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Analytics at Work - Book Review

1 August 2011
Dr. Moria Levy
book cover

"Analytics at Work: Smarter Decisions, Better Results" was penned in 2010 by Tom Davenport in collaboration with Haris and Morison. One might consider it a sequel to their joint book, "Competing on Analysis," or vice versa. The latter, which showcases companies excelling in business intelligence and analytics, was initially intended to follow this book.

The backstory of this book is intriguing. After delivering lectures on their initial book and receiving applause but minimal company implementations, the authors realized that the excellence standard they had set seemed unattainable to many. In response, they crafted a book aimed at helping organizations gradually elevate themselves, guiding each entity to a better position than their current state. The book delineates five stages and five components (DELTA) crucial for success as an analytical organization, offering guidance for organizations seeking improvement.

The book delves into the following topics:
  • The five steps

  • Data

  • Enterprise

  • Leadership

  • Targets

  • Analysts

  • Enterprise infrastructure

For those unfamiliar with the previous book, analytical involves using analyzed data to make crucial organizational decisions and enhance business performance. This doesn't imply every decision, but there are numerous instances where processed data and information can be instrumental.

In typical Davenport fashion, the book is highly accessible, conveying essential ideas clearly and straightforwardly, augmented by numerous explanations and examples. It offers valuable insights into knowledge management projects, especially in managerial aspects. Notably, Tom Davenport, a figurehead in knowledge management, has brought his extensive experience to bear in this book.

Similar to its predecessor, this book enlisted the support of several software companies, notably SAS.

Wishing you an enjoyable read.

The five steps

Any organization can attain five levels of analytical maturity:

  1. Preliminary, limited analytical ability.

  2. On-premises analytical solutions.

  3. An organization that strives to be analytical.

  4. Analytical organization.

  5. An outstanding analytical organization with a competitive advantage thanks to this conduct.

The book "Competing on Analysis" offers a comprehensive description of each of these stages. In the book summary, the stages are elucidated in the chapter titled "Axis of the Organization."


The presence of data is a prerequisite for engaging in analytical analysis. Seven aspects must be considered when dealing with data, each requiring advancement at every stage:

  1. Structure: Data must be organized in an orderly manner, whether in a cube, matrix, or non-numeric structure. The chosen data structure impacts the level of analysis possible.

  2. Uniqueness: The uniqueness of the data held by the organization, compared to competitors, is a crucial consideration.

  3. Integration: Data integration from various sources, including operational and supporting systems, as well as external organizational sources, is essential. While settling for easily integrable operational data may be tempting, navigating the integration complexities is vital.

  4. Quality: Although data quality is vital for decision-making, a lower level than that accepted in operational systems may suffice, prioritizing progress over solving every obstacle.

  5. Access: Analytics require data separate from that used for day-to-day operations within the organization.

  6. Privacy: Recognize that the level of confidentiality required for analytical data is higher due to the aggregation and significance of summary data.

  7. Governance Policy: Define workflows for data management to ensure adherence to the aspects described above.

How to proceed in stages?

  • From a Phase 1 Organization to a Stage 2 Organization:

    - Develop expertise in critical data, including setting up a small local data warehouse.

  • From a Phase 2 Organization to a Stage 3 Organization:

    - Build organizational consensus around targeted analytical objectives and the required data.

    - Establish data warehouses and initiate analytics on them.

    - Encourage cross-organizational data sharing.

  • From a Phase 3 Organization to a Stage 4 Organization:

    - Construct an enterprise data warehouse and integrate external information.

    - Involve senior management in warehouse planning and management, initiating monitoring.

  • From a Stage 4 Organization to a Stage 5 Organization:

    - Educate senior management about the competitive potential inherent in data and its analysis.

    - Implement unique data management, introducing data management policies, and establishing a Business Intelligence Competency Center (BICC).

A vital data tip: Avoid striving for excellence or perfection in integrity, quality, or privacy, as it may hinder progress toward the crucial analytical analysis stage.


Adopting an organizational perspective in data analysis is imperative to evolve into a genuine analytical organization. Failing to do so may result in local optimizations across products, fields, or locations, which may undermine other organizational activities without providing actual benefits. Beyond these drawbacks, there are additional disadvantages from a non-organizational perspective, such as the challenge of allocating resources appropriately, given the myriad needs of the organization.

The authors of the book strongly recommend embracing an organizational vision from the inception of analytical activities, even if, in practice, local initiatives are kickstarted.

How to adopt an integrative organizational vision:

  1. For every new data addition, technology, or model, inquire about potential interests within the organization.

  2. Prioritize analytical resources centrally, guided by the organization's needs.

  3. Communicate in business language rather than computational terms, telling illustrative stories emphasizing clarity over factual accuracy.

How to progress through stages:

  • From a Phase 1 Organization to a Stage 2 Organization:

    - Seek small partnerships for functional cross-organizational analytical analysis.

    - Collaborate (via an information systems body) on selecting software tools and data management standards.

  • From a Phase 2 Organization to a Stage 3 Organization:

    - Opt for applications with high relevance across multiple business areas.

    - Manage information in a limited but organizationally oriented manner for future expansion.

    - Establish standards for privacy and permission management. Begin constructing the overall infrastructure with a scalable approach.

  • From a Phase 3 Organization to a Stage 4 Organization:

    - Develop an analytical strategy for a significant organizational unit or the entire organization.

    - Mitigate risks across the entire spectrum. Implement policies for technology and analytical infrastructure.

  • From a Phase 4 Organization to a Phase 5 Organization:

    - Prioritize and manage analytical assets at the enterprise level.

    - Implement a comprehensive organizational model and execute overall management.

    - Expand tools and infrastructures, implementing them extensively across the organization.

Top Tip: Organizational vision transcends mere data integrations. It involves integrating numerous managerial aspects, each with unique needs, agendas, and concerns about potential organizational impacts.


Without a doubt, while data is necessary, leadership stands out as the critical element defining an organization's analytical prowess.

In contrast to their earlier book, where the authors emphasized the CEO as the exclusive figure for leadership, they now broaden their perspective to include local leadership within specific sub-organizations and units. Undoubtedly, CEO leadership can usher in a level of excellence. Still, this leadership level is only sometimes available, and lower-level leaders can propel analytical decision-making within the organization.

Although not a commonly discussed concept in professional business intelligence literature, analytical leadership is undeniably crucial for organizations implementing analytical analysis. Examining analytical leaders reveals a spectrum of behaviors, including:

  1. Developing human skills, emphasizing proper conduct with people, not just computers and data.

  2. Advocating for increased data and analytics within the organization.

  3. Recruiting bright individuals and acknowledging their intelligence.

  4. Setting a personal example in analytical decision-making.

  5. Committing to results based on analytical activity, at least in part.

  6. Patiently teaching how analytical data can be effectively utilized, sometimes imparting analytical techniques.

  7. Leading a strategy of analytical decision-making, understanding that change requires goal setting and guidance.

  8. Identifying analytical leverage points.

  9. Demonstrating determination over time, supporting the analytical process.

  10. Building a cultural ecosystem that supports managers, employees, and partners in analytical activities.

  11. Simultaneously working on multiple analytical fronts.

  12. Acknowledging that analytical analysis only solves some business/organizational problems.

How to proceed in stages:

  • From a Phase 1 organization to a Phase 2 organization:

    - Encourage leadership growth in various units.

  • From a Phase 2 organization to a Phase 3 organization:

    - Develop a vision of how analytical information will be utilized in the organization's future and commence identifying the necessary capabilities.

  • From a Phase 3 Organization to a Stage 4 Organization:

    - Integrate and secure leaders' commitment to creating analytical capabilities, especially regarding data, technology, and personnel.

  • From a Phase 4 Organization to a Phase 5 Organization:

    - Encourage leaders to gain visibility into existing analytical capabilities.

    - Communicate to internal and external stakeholders how analytics contributes to success.

Tip: Analytical leaders don't rely solely on analytical skills; their decisions blend science and art.


Similar to other matters, it is advisable to establish concrete objectives. Beyond their business significance, goals hold organizational importance as they foster management commitment and generate momentum to propel activity.

The two most critical decisions regarding objectives:

  1. Choosing the right opportunities. Possible opportunities:

    a. Promoting the organizational business strategic plan, if any.

    b. Identifying opportunities realized in parallel places in the industry/sector.

    c. Basing decisions on an overarching analysis of the organization and emerging trends relevant to it (economic, customer needs, etc.).

    d. Conducting a systematic analysis of business processes in the organization and identifying decisions that can benefit from an analytical aspect.

    e. Processes that can potentially benefit from analytical thinking meet the following conditions:

    -Complex decisions are based on variables and many stages.

    - -Simple decisions where consistency is desired or required (e.g., by law).

    - - -Where optimizing the entire process is needed.

    - - - -Decisions in which it is necessary to understand the nature of relationships and correlation between variables.

    - - - - -Places where it is required to predict or evaluate.Places where the success rate could be higher.

  2. Prioritizing the objectives on which the activity will focus by:**

    a. The estimated profit that can be derived from the activity.

    b. The supporting analytical capabilities required for the activity (from well-defined optimized data for specific decisions to the ability to integrate decisions in real time for such optimization).

How to proceed in stages?

  • From a Phase 1 organization to a Phase 2 organization:

    - Act where there are sponsors and reasonable data.

    - Search for initial successes.

  • From a Phase 2 organization to a Phase 3 organization:

    - Focus on areas where there is already analytical activity or where the apparent gap from such activity is significant. Target business processes or cross-application. Start systematically searching for opportunities.

  • From a Phase 3 Organization to a Stage 4 Organization:**

    - Engage in significant business processes and collaborate with their managers. Focus on obtaining high value and goals of great significance to the organization—organization-wide reference in the search for goal opportunities.

    - Formalize the goal-setting process as a collaborative effort between managers, information systems, and leaders.

  • From a Phase 4 Organization to a Phase 5 Organization:

    - Collaborate with a senior leadership team, focusing on strategy, creating value, and building a unique significant advantage for the organization. Influence organizational strategy and not just derive from it.

Tip: Avoid spreading out over too many goals, especially when the organization is still learning.


In various domains, we often overlook the individuals behind the data and software. Analytical analysts represent a crucial resource that requires astute and sensitive management.

There are several types of analytical analysts:

  1. Analytical Champions: Managers heavily rely on data and analytical analysis for decision-making.

  2. Analytical Professionals: Employees developing analytical models for organizational use.

  3. Semi-Professional Analytical: Employees implementing analytical models in the organization.

  4. Amateur Analytical: Employees whose primary role is not analytical but need analytical understanding for successful task execution.

An organization should maintain a balance of these four types, each requiring different doses of the following skills:

  1. Knowledge and quantitative understanding (mainly for professionals and then for semi-professionals).

  2. Knowledge and understanding of the business aspect (mainly for champions and then for semi-professionals).

  3. Skills in connections and counseling (mainly for champions and then for semi-professionals).

  4. Knowledge of coaching and team development (especially for champions).

A 2008 study revealed that analytical analysts tend to be connected and loyal to the workplace, expressing higher satisfaction with their roles than the average employee.

To motivate analytical employees, proactive measures should be taken, including:

  1. Diversity at work and a sense of personal progress.

  2. Recognition of the importance of their contributions to the organization.

  3. Organizational support with autonomy in their work.

  4. Creating a work environment surrounded by intelligent and capable individuals.

While there's no definitive answer to the proper organizational structure, the authors prefer a centralized or center-of-excellence structure. Even in distributed setups, these employees must be managed for optimal resource utilization and ongoing development.

How to proceed in stages?

  • From a Phase 1 organization to a Phase 2 organization:

    - Identify pockets of analytical talent. Offer tutorials. Encourage analytical elements in projects. Engage in dialogue with managers to foster appreciation for these employees.

  • From a Phase 2 organization to a Phase 3 organization:

    - Identify analytical roles and use diverse recruitment methods to fill these positions.

    - Encourage cooperation and dialogue among analytical analysts. Implement job rotations, especially for professionals.

  • From a Phase 3 Organization to a Stage 4 Organization:

    - Evaluate the analytical capabilities of knowledge workers in the enterprise.

    - Develop relationships with universities and institutes, offering advanced training to analytical professionals. Focus on business development and integrate aspects of model development and implementation into business processes. Establish a knowledge community for analytical analysts.

  • From a Phase 4 Organization to a Phase 5 Organization:

    - Relate employees' analytical abilities as a parameter for recruitment to any position in the company.

    - Formalize rotation processes.

    - Create a recognition mechanism and consistently challenge analytical analysts within the organization.

A non-standard example of an analytical amateur is film actor Will Smith, who bases his film selection on analyzing the characteristics of successful films in theaters. This approach has proven successful, as he is considered one of the highest-paid actors in Hollywood today.

One last tip: Remember that analytical analysts are people who can feel isolated if not managed.

Enterprise infrastructure

Beyond all the aspects mentioned earlier, an organizational infrastructure is essential to facilitate the transformation of an organization into an analytical one, ensuring the perpetuation of an analytical culture over time.

Analytical organizations exhibit three standard practices:

  1. Work Processes: Analytical activity is seamlessly integrated into work processes through three possible methods:

    a. Automated by software following an analytical decision.

    b. Automatic by the software, with the employee having the ability to override the system decision.

    c. Manual decision supported by editorial recommendation.

    Given information systems' significant role in work processes, it's valuable to explore integration into already computerized processes.

  2. Organizational Culture Supporting Analytical Conduct: An organization, within its guiding values, fosters an analytical culture that involves:

    1. The pursuit of truth.

    2. Pattern identification and root cause analysis.

    3. Attention to detail.

    4. Demand for data over "stories."

    5. Evaluation of adverse outcomes, not just successes.

    6. Use of data for decision-making.

    7. Pragmatism is about where data can genuinely aid decision-making and where it cannot.

    It's worth noting that an analytical culture also encourages other positive behaviors like transparency and honest customer interaction.

    Tip: The authors discovered a gap between dealing with data and using it for decision-making. Recognizing and addressing this gap is crucial; otherwise, all efforts may be in vain.

  3. Continuous Improvement: Never stand still; always seek required enhancements and ways to adapt and progress. Areas to monitor include:

    1. Changes in organizational strategy or business model.

    2. Changes in objectives that analytical activity should focus on.

    3. Adjustments necessitated by competitors' activities.

    4. Customer or partner behavior changes (e.g., mortgage repayment patterns shifts before the 2008 crisis).

    5. Technological advancements.

    6. Shifts in data or information (considering the vast information available online).

    7. Alterations in laws and regulations.

Like any change, managing the transition to an analytical organization requires careful consideration of obstacles and challenges. The process should be gradual, with difficulties expected and requiring ongoing cultivation and management even after the organization becomes analytical. According to the authors' research, the business potential inherent in making an organization more analytical is remarkably high, making the effort undoubtedly worthwhile.

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