Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness

Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness

by Alec Levenson

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Overview

Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness by Alec Levenson

More than ever, data drives decisions in organizations—and we have more data, and more ways to analyze it, than ever. Yet strategic initiatives continue to fail as often as they did when computers ran on punch cards. Economist and research scientist Alec Levenson says we need a new approach.

The problem, Levenson says, is that the business people who devise the strategies and the human resources people who get employees to implement them use completely different analytics. Business analytics can determine if operational priorities aren’t being achieved but can’t explain why. HR analytics reveal potentially helpful policy and process improvements but can’t identify which would have the greatest strategic impact.

This book shows how to use an integrated approach to bring these two pieces together. Levenson presents a thorough and realistic treatment of the reasons for and challenges of taking an integrated approach. He provides details on the different parts of both enterprise and human capital analytics that have to be conducted for integration to be successful and includes specific questions to ask, along with examples of applying integrated analytics to address particular organizational challenges.

Effective analytics is a team sport. Levenson’s approach allows you to get the deepest insights by bringing people together from both the business and HR perspectives to assess what’s going on and determine the right solution.

Product Details

ISBN-13: 9781626560550
Publisher: Berrett-Koehler Publishers
Publication date: 11/02/2015
Pages: 256
Sales rank: 1,160,125
Product dimensions: 5.50(w) x 8.20(h) x 0.80(d)

About the Author

Alec Levenson is an economist and senior research scientist at the Center for Effective Organizations, Marshall School of Business, University of Southern California. He has trained business professionals from a large number of Fortune 500 companies in human capital analytics.

Read an Excerpt

Chapter 1
Of Elephants and Incomplete Analytics
Issues Addressed in This Chapter
• Most analytics conducted today by the business and by HR are incomplete and cannot solve strategy execution problems on their own

• You need a full causal model to diagnose the entire system and to understand what really drives behavior and performance

Key Questions
• What problems are you trying to solve with your analysis?

• How does the analysis you have chosen help to improve strategy execution?

You need to know what drives performance in your organization to get strategy execution right. The problem with organizational analytics today is that they tell an incomplete story. Enterprise analytics and human capital analytics are conducted along parallel and separate tracks. Both attempt to determine why performance happens, yet each on its own can tell only part of the story. Without the complete story, we don’t really know the best ways to improve strategy execution and organizational effectiveness.

Enterprise analytics can tell us if we’re achieving the strategy and details about the operational measures that contribute to strategy execution. A typical analysis addresses questions like these:

• What types of customers can best help increase our share in existing markets?

• What new markets can we succeed in?

• What organizational capabilities do we need for strategic success?

The enterprise metrics used to answer these questions include market share, sales, and margins, among others, and extend to operational and technical measures that describe business processes, such as productivity, innovation, quality, manufacturing uptime, time-to-market, customer service, and others.

On the human capital side, a typical analysis tries to figure out the sources of organizational ineffectiveness, focusing almost exclusively on how work is done and whether people work well together. A typical analysis addresses questions like these:

• Who are our best leaders, and what role do they really play?

• Is a group or team performing well or working at cross purposes?

• What is the right mix of compensation versus non-monetary rewards in motivating performance?

• How can we improve our HR practices to be more effective?

The human capital metrics used to answer these questions include leadership and frontline worker competencies, employee attitudes, and measures of human resource (HR) program efficiency and effectiveness.

Enterprise analytics tells us whether the strategy is executed. But it can’t tell us which jobs and individual-level behaviors most directly lead to improved strategy execution. The human capital perspective is essential, but only on rare occasions are people from the human capital side invited to contribute when senior leaders conduct business analytics. Instead, they usually are told what happened only after the key decisions were made.

Business analytics and HR analytics as commonly practiced are examples of incomplete analysis: they do not specify and test a full causal model. Rather than looking at the entire system that defines and drives organizational performance, they take shortcuts and focus on too few elements. To illustrate the problem with the analysis of large complex systems based on only partial data, consider the scene in figure 3, which is from a centuries-old fable.

The scene is a group of blind monks who are each touching different parts of an elephant. Modern versions of the story star blind men, and sometimes only three of them, though the specific stars of the story are not important. What matters is the analysis performed by each person, which occurs in isolation and without consideration of the data gathered by the other people.

WHAT’S THE RIGHT LEVEL OF CUSTOMER SERVICE? A SYSTEMS DIAGNOSIS APPROACH.

Business-to-consumer industries. Determining the right amount of customer service is a challenge for all organizations. If you don’t provide enough, key customers walk out the door. If you provide too much, profit margins get whittled down to nothing and you don’t make any money.

Customer service in business-to-consumer industries is driven by product quality, ease of use, and responsiveness of customer service representatives (CSRs). When customer service scores fall, the directive to operational leaders may simply be “Go figure out how to get customer service back to where it was before.” Suppose product development previously decided to save costs by reducing spending on quality assurance processes. They may have assessed that existing processes are redundant, slowing time to market and reducing sales. If the lower spending on quality assurance is misguided, the end result would be lower product quality and unhappy customers. If the customer service scores do not measure product quality, fingers could be pointed at the CSRs, leading to the incorrect conclusion that they had become less motivated to provide high-quality customer service.

For another case, consider the link between CSR compensation and customer service. Suppose the customer service site leader is held accountable for metrics such as time to resolve customer complaints and efficiency of the operations (call volume, wait times, and similar issues). If she does not have profit and loss (P&L) responsibility for her operations she will push for higher compensation for her CSRs as a way to attract and retain higher skilled employees. Similarly, HR might advocate for greater pay to reduce attrition and improve retention of the longer-tenured and more experienced CSRs. However, evaluating whether better pay is worth the investment requires a complete Strategic Analytics diagnostic that addresses the relationship between customer retention and sales and profitability.

Business-to-business industries. In business-to-business industries, customer service involves striking the right balance between cutting prices to make the sale and maximizing profits. A Strategic Analytics diagnostic looks at the complete set of interactions between the customer and organization, along with the role played by each employee and function. For example, salespeople may be given discretion to set specific contract terms, but they need timely and accurate information on how the terms impact enterprise profitability through metrics such as capacity utilization. And they need to be trained and evaluated on overall profitability, not just sales.

The person touching the tail concludes that an elephant has features like a rope. The person touching the leg thinks the elephant’s shape is more like a tree. The person touching the tusk has no idea what the elephant’s skin feels like. And so on. All their conclusions seem reasonable, given the information at each person’s disposal, but all fail to describe the entire animal properly.

Enterprise analytics today are like the person touching only the head. They are out in front, focusing on a part of the animal that is pointed forward. Yet trying to dictate the direction and pace of the animal by focusing only the head can be a lost cause. If the animal’s legs are tethered to a post, it cannot move, no matter what you do. You may point the animal in the right direction, but you will never get it to move forward.

Human capital analytics today are like the person touching only the hind legs. They move in unison with other parts of the body, but they contribute only one part to the animal’s full range of motion and have no effect on direction. You can’t properly diagnose problems with overall direction and speed by ignoring the rest of the animal and analyzing just the rear legs.

What’s missing from both types of analysis is the rest of the body: the front legs that work in unison with the rear legs to propel the body forward and the torso that holds it all together. Excluding the trunk leaves out key information about how the animal maintains its health through eating, drinking, and bathing. Enterprise and human capital analytics, when conducted separately, fall short of identifying and testing a complete causal model. The most accurate insights require a combined analysis that diagnoses the performance drivers for the entire system, not just one part of it.

Data mining is not causal analysis. One of the biggest mistakes I see frequently is simple data analysis that focuses on only one or two pieces of information. The problem is that simple analysis without a causal model can lead to the wrong conclusions: it is just data mining and not science. A typical example is when consultants look at a group of companies and conclude that “the use of HR practice XYZ is more common at high-performing companies,” where XYZ could be the latest leadership development program, incentive pay philosophy, employee engagement strategy, and so on. The implication is that if your organization adopts the same practice, your business results will improve. The implicit causal model is shown in figure 4.

The problem is that HR practices by themselves do not create business results. They are one contribution in a larger system that enables the business results. HR practices can improve strategy execution only when they are aligned with other parts of the system.

Table of Contents

List of Figures and Tables
Preface
Introduction: Integrating Enterprise and Human Capital Analytics
Part I Why Do Strategic Analytics
1. Of Elephants and Incomplete Analysis
2. Beware the ROI Bogeyman and Other Monsters Lurking under the Bed
Part II How to Do Strategic Analytics
3. Put the Horse in Front of the Cart—Where to Focus the Analysis
4. Step 1—Competitive Advantage Analytics
5. Step 2—Enterprise Analytics
6. Step 3—Human Capital Analytics
7. Putting It All Together
8. Application—Customer Retention and Profitable Growth
9. Application—Go-To Market Strategies and Effectiveness
Part III Diving Deeper: How to Make Current Practice Better
10. Critical Roles, Competencies, and Performance
11. Making Sense of Sensing Data
12. Evaluating Human Capital Development—Build versus Buy versus Redesign
Conclusion: Key Learning and Action Points
References
Appendix Strategic Analytics Diagnostic Interview Template
Acknowledgments
Index
About the Author

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