top of page

Competing on Analytics - Book Review

1 August 2008
Dr. Moria Levy
book cover

The book "Competing on Analytics," co-authored by Prof. Thomas Davenport and Ms. Jeanne Harris, follows Davenport's renowned work, "Working Knowledge," which has gained prominence in knowledge management. While this 2007 publication from Harvard Business School may have reached a different level of fame, it stands out for its unique analysis of the analytical subject. Providing a comprehensive overview of the business intelligence (BI) landscape, the book explores companies that have successfully become leaders in the field and outlines the gradual progression from one stage to another to advance a company. With numerous examples, it is highly recommended for any organization seeking detailed insights into the practices of sector leaders and competitors, along with guidance on managing decision-support information. The book's clear motto emphasizes that organizations making information-based decisions are more likely to achieve success.

The book covers the following topics:

  • What is analytical BI, and why does it offer a competitive advantage?

  • Intra-organizational analytical BI

  • External Analytical BI

  • Motivating an organization to develop analytical capabilities

  • Organization axis

  • People's Axis

  • Axis of technology

  • Future trends

Reading the book requires patience and openness: patience for the numerous examples and details and openness to understanding that the world of analytical BI extends beyond technology, encompassing various processes and human aspects. I strongly recommend reading the book as an excellent resource for organizations interested in advancing themselves. Much of the content is applicable for generalization and learning in parallel processes, such as organizational knowledge management. I hope you find it an enjoyable read.

What is analytical BI, and why does it offer a competitive advantage?

Analytical BI focuses on the extensive use of data, statistical and quantitative analysis, forecasting, and fact-based decision-making as a management tool. Positioned as a subset of Business Intelligence (BI), which encompasses tools and processes utilizing data to understand and analyze business performance, Analytical BI is distinct in its concentration on advanced analysis and presentation capabilities. Unlike BI, it excludes reporting capabilities and access to information, focusing on higher-order analytical functions.

These capabilities address questions such as:

  • Why do things happen? (statistical analysis)

  • What happens if current trends continue? (Forecasting)

  • What happens next? (Simulations)

  • What's the best thing that can happen? (Optimization)

While the authors use the term "analytics," I have chosen to use the term "BI analytical" in this translation.

The book does not aim to undermine the importance of intuition; instead, it acknowledges its relevance when data is unavailable and emphasizes its value when supported by extensive experience. However, in other cases, the book suggests that the efficacy of intuition is limited.

The application of analytical BI is not restricted to specific sectors or organizational departments. The book provides diverse examples, spanning sports decision-making, medical applications, use in public/government organizations, research applications, and more. Numerous instances of organizations achieving significant success by implementing analytical BI solutions are cited, showcasing how such initiatives have propelled business advancements.

The book also references studies highlighting the correlation between data-based decision-making and business success, emphasizing the high return on investment from various analytical projects. Additionally, there is recognition that analytical BI solutions are challenging to replicate due to their uniqueness, adaptability to specific organizations, scenario-specific construction, renewability, and process-intensive nature, which necessitates alignment with organizational culture. Significantly, the book clarifies that analytical BI is not a substitute for management but a complementary tool.

Intra-organizational analytical BI

Analytical BI proves instrumental in guiding decisions related to internal organizational processes, with a critical focus on identifying strategically significant procedures that can confer a competitive advantage to the organization.

Standard intra-organizational processes benefiting from analytical knowledge management encompass:

  1. Financial processes: Utilizing reports and balance sheets for regulatory compliance, as well as performance analysis, enterprise performance forecasting, cost management, profitability analysis, market and merger analysis, and compensation planning. Dashboards are effectively employed to present results to managers.

  2. Production, operations, and quality processes: Rooted in TQM and Six Sigma, these processes involve statistical analysis. Applications extend to configuration management, real-time operational analysis, and text analysis to identify product/service issues, such as analyzing website surfer movements in the Internet age.

  3. Research and development processes: Particularly crucial in pharmaceuticals, analytical BI supports innovation and adaptation in various sectors.

  4. HR processes: Emerging in large organizations with operational information systems, HR analytics aids in analyzing employee performance, including qualitative assessments. In sports, it assists in decisions related to recruiting.

Various types of analytical applications supporting internal organizational processes include:

  • Activity-Based Costing: Understanding cost allocation based on activity components.

  • Bayesian Inference: Numerical assessment of belief levels in assumptions before and after supporting facts emerge.

  • Biosimulation: Mathematical manipulation of biological parameters and the impact of chemical substances.

  • Constraint Analysis: Employing algorithms to find possible solutions within known constraints.

  • Future Value Analysis: Breaking down market capital into present and future value.

  • Monte Carlo simulation: Assessing chances of defined outputs or evaluating risks.

  • Multiple Regression Analysis: Statistical technique examining the effect of several independent parameters on a dependent parameter.

  • Neural Network Analysis: Applying learning rules suitable for brain-like systems with large branched networks.

  • Textual Analysis: Examining frequency, semantic relationships, and relative importance of words, terms, and documents in a given text.

  • Yield Analysis: Using statistics such as averages, medians, and standard deviations for quantitative and qualitative comparisons.

It's crucial not to be intimidated by the list. While in-depth understanding may not be universal, leveraging these analytical tools through appropriate personnel allows organizations to benefit from the results of analytical analysis, akin to how most people use TV without manufacturing or fixing it.

External Analytical BI

Analytical BI has undoubtedly made significant strides when applied to extra-organizational processes. In these processes, the correlation between analysis and company performance is evident, particularly in dealings with:


This encompasses processes related to acquiring new customers and, notably, retaining existing ones. Other uses include profitability analysis, product pricing, sales channel analysis, and brand and promotion management.

Types of analytical applications supporting marketing processes include:

  • CHAID: A statistical technique for creating customer clusters based on defined parameters.

  • Conjoint Analysis: Evaluating customer preferences and strengths.

  • Lifetime Value Analysis: Utilizing analytical models to assess the profitability of individual customers.

  • Market Experiments: Analyzing various parameters based on experiments to gauge customer reactions to new offers.

  • Multiple Regression Analysis: A commonly used statistical technique for examining the effect of numerous independent parameters on a dependent parameter.

  • Price Optimization: Analyzing pricing flexibility and customer responses according to pricing variations.

  • Time Series Experiments: Analyzing experimental conditions that led to changes in tested parameters.


This involves providing crucial customer data to suppliers, managing the supply chain, and analyzing and improving maintenance processes. Analytical applications supporting supply chain processes include:

  • Capacity Planning: Planning the capability or capacity of supply chain components.

  • Demand Supply Matching: Analyzing production and storage to meet demand without accumulating excess inventory.

  • Location Analysis: Optimizing decisions on warehouse, manufacturing plant, and distribution center locations.

  • Market Experiments: Analyzing various parameters through experiments to gauge customer reactions to new offers.

  • Modeling: Creating models for analyzing simulations and potential scenarios in a supply chain.

  • Routing: Planning distribution routes.

  • Scheduling: Planning timings for resource flows.

Motivating an organization to develop analytical capabilities

An organization equipped with analytical BI capabilities and effective leadership gains a competitive advantage by consistently employing advanced analytical tools and strategies beyond those of its competitors. The authors identified several organizations, including Google, Amazon, Netflix, Capital One, and Sara Lee Bakery. These organizations, collectively defined by four key characteristics (elaborated in the book), share a commitment to leveraging analytical BI to enhance various strategic capabilities, an organizational management approach centered on BI, a strong commitment from senior management to product utilization, and a strategic investment in analytical BI to gain a competitive edge.

Contrastingly, other organizations involved in analytical BI often exhibit no more than one or two characteristics from the list above, even if they are successful in their analytical BI endeavors. The authors delineate five stages that categorize organizations engaged in this field. Subsequent chapters, organized around three axes (organizational, human, and technological), elaborate on the characteristics of companies at each stage and outline steps for progressing to the next level. The five defined stages are:

Step 1: Analytically Weak: Characterized by questions like "What's happening in our organization?" and a desire for accurate information to enhance performance, yet lacking measurement of the value of the activity.

Step 2: Local Analytical BI: Characterized by local opportunities and questions such as "What can be improved in business?" and "How can the business be better understood?" This stage involves BI activities to enhance specific functions, measuring time returns in individual applications.

Step 3: Analytical Aspirations: Marked by the initiation of integrative activities related to data and analytical BI, with questions like "What is happening now, and what can we learn from current trends?" Analytical BI usage spans various fields, with measurements against future performance improvement and market value.

Step 4: Analytical Companies: Distinguished by an overarching organizational perspective, capable of utilizing BI to identify advantages, with the knowledge of how to progress further. Questions at this stage focus on "How do we use BI to create innovation and competitive value?" In building lateral capabilities to suit diverse needs, analytics are a significant driver for performance and value.

Step 5: Analytical BI Companies with Leadership (Competitive Advantage): Featuring comprehensive lateral activity, substantial results, and stable advantages. Questions at this level shift towards "Where to?" and "How do you maintain leadership?" Competitiveness is grounded in analytical BI, acting as the primary catalyst for performance and value.

Only some companies can reach Level 5, but the effort is worthwhile. Notably, the primary determinant of success is not solely technology; the organizational and human axes are equally influential.

Organization axis

The organizational roadmap for advancing through analytical BI stages comprises several phases:

Phase 1: Start-Up Stage

At this initial stage, the company expresses interest in analytical BI, but the necessary conditions for realization are lacking. Challenges include a lack of functionaries, a limited understanding of feasibility and significance, and insufficient commitment. The organization must decide where to commence analytical BI, set goals, and create local successes with executive commitment.

Phase 2: Local Activity Stage

This stage focuses on local activities that pave the way for broader commitment. It is crucial to begin small, maintain a limited framework, and resist the temptation to expand prematurely. Key sub-steps involve finding a sponsor and identifying a business problem benefiting from analytical BI, executing a small, local project, documenting and communicating benefits with partners and decision-makers, and progressively building a chain of activities and successes.

Stage 3: Managerial Sponsorship

This stage commences when analytical activity gains managerial sponsorship. Managers become advocates for a data-driven culture, and while still primarily independent BI processes, they start receiving resources and a comprehensive organizational plan. Sophisticated implementations and deeper analytics characterize this stage, with the duration varying from several months to two years.

Phase 4: Implementation of Plans

Fewer companies reach this stage, marked by implementing plans formulated in Phase 3. Significant progress is made in sponsorship, culture, skills, strategic insights, content, and technology. Capacity building becomes an organizational priority, and management gains confidence and experience, leading to increased mass activities and the integration of analytical BI into organizational work processes.

Stage 5: Competitive Advantage

This rare stage sees analytical BI becoming a strategic tool, providing a competitive advantage for the organization. Measurement, processes, and data become obstacles for competitors attempting to catch up. The organization continuously advances the BI bar, maintaining a strategic lead.

People's Axis

While the association with analytical BI often revolves around technology, the primary driver of success lies in people. Key stakeholders include:

  1. Senior Management: The CEO or a senior management representative plays a critical role in success. Understanding the deliverables and the importance of data-driven decision-making is crucial for leveraging BI capabilities.

  2. BI Experts: These professionals, mathematicians, and BI functionaries contribute to building algorithms, optimizing data mining processes, and implementing analytical strategies. They play a decisive role in determining when analytical BI will make automated decisions or assist decision-makers.

  3. Mid-Level and Lower-Level Managers: These individuals become crucial partners in the group of analytical experts, collaborating on decision-making and contributing to the successful implementation of analytical BI.

  4. Human Resources: Building a winning team is the most challenging aspect of strategy implementation. Organizations emphasize the importance of human resources in transitioning between stages, emphasizing skill development, increased managerial commitment, and support for all organizational levels.

Axis of technology

Commencing with the premise that an organization leveraging analytical BI requires robust support from the information systems group, whether overseeing the entire process or acting as a service provider, they are responsible for acquiring data, technology, and supporting processes. The umbrella term for BI infrastructure architecture aims to integrate systems, applications, and governance processes, facilitating the flow of processed data to the necessary functionaries at the required time. The six technological components of the BI architecture encompass:

  1. Data Management: Ensuring that the correct data for BI exists and is managed, excluding unnecessary information.

  2. Tools and Processes: Facilitating the migration of data to the BI environment.

  3. Repositories: Storing and organizing data and accompanying properties.

  4. Analytical Applications and Software: Utilizing applications and software for analytical purposes.

  5. Display Tools: Utilizing applications and tools to make content accessible to knowledge workers and managers, aiding in visualization and processing.

  6. Operational Processes: Assisting in information security, error handling, archiving, and more.

To maximize the benefits of analytical BI, five critical issues must be addressed in the process:

  1. Data Relevance: Identifying the data required for analytical leadership and competitiveness.

  2. Data Source: Determining how to obtain the necessary data.

  3. Amount of Data: Assessing the quantity of data required for practical analysis.

  4. Data Quality: Ensuring the accuracy and value of the data.

  5. Data Governance: Establishing rules and processes governing data from creation to completion.

The authors acknowledge the challenges in data transfer, emphasizing that despite the existence of automated tools, substantial investment is still necessary in customizing manual processes. Technological tools for analytical analysis include datasheets (with reservations about their use), OLAP tools, statistical or quantitative algorithm tools, rule engines, data mining tools (texts), and simulation tools. Emerging technologies as of early 2007 include cataloging tests using statistical models, genetic algorithms, expert systems, audio and video mining, swarm intelligence, and data extraction (from geographical content and texts).

Future trends

The authors envision a future where more companies will opt for analytical BI to forge unique capabilities, aiming for leadership positions. They depict the analytical future world by highlighting three influential areas:

  1. Changes in the Influence of the Technological Factor:

    a. Widespread and extensive use of BI software.

    b. Increasing adoption of dedicated BI solutions, especially for large data warehouses (both hardware and software).

    c. Growth in automated decision-making processes.

    d. Rise of real-time BI capabilities.

    e. Increased utilization of notifications for timely insights.

    f. Advancements in interactive analytical BI featuring visual tools.

    g. Greater emphasis on predictive analytics over traditional reporting.

    h. Expanded use of text mining techniques.

  2. Changes in the Influence of the Human Factor:

    a. A surge in market analytical experts from the previously mentioned two groups.

    b. An increase in university courses guiding learners on the application of analytical BI.

    c. Greater reliance on outsourced or offshored BI, especially for specific functions.

    d. More intra-organizational training focused on data utilization.

    e. Organizations placing formal emphasis on managing data-driven decision-making processes.

  3. Changes in Strategic Impact:

    a. Exploration of ways to manage intangible assets, leveraging analytical BI tools.

    b. Implementation of analytical BI across various organizational domains with significant reliance on its capabilities.

Analytical companies with leadership will persist in pursuing innovation, consistently outperforming competitors. These companies will likely thrive financially and contribute to solving global challenges, leading us into the future.

bottom of page