deploying and succeeding with analytical initiatives. You'll learn how

· Use data more effectively and glean valuable analytical insights
· Manage and coordinate data, people, and technology at an enterprise level
· Understand and support what analytical leaders do
· Evaluate and choose realistic targets for analytical activity
· Recruit, hire, and manage analysts

Combining the science of quantitative analysis with the art of sound reasoning, Analytics at Work provides a road map and tools for unleashing the potential buried in your company's data.

Genres: Business, Nonfiction, Research, Technology, Mathematics, Management, Leadership

240 pages, Hardcover

First published January 1, 2010

About the author

Tom Davenport holds the President's Chair in Information Technology and Management at Babson College. His books and articles on business process reengineering, knowledge management, attention management, knowledge worker productivity, and analytical competition helped to establish each of those business ideas. Over many years he's authored or co-authored nine books for Harvard Business Press, most recently Competing on Analytics: The New Science of Winning (2007) and Analytics at Work: Smarter Decisions, Better Results (2010). His byline has also appeared for publications such as Sloan Management Review, California Management Review, Financial Times, Information Week, CIO, and many others.

Davenport has an extensive background in research and has led research centers at Ernst & Young, McKinsey & Company, CSC Index, and the Accenture Institute of Strategic Change. Davenport holds a B.A. in sociology from Trinity University and M.A. and Ph.D. in sociology from Harvard University. For more from Tom Davenport, visit his website and follow his regular HBR blog.

Reviews

[Aaron Bolin · Rating 4 out of 5]

Analytics at Work is billed as a how to guide for managers to "effectively deploy analytics in their day-to-day" operations (from the inside cover). Though I enjoyed the book, I don't believe that a reasonable person could say that it lives up to the promise. Instead, Davenport and his co-authors provide a very general framework that lacks the advertised day-to-day details required for deployment.

I found the book to be interesting and quite useful from a "oh, I hadn't thought of that..." perspective. However, I'm not sure that there is much original material here beyond the general framework -- most of which was presented in an earlier work by the same authors. Though I wouldn't recommend this book for serious analytic how-to, it would be a good read for someone seeking a general overview of the topic in a reasonably non-technical format.

For me, the measure of a book's contribution to my understanding of a topic is the number of marginal notes I make as I read. In this particular volume, I didn't make many marginal notes. I'm still very much of fan of Thomas Davenport; the quality of his thought on the topic of business analytics is top notch. Analytics at Work is still worth a quick read, especially if this is your first exposure to Davenport's framework for business analytics.

[Foad Ansari · Rating 2 out of 5]

تا وسط های کتاب مباحث خوبی مطرح میشه ولی از صفحه 90 به بعد هذیان نویسنده و پراکنده گویی شروع میشه و خواندنش خسته کننده ست

[Kassitti Balomenos · Rating 5 out of 5]

Gave me what I am looking for in helping to set the organization’s strategy on how to implement more data-driven decisions and embedding analytics into the workflow processes and business culture.

I need to go thru the book again and create an outline/roadmap based on the ideas to present to leadership for buy-in on continuous investment into analytics tools and personnel.

[Mehwish · Rating 1 out of 5]

It is not what I was looking for very basic talking about obvious things (n)

[عباد ديرانية · Rating 2 out of 5]

قرأتُ نصف الكتاب تقريبًا في مادة جامعية عن "التحليل المهني" ("بزنس أناليتك")، وهو أقرب إلى نسخة محدّثة من كتاب سابق ومشهور نسبيًا لنفس المؤلّفين بعنوان "Competing on Analytics" (التنافس على البيانات). شخصيًا، وجدتُ الكتاب الأقدم أفضل بكثير وأكثر تميّزًا، وأما هذا فهو أقرب إلى إصدار تجاري، فالمحتوى قليل والفكرة الرئيسية واحدة وبسيطة، وهي ما يُسمّيه المؤلّف "منهج دلتا" (DELTA Framework)، وهو منهج في إدارة البيانات يُقيِّم أداء كل شركة في تحليل اللبيانات من خمسة جوانب: 1. نوعية البيانات، 2. توظيفها في خدمة الشركة، 3. تأييد مديري الشركة، 4. تقنية تخزين البيانات وتحليلها، 5. منهج تحليل البيانات. قد لا تبدو هذه الأسس مفهومة كثيرًا من رؤوس الأقلام هذه، لكنها بسيطة جدًا ويسهل اختزالها في مقال أو فيديو قصير، لكن المؤلف يسهب في شرح كل منها بفصل متكامل ليملئ كتابه بالمحتوى، وقد شعرتُ بأن أضيّع وقتي وأنا أقرأ هذه الفصول المُطوَّلة والتجارية مقارنةً بمقالات مختصرة وبسيطة للمؤلّف نفسه تصل منها الفكرة بسرعة أكبر بكثير.

[Tom Kloiber · Rating 4 out of 5]

DELTA model is helpful:

D - must have well structured data
E - must have an enterprise-wide approach
L - must have leadership supporting analytics initiative
T - identify key target projects
A - hire and train strong analysts

creating an analytics culture is key to success; target some basic projects first to gain early momentum and support; don't take on too much too soon

[Brian D Barker · Rating 5 out of 5]

Analytics at works.

Provides great insights from a students perspective. There is a vast amount of business acumen shared with the reasoning for a move to data analytics. The DELTA model is easy to understand, and is sound reasoning for leadership in any organization.

[Amanda Sagastume · Rating 5 out of 5]

This book really helped “label” my organization and gave me practical ways of getting to the next level of using data. It is a great resource that I’m sure to go back to from time to time.

[Mike · Rating 2 out of 5]

Co-author Jeanne Harris spoke at the 2010 JMP Discovery Conference which I attended and received this book for free. This is not the type of book I would typically choose to read. Here are a couple interesting points I noted:
a) p. 103: "Analysts want to feel supported and valued by their organizations but they also want autonomy at work - the freedom and flexibility to decide how their jobs are done. Managers should provide goals and resources, and then give analytical people freedom to organize their own work. Autonomy is not abandonment, however. Managers (and customers for that matter) need to recognize analysts' work and make their contribution visible to senior management.
b) p.152: "American Airlines uses analytics to optimize its route network and crew schedules. Without analytical tools, managing a complex hub-and-spoke network with over 250 destinations, twelve aircraft types, and 3400 daily flights would be nearly impossible. Nevertheless, it might be argued that American's optimized complexity works against it. Neither it nor other major U.S. airlines with similar complexity levels have been profitable for years.
A much less complex airline model is offered by Southwest Airlines, which has only one aircraft type and not airport hubs. Southwest also uses analytics for seat pricing and operations, but its model is much simpler to optimize. Most important, Southwest has beee profitable for thirty-six consecutive years, and at several times over the recent past its market values has been worth more than the combined market value of all other U.S. carriers. This sobering comparison suggests that American and the other more complex carriers need to simplify their own business models."
c) p. 180: "Your analytical decisions won't always be perfect. In most cases gathering and analyzing data significantly increases the likelihood that your answer will be right, or at least better than a guess. Sometimes your analytical decision will be wrong or suboptimal. Indeed, one of the biggest hurdles organizations face is learning not to keep the making the same bet when the model was wrong last time. Don't lose faith in data and analytics. You're better off overall making analytical decisions, even if sometimes you end up on the wrong side of a statistical distribution of outcomes."

[JP]

The authors build on their previous work, which I was glad to see because the analytics space is evolving quickly. A few elements I especially liked about this book include a framework for what analytics can answer, a model for success, and a tie in to employee engagement. In their framework for the questions analytics can answer, they distinguish between information and insight, and then show how each can be applied to past, present and future. The DELTA model outlines key success factors, such as enterprise orientation, leadership and the analysts themselves. Most of the body of the book is an expansion on these factors. Books in this genre that build on a previous work to often repeat the same case studies featuring the same companies. I noticed a return to some of the original firms in this case as well, but there were just enough new stories to convince that the authors work from a growing base of experience. Overall, this book gives practical and insightful perspective on applying analytics in business.

[Cliff Chew · Rating 4 out of 5]

This book is quite easy to read, although it's focus isn't on statistical models and analyses. However, it does cover very interesting issues that firms and analysts face when they are approaching analytics in their workplaces. We hardly live and work in silos, so many of the issues mentioned in this book are pretty interesting perceptions to take back. I do even see some of the situations explained in the book unfolding in some of the organizations that I have dealings with. To re-emphasize, you will not learn how to do customer segmentation or market-basket analysis with this book, but if you have the bandwidth, I suggest reading this book to learn about how to approach management with your analytics.

[Willis · Rating 4 out of 5]

I've found that how much I like a book can depend a lot on your current mood, mindset and situation. This book came at a perfect time for me so I ended up rating it higher than I would have otherwise. If you're looking for specifics around analytical tools, don't even bother with this book. If you are working on how to create an analytics organization or how to create more of a culture for analytics, then this is the book for you. This coincided with some work that I'm currently doing around organizational design and it was chock full of good advice and things to think about - so it was extremely valuable for me.

[Ken · Rating 3 out of 5]

I had very high hopes for this book. What it wasn't was a detailed list of analytical tools that you can use and implement in your job. It was more of identifying a process to create analytical tools. It did more of a good summary review of the different elements and parts to the process and what to look for and support. It wasn't bad, just not what I thought it would be.

[Harlan · Rating 4 out of 5]

I'm not typically one to recommend business books, but Davenport has a lot of wisdom about how organizations can structure themselves and their processes to make the best use of analytical employees.

[Jonathan Phares · Rating 5 out of 5]

Great insights on analytics that couldn't have been better timed for me. DELTA framework has been very helpful for me and it's helpful to see a good example path for moving toward a more analytical culture.

[Bill Seliger · Rating 4 out of 5]

More detailed than "Competing on Analytics" with some specific action plans and requirements for developing or enhancing your organization's analytic expertise.

[WR · Rating 3 out of 5]

Work-related reading, to help me understand how analytics can be applied in the workplace to make better fact-based decisions.

[Ron · Rating 2 out of 5]

skip this one and read the first book - Competing on Analytics - and only read if you need an overview of what analytics is.

[Mitch · Rating 1 out of 5]

Insights could be summarized on a postcard,

[Paresh Kamat · Rating 4 out of 5]

Good read for reader who wish to know how to employ analytics in business

[Rohit Raj · Rating 4 out of 5]

A book that teaches, in a simple and straight forward way, how companies can leverage their data and analytical capabilities to stay ahead of their competitors.