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Business Intelligence for Dummies - Book Review

1 August 2012
Dr. Moria Levy
book cover

The book "Business Intelligence for Dummies" is part of a series of textbooks designed for non-experts, but it is also highly recommended for experts. It comprehensively covers the fundamentals of business intelligence (BI), starting from the basics and providing an overview of its historical development, including brand analytics and various other aspects.

The summary primarily focuses on the practical aspects of business intelligence, with less emphasis on other facets. Notably, it does not delve into the realm of data warehouses, despite their close association with business intelligence—a noteworthy detail given the book's concise nature spanning over 350 pages. Interestingly, many managerial and project aspects discussed could easily be found in a knowledge management book.

The book explores the following topics:
  1. Introduction: What is Business Intelligence

  2. Types of BI solutions

  3. Reports

  4. OLAP

  5. Dashboards

  6. Advanced methods

  7. Fulfillment process

  8. Strategy

  9. Project Plan

  10. Collecting requirements

  11. Design & Development

  12. Maintenance and improvements

  13. Infrastructure

    1. Technological infrastructure: software products

    2. Human infrastructure: functionaries

  14. The Big Ten - 10 tips on various topics related to the realization of business intelligence in the organization

  15. 10 keys to business intelligence success

  16. 10 Risks in BI and ways to cope

  17. 10 Tips for Collecting Requirements

  18. 10 tips for good assimilation

  19. 10 Tips for a Healthy BI Environment

  20. 10 signs that the BI environment is at risk

The book is comprehensive, precise, and written in simple language, making it easily accessible. It is recommended for anyone seeking a closer understanding of business intelligence. Moreover, it is a valuable resource for those working in the field, aiding in knowledge organization or preparing training materials. It's also worth exploring for individuals involved in general project management and knowledge management projects.

Happy reading!

Introduction: What is Business Intelligence

Business intelligence is the achievement of accurate, valuable, timely, and action-oriented business insights facilitated by processes and technologies. It is imperative to focus on the four critical components outlined in the definition:

  1. Accurate

  2. Valuable (Enterprise Business)

  3. Timely

  4. Action-oriented

Clarity regarding how officials should utilize the received data/information is crucial. This defines a cyclical process characterized by continuously improving BI activities, using data and information, measuring, and assimilating lessons learned. Further details regarding the processes and technologies are elaborated in subsequent chapters.

Critical considerations in establishing BI activities include:

  1. Centralized organizational BI or decentralized departmental BI: The recommendation is to initially initiate departmental, focused activities and transition to a corporate response based on experience.

  2. BI's role in supporting strategic or tactical decisions: There is no unequivocal recommendation; the answer depends on the organization. For organizations centered around operational aspects, commencing at the tactical and operational levels is advised for significant business-added value.

Less critical issues include:

  1. User-friendliness vs. powerful tools: Despite vendor claims, there is tension between user-friendliness and powerful tools. Decision-making should align with users' primary business needs.

  2. Analytical reports or forecasts: No specific recommendation is provided.

General insights include:

  1. BI is as much an art as a science.

  2. Avoid thinking too technologically; consider people as well.

  3. Suppliers may oversell in words compared to actual delivery. Be cautious and expect variances.

Types of BI solutions


A frequently asked question regarding reports revolves around the distinction between reporting and querying tools. There is no discernible difference in contemporary terms as suppliers have amalgamated these two solution types. Initially, querying tools were employed for elucidating ad hoc data, while reports were tailored to fixed needs, emphasizing product visibility.

The functionality of these tools encompasses:

  • Definition of SQL queries and instructional screens (customizable for each user)

  • Data calculation capabilities

  • Graphs facilitating the incorporation of charts and graphs

  • Effortless publishing and dissemination of information, with data export capabilities

  • Scheduling of reports

  • Management of permissions

Advanced capabilities found in some tools include:

  • Adaptive authoring of reports

  • Displaying reports with updated (refreshed) data upon reopening

  • Automatic translation of a query into a predefined custom report (interactive reporting)

  • Alert capabilities

Key features of a report tool include:

  • User-friendliness

  • Web-based accessibility

  • Speed (considering the substantial size of data warehouses)

  • Forwarding deliverables (e.g., to Excel)

  • Drill-down capability through reports, gradually revealing information

  • Flexibility, allowing regular runs and receipt of alerts based on conditions

These tools cater to a diverse range of target audiences:

  • Information consumers: Generating regular reports for ongoing tasks

  • Intermediate BI consumers: Creating ad hoc variable reports to clarify issues

Power users, including developers and administrators who prepare reports for others, fall into the category of experts.


OLAP (Online Analytic Processing) is an online application that facilitates the interactive display of processed data. Typically, this pertains to online reports enabling users to delve into and explore alternative perspectives of various dimensions within the same dataset. For instance, users can present sales data by product, swiftly transition to a timely view, switch to a country-specific perspective, or easily explore specific areas. While it's feasible to simultaneously present data according to two dimensions (and some tools even support three), further dimensions require switching and focusing on different partial views each time. The varied information displayed across dimensions is often called a "cube," symbolizing the three-dimensional view.

Architecturally, this requirement can be addressed through several alternatives:

  1. MOLAP: Unique M-Multi database.

  2. ROLAP: Tabular database, flattened by the R-relational.

  3. HOLAP: Integrated database (H-Hybrid).

Critical concepts in OLAP include:

  • Dimension: A perspective through which data is examined, such as places or time. Note: Dimensions can exist at multiple levels, for example, Time: year-month-day.

  • Measure: A quantitative figure, such as income or profit, is presented in a table. ROLAP is stored in tables known as fact tables.

  • Consolidation and Drill Up: An increase in the dimension level and examination of summary data.

  • Drilling Into: Diving and deepening, involving a decrease in dimension level and examination of more detailed data. Example: Moving from viewing sales in cities to examining sales in each store.

  • Pivoting: A multidirectional view, like Excel, allows the examination of the same data from different perspectives.

For those interested, This field was known as Executive Information Systems (EIS) a decade ago.


A Dashboard, or "dashboard" in Hebrew, draws its name from the cockpit, encompassing numerous indicators for awareness and action. Evolving from the Executive Information Systems (EIS) world, dashboard solutions are interactive screens that use various indicators, as detailed below, to depict the organization's status. They enable managers to grasp the bottom line, highlighting and alerting to exceptions that demand knowledge or intervention.

Dashboards rely on key performance indicators (KPIs), crucial indicators of organizational performance, to present a corporate picture. While not exhaustive, well-planned measurements can be highly representative, akin to a school certificate providing a comprehensive yet not thorough view of a student.

KPIs must balance being focused enough to analyze the source of organizational/business problems and being general enough to offer an overall corporate picture without drowning in details. Examples of KPIs include average customer wait time at a call center, stock replenishment frequency, monthly percentage of defective units, average sales cycle time, and sales relative to store floor square meters.

Dashboards can be numeric but are often color-coded using traffic light colors and arrows: red indicates a problem, yellow/orange denotes an intermediate state, and green signals normal conditions. These colors may be part of a traffic light, an indicator like a fuel gauge, or a circle in the relevant color.

Critical properties of dashboards include:

  1. Views that facilitate navigation between different panel areas.

  2. Attractive panels with versatile graphics.

  3. Interactivity of the boards.

  4. Interface adaptability to the organization and user group, considering the target audience.

  5. Integration capability of external content with organizational data (e.g., stock data).

  6. A web-friendly interface.

Dashboards can be tactical, operational, or strategic based on organizational needs and the stage of BI activity. A related tool is the scorecard, which aims to graphically illustrate progress against planned organizational/business goals, also relying on KPIs.

Advanced methods

Today, additional tools facilitate the delivery of data-based messages to users through advanced methods, allowing for navigation and other features outlined in the previous Dashboards section (WEB-y interface, etc.). Conventional tools include spatial visualization techniques, such as:

  1. Geographical maps.

  2. Layer display.

  3. Three-dimensional geometric structures.

Another family of advanced tools is Data Mining, which relies on sophisticated algorithms to identify mathematical patterns, predict future behavior based on the past, or unveil behavioral aggregates not apparent to the observer without supportive software. Examples, notably in Israel, involve insights like young men buying diapers on weekends being highly likely to purchase beer and increased consumption of halva during Ramadan in Arab and Muslim localities.

The latest trend in the BI world involves content mining from unstructured information (documents). Here, the emphasis is not on proactively searching for the user but on identifying patterns and extracting valuable information for the organization.

While these observations hold at the time of writing, other advanced methods will likely emerge—patience is key. [It should be noted that at the time of writing the book in 2008, the analytical subworld, though covered to a lesser extent in the book, was flourishing.]

Fulfillment process


Let's begin with the conclusion – there is no one-size-fits-all recipe, and no single methodology dictates how to proceed. Each consultant has their own methods, just as each organization has unique needs. When an organization embarks on Business Intelligence (BI) activities, it is advisable to formulate an action strategy for organizational realization.

  1. Current Business Assessment:

    a) Business functions

    b) Operational processes

    c) Identifying painful issues/opportunities

  2. Current Technology Assessment:

    a) Infrastructure

    b) Tools

    c) User interfaces

    d) Permissions management and information management policies

  3. Planning the New Technological Infrastructure:

    a) Deciding what will be preserved and integrated into the future strategy

    b) Deciding what to eliminate/ignore

    c) Deciding what to expand

    d) Deciding what is worth purchasing

  4. Description of the Organization's BI Vision (Utopian BI):

    a) Technological infrastructure

    b) Data management

    c) Business infrastructure

    d) The writer recommends not rushing this stage and using it to define successes and aspirations.

  5. Evaluation of Possible Obstacles:

    a) Human

    b) Methodological

    c) Processes

    d) Technological

    e) Corporate politics

  6. Examining Alternatives and Deciding on Feasibility:

    a) In the existing organizational situation

  7. Identification of Risks and Coping Strategies:

    a) Data risks

    b) Application risks

    c) Organizational risks

    d) Budgetary risks

  8. Identification of Business Value Derived from BI Activity

  9. Choosing an Alternative that Suits Organizational Structure and Culture:

    a) Taking into account risks and potential added value

  10. Gaining Buy-In Support in the Organization

Following the strategy, a Roadmap plan is constructed for the initiation of the activity:

  1. Defining the Business Problem and Demarcating Solution Content

  2. Definition of Information Required for the Solution:

    a) Setting its location and condition

  3. Preliminary Budget Analysis and Estimated ROI

  4. Overview of Hardware Requirements for the Project

  5. Definition of Existing Supporting Software and Acquisitions for Project Benefit

  6. Definition of Project Team and Responsibilities

  7. Details of Risks, Constraints, and Other Possible Problems

The book's author delves into each section, providing detailed insights, such as possible costs for budget estimation or options and preference criteria for business distinction.

Project Plan

Like any project, a Business Intelligence (BI) project requires effective management. The project plan should encompass the following elements:

  1. Resources

  2. Partners and Functionaries

  3. Tasks:

    a) Super-level

    b) Detailed milestones

    c) Schedule

  4. Connections and Dependencies of Tasks; Constraints

  5. Risks and Coping Methods:

    a) Including a continuity plan

    b)Control points

A warm yet non-trivial recommendation is as follows:

  1. Keep Your Project Plan Up to Date:

    a) The program may change due to budget adjustments, shifts in goals, technological constraints, and alterations in team composition. Be prepared!

  2. Monitor Daily Task Status and Progress:

    a) Keep track of both inputs and outputs

Effective management ensures the smooth execution of the BI project and allows for adaptability to changes that may arise during its course.

Collecting requirements

We expect that users will communicate their business needs, and from there, we can easily derive Business Intelligence (BI) solutions. However, this expectation is often unmet, leading to significant mistakes and catastrophic results. It's crucial to recognize that this process is not solely about technology but, fundamentally, about the business itself.

The collection of requirements should be approached with the right mindset and should include the following steps:

  1. Precise Demarcation

  2. Set Outputs

  3. Details of Business Requirements: Prioritize

  4. Work Processes and Expected BI System Use in These Processes

  5. How to Manage Changing Requirements and Content Changes

  6. Prototypes and Screenshots

  7. Example Scenarios

Business analysts should engage in conversations with users at various levels and sponsors. It's strongly recommended to refrain from involving technologists at this stage. During the requirements-gathering phase, it's essential to clarify with stakeholders the data they need to enhance their work and how BI tools can effectively provide this data.

When defining how BI tools can offer the best solution, considerations should encompass reports, graphs, dashboards, and the use of advanced methods. Attention should be given, both at this stage and later, to the importance of the user interface, emphasizing its convenience and simplicity.

After the requirements-gathering process, two final sub-stages are critical:

  1. Validation of Requirements (Vis-à-vis Users)

  2. Prioritization of Requirements:

    a) Identifying what needs immediate attention and what can be deferred for the future

By following these steps, the requirements-gathering process becomes a crucial foundation for developing effective BI solutions tailored to the specific needs and priorities of the business.

Design & Development

Preliminary Planning Recommendations:

  1. Success is not guaranteed and relies heavily on implementation.

  2. Be realistic in planning – delineate.

  3. Move according to user demand; don't rush, even if it is technologically feasible.

  4. Act based on immediate needs but consider long-term needs.

  5. Users, their abilities, will, and way of thinking should guide the planning process.

Recommendations for the Various Planning Stages:

  1. Data Environment Planning:

    a) Analyze: 1. Identify information sources and assess their qualitative status; evaluate their accessibility for BI. 2. Understand users' questions; create unique templates for necessary reports. 3. Consider performance requirements and how quickly users expect answers.

    b) Assemble the most talented individuals on your team to design a robust ETL (data transfer) process.

    c) Design a more normalized foundation with minimal repetition if flexibility is needed in posed questions.

  2. User Environment Planning:

    a) Reports: The Core of All BI Activity: 1.Design a software system enabling a tool for defining and generating reports; management services for running, storing, and authorizing reports; a portal for entering and viewing reports. If users need non-standard reports, design a dedicated subenvironment. 2. Establish a fixed pattern (header, footer) for reports, including legal reservations, a current corporate logo, and a date.

    b) OLAP: 1. Identify variables to track and measure; group them by domain. 2. Define relationships between different entities. Consult an expert to define dimensions. 3. Specify formulas and snippets to display OLAP cubes.

    c) Analytical Applications: Remember that despite the complexity of the solution, the product is only as good as the quality of data, the correctness of the model, and the suitability of reports for organizational needs.

Note: No specific design guidelines are provided for Data Mining and Dashboards. Before concluding, remember to validate in front of users, conduct tests, and remember that piloting is critical.

Maintenance and improvements

Preliminary Recommendations for Maintenance and Improvements:

  1. Recognize that merely implementing software, tending to content, and preparing reports is insufficient; BI is a process, not a project.

  2. After going live, plan a trial phase. Experience is distinct from testing, and additional, albeit few, demands not expressed before may arise. Don't panic; it's natural. Always be prepared for part B of a project that includes improvements.

  3. Conclude the overall project aspect, the technological aspect, and the perspective of business impacts and implications. If indices/goals have been defined from the business aspect, examine against them. Pay attention to whether it is possible to produce the desired reports and whether it is indeed more accessible, cheaper, and faster to have them in the new system.

During the maintenance stage, the most crucial aspect is ensuring the stability of the system in several dimensions:

  1. Software stability: Always use up-to-date versions.

  2. Data stability: Periodically maintain databases and orient them.

  3. Stability of response times: Regularly monitor and ensure stability.

Beyond that, for maintenance, ensure that:

  1. The proposed solution remains relevant in light of ongoing business changes.

  2. There is support for operational issues and malfunctions.

  3. A mechanism is in place for receiving and managing feedback.

  4. Regular guidance is provided for users.

  5. The BI team actively participates in setting standards and organizational governance policies.

  6. There is regular contact with sponsors in management


Over time, the luster of BI systems diminishes. To rekindle enthusiasm, it is recommended to:

  1. Address unmet needs from previous stages.

  2. Provide a more sophisticated solution in business areas where an initial solution is offered (e.g., analytical analysis).

  3. Enhance accessibility.

  4. Install new software versions and advanced tools.

  5. Expand the user base.

Every 18-24 months, conduct a comprehensive survey regarding usage, level of performance, added value, and needs.


Technological infrastructure: software products

To establish a Business Intelligence (BI) environment, the setup involves creating a central data warehouse (Data Warehouse), small distributed warehouses (Data Marts), or combinations of both, presenting data without a unifying data source.

As part of the data warehouse, it's customary to establish a repository (Master Data Management - MDM) that describes all data, providing their name, description, characterization, responsible factors, and related business laws. Additionally, each piece of information is described with details on when, how, and where it was derived.

As of the book's writing in 2008, the author outlines all major software vendors in related fields, listing the capabilities of each in the following areas:

  1. Databases

  2. Data integration tools (ETL tools, Data warehousing tools)

  3. Polling tools, reports, and graphs, including OLAP

  4. Data mining tools; Analytical tools

  5. Additional dashboards and visualization tools

  6. Interfaces (portals and mobile access tools)

Furthermore, it's essential to remember that there are complementary technologies to the BI environment:

  • ERP (Resource Management Systems): One of the three primary operational sources of data that BI relies on and supports.

  • CRM (Customer Management Systems): Another of the three critical operational data sources supporting BI.

  • Finance: Finance is the third primary operational data source that BI relies upon and supports.

  • Knowledge Management (KM): A complementary technology aiding decision-making.

Human infrastructure: functionaries

Every Business Intelligence (BI) project, regardless of its size or delineation, is significantly influenced by the human aspect. The correct consideration of this aspect is at least as important as the technological aspect and should be treated accordingly.

Key functionaries in this context include:

  1. BI Team:

    1. Project Manager

    2. Business Analyst – An individual not necessarily specializing in BI but proficient in interviewing business functionaries, understanding their needs across content areas, and providing suitable BI solutions. An experienced BA can be pivotal for successful realization.

    3. Data Architect / DA - An expert in structuring databases and various data structures, responsible for converting data from the operational world to the data warehouse. They may also handle data cleansing unless designated functionaries exist within the organization.

    4. Supporting Functionaries:

      1. BI Infrastructure Architect – Responsible for Software Products.

      2. App Developer.

      3. DBA - Data Administrator.

      4. Software Tester – QA Specialist

  2. Officials in the Organization:

    1. Users

    2. Content Experts

    3. Administrators

    4. Sponsor – A senior manager is leading and overseeing the activity.

Considerations from the human aspect include:

  • Training the BI team, developing their expertise, and providing ongoing support.

  • Establishing a supportive organizational structure, whether decentralized in departments, centralized in IT, or a Business Intelligence Competency Center (BICC) that includes business functionaries.

  • Implementing change management practices concerning users and content experts (where all relevant change management techniques apply to this section).

The Big Ten - 10 tips on various topics related to the realization of business intelligence in the organization

10 keys to business intelligence success

  1. Selecting KPIs that genuinely contribute to better decision-making and performance improvement.

  2. Customizing methodologies/recipes to the organization's specific needs, avoiding rigid adherence to standardized solutions.

  3. Recognizing that, despite supplier and consultant assurances, this is a complex journey that is neither easy nor simple.

  4. Engaging in innovative thinking and activities beyond conventional boundaries.

  5. Building a solid team—considering both professional expertise and interpersonal dynamics.

  6. Continuously studying methodologies, tools, and methods for realizing business intelligence.

  7. Acknowledging that failures and mistakes are inevitable, displaying the wisdom to learn from them and avoid repeating them.

  8. Incorporating organizational culture considerations from the initial stages of activity and during requirements gathering.

  9. Implementing a rolling project approach, emphasizing smaller tasks that demonstrate the feasibility and allow for gradual progress.

  10. Securing a senior manager who serves as a key sponsor for the BI activity.

10 Risks in BI and ways to cope

  1. Objections: Addressing objections requires building stable and meaningful relationships with information systems professionals, information experts, and other partners. Establishing a joint steering committee or center of excellence provides a framework for applying ideas and fostering collaboration.

  2. Changing Goals: When faced with varying goals, linking them to other organizational goal systems and actively seeking changes allows for assessing the need for appropriate BI adjustments.

  3. Higher Expectations of Software Tools and Applications: To manage higher expectations, reducing the risk of choosing software products can be achieved by requesting demos, working with suppliers who transparently discuss product challenges, and engaging in dialogues about difficulties with existing customers.

  4. Users Not Using the System: Addressing user adoption challenges involves integrating users into the process, even when the BI team believes it understands their needs. Statistical usage tracking helps identify and address usage problems in the initial stages.

  5. Problematic Data Quality: Dealing with complex data quality involves understanding that improvement is ongoing. Constant monitoring and identifying problems during the data transfer to the data-ETL warehouse contribute to practical solutions.

  6. Budget Shortages: Addressing budget shortages involves considering all costs in advance whenever possible and avoiding executing a project at any cost.

  7. Increased Demarcation: To manage increased demarcation challenges, starting with broad demarcation and preparing for change from the outset is critical. Working with skilled business analysts aids in accurate planning. Managing change according to established practices prevents panic when changes occur.

  8. Adherence to Initial Decisions: Coping with adherence to initial decisions requires preparation for flexibility. Including tools that allow users to add their responses to additional needs later on is a strategic approach.

  9. Falling of the Environment: In the event of a system failure, the option to enter the tools directly, bypassing the overall environment, provides an alternative.

  10. Note: Despite there being numerous risks, the book mentions only nine.

10 Tips for Collecting Requirements

  1. People: Ensure that representatives from the departments you meet with are those who can contribute the most.

  2. Early Coordination with Managers: Initiate early coordination to align expectations and unify perspectives.

  3. Link Requirements to Organizational Issues: Relate requirement issues to the primary organizational concerns and objectives.

  4. Ensure Alignment with Key Topics: Verify that all requirements are connected to the earlier key topics.

  5. Evaluate Existing Resources and Process Change: Understand where existing resources suffice and where process changes are necessary. Avoid rushing into too many changes.

  6. Consider Organizational Dependency on Data: Explore how the organization can function without certain data, should you waive a specific requirement. This understanding aids in prioritization.

  7. Comprehensive Requirement Collection: During requirement collection, seek to understand essential details for each topic, including required information, planned actions, processing methods, conveyance procedures, potential insights, mandatory preliminary steps, and associated obligations.

  8. Detailed Exploration of Data: Delve into the specifics for each required data and identify its current storage location, retrieval methods, condition, and implications if optimized or processed. Acknowledge that different departments may have distinct perspectives on the same data.

  9. Balancing Focus and Vision: While maintaining focus is crucial, plan for a broader vision. It adds value!

  10. Include Prioritization in Requirements Document: Ensure that your requirements document includes prioritization, reflecting both customer aspects (what's important) and project aspects (what's feasible).

10 tips for good assimilation

In any project, it is evident that implementation plays a crucial role in determining success. The challenge is not straightforward in non-operational systems, such as knowledge management and business intelligence. This is because people have often managed even without information and knowledge, making it a cultural change.

Here are tips for success in the implementation process:

  1. Initiate the implementation process as early as possible.

  2. Seek assistance from software vendors. You will need their support, and the contracts include their activation – so activating them should pose no difficulty. The combined products may not always function together as expected.

  3. Utilize user representatives not only to comprehend the requirements but also to consult and resolve content-related conflicts.

  4. In terms of marketing, do not assume customers will inherently know and remember the significance of your activities. Ensure the topic remains part of the organizational conversation.

  5. Do not overlook testing the software and the entire system.

  6. Establish a war room (war room/situation room) to concentrate on initial activities when going live.

  7. Effectively manage the project; ensure comprehensive management and control.

  8. Address foot-dragging and procrastination immediately. Do not let them escalate.

  9. Conduct a Proof of Concept (POC) before implementing the system organization-wide. Many difficulties are revealed at this stage.

  10. Pay attention to details. Even though the pilot must be short and fast, ensure all aspects are addressed.

  11. Bear in mind that visibility and edge reports can and will change. However, ensure your data and data structure remain stable and rigid. Do not allow occasional changes at this level.

For those who are still attentive – indeed, there are 11 tips, not 10. Perhaps compensating for the episode where a tip was missing...

10 Tips for a Healthy BI Environment

A stable BI environment must be constructed, capable of withstanding any changes. Here are 12 tips for appropriately planning the preliminary stages of the activity:

  1. Ensure data quality: Is it usable? Are the data complete? Are they mutually exclusive?

  2. Define functionality in advance, including the number of vendors and software involved and the number of information sources. This will minimize deviations from the project budget (and may be the key to its survival).

  3. Carefully and inflexibly plan schedules for each stage, adhere to them and remember to incorporate training and implementation times.

  4. Utilize techniques that have proven effective. Avoid reinventing the wheel each time.

  5. Establish a fixed and methodological process for learning from errors (lessons learned).

  6. Emphasize documentation!

  7. During the initial stage, concessions may be necessary. Remember to address any gaps in the subsequent version.

  8. Ensure regular and periodic software upgrades.

  9. Monitor activity and assess areas that require changes.

  10. Communicate changes; do not anticipate people to commence work without prompting.

  11. Invest significantly in training; despite supplier claims of clarity and unnecessary training, ignore them and invest.

  12. Manage maintenance regularly – including upgrades, training, and staff development.

Once again, the number 12 is more symbolic than practical. It remains unclear why the tips were compiled under one list instead of multiple chapters. Regardless of their organization, these tips are correct, helpful, and warrant attention.

10 signs that the BI environment is at risk

Despite meticulous planning and activity, specific challenges may arise. Here are 10 factors to be mindful of:

  1. Despite the success of the BI environment, some individuals persist in using Excel-based reports. Analyze each case individually and seek opportunities for change.

  2. Even with clear instructions, people may struggle to comprehend and pose questions. Be prepared with adequate resources.

  3. Some individuals may refrain from seeking help altogether. Recognize this as a potential concern and actively explore any signs of underutilization.

  4. The environment may be less intuitively accessible than anticipated. Conduct analysis and improvements, especially addressing usability issues. Informal conversations can be a valuable source of insights.

  5. Acknowledge that the past may seem more nostalgic and triumphant. If this perception persists, it could indicate a genuine problem.

  6. While more users may initially engage close to the launch, a well-designed BI environment should continually attract users. If not, analyze and identify potential issues.

  7. Ensure BI tools are regularly integrated into strategic planning. Monitor and investigate any deviations from this practice, as it could be a worrisome sign that requires attention.

  8. Senior executives and sponsors may lose enthusiasm over time. Avoid taking their commitment for granted and invest in maintaining their engagement.

  9. Recognize that senior managers and sponsors may leave the organization. Establish a safety net to mitigate potential impacts.

  10. Acquiring a budget for an initial project may be successful, but difficulties may arise in securing continuation funding. In such cases, signal the jeopardy of the activity and ascertain the reasons for the lack of excitement and perceived criticality, emphasizing the necessity for continued investment.

In summary, business intelligence extends beyond a mere project; it's a multifaceted journey with numerous tools and helpful tips for implementation. Now, it's time to start practicing. Best of luck!

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