BI project planning and implementation plan is the primary link to ensure the smooth implementation of BI projects. Good project planning can effectively improve the efficiency of developers, shorten the project cycle, and achieve the expected goals of the project. (The importance of project planning). Around project planning, companies need to determine three things: what to do, who will do it, and how to do it.
Defining the scope of the project and assembling the project team is a critical step in ensuring the success of the project before the BI project is developed. The determination of the scope of a project involves a clear definition of the project's objectives, required features, expected outcomes, and the project's time and cost constraints. This process is critical to prevent project sprawl, ensure efficient use of resources, and meet stakeholder expectations. At the same time, the formation of a cross-functional project team can ensure that the project is considered from multiple perspectives, thereby improving the practicality and effectiveness of the project solution. Close collaboration and clear role assignment among team members help to improve the efficiency of project execution, reduce communication barriers, and speed up the problem solving process. Through these upfront preparations, a solid foundation can be laid for the BI project, increasing the likelihood that the project will be successfully completed on time and on budget.
This article will answer in detail how to determine the scope of a BI project, that is, what to do, and how to form a team with an orderly division of labor, that is, who will do it, so as to lay a solid foundation for BI project development.
1. Determine the scope of the project: what to do
The first step in project planning is to define the scope of the project based on the needs and objectives of the project, and this is where gathering and clarifying requirements at the beginning of the project comes in handy. It is not enough for the project manager to know what the project scope means, the most important thing is to define the project scope correctly and clearly. If the scope of the project is not clearly defined, it will directly lead to unexpected changes in the project content, which may lead to the final cost of the project, serious delays, deviations from the original goals, and affect the development of the entire project and the motivation of the project team members.
Specifically, the scope of a BI project needs to be planned from five aspects: organization, function, business, data, and interfaces.
1. Organizational scope
The scope of the organization is framed by the main body of the implementation project, and the enterprise needs to clarify whether the current project needs to be implemented only at the headquarters or at the headquarters and all subsidiaries, and which business units are involved in the implementation.
2. Functional scope
The functional scope refers to the functional modules and specific functions contained in the BI project, as shown in the following figure. IT developers can learn and master BI tools in advance according to the scope of functions, and be more targeted and efficient in their development.
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3. Business scope
The business scope describes the daily business processing and analysis tasks that the enterprise needs to achieve through the BI system, and mainly defines the business module, analysis application, analysis dimension, analysis form, etc.
(1) Business module
Business modules refer to different business areas or functional areas within an enterprise, such as sales, finance, inventory management, human resources, etc. Within the scope of a BI system, it needs to be clear which business modules will be included in the analysis and how these modules are related to each other. The determination of business modules is the first step to achieve data integration and analysis, which directly affects the breadth and depth of data analysis.
(2) Analytical applications
Analytic applications refer to specific analytic tools or applications that are used to perform specific analytic tasks, such as predictive analytics, trend analysis, anomaly detection, and so on. These applications are often designed to solve a specific business problem or meet a specific decision-making need. In a BI project, you need to define which analytic applications are necessary and how to integrate them with business modules.
(3) Analyze dimensions
The analytics dimension is a key concept in data analysis, which represents different perspectives or classification criteria for data analysis, such as time, place, product type, customer group, etc. In BI systems, properly defining the dimensions of analysis is essential for multi-dimensional analysis of data, allowing users to look at and understand data from different angles to gain more comprehensive business insights.
(4) Format of analysis
The form of analysis refers to the way data analysis is presented, including charts, reports, dashboards, scorecards, etc. These formats determine how the user receives and understands the results of the analysis. In a BI project, you need to determine which forms of analysis are best suited to present different types of data and analysis results, and how to match these forms of analysis to the user's decision-making process.
Combining these four parts, a comprehensive BI system business scope description can be constructed, which will guide the planning, implementation, and optimization of BI projects, ensuring that BI solutions can meet the specific business needs and decision support requirements of enterprises.
4. Data range
The data scope not only describes where the data comes from, but also includes the understanding of the source data, the quality assurance of the source data, and the extraction of data.
It involves the following key aspects:
(1) Data source
This refers to the starting point of the data, which is where the data is stored and generated. Data sources can include the enterprise's internal systems (such as ERP, CRM, SCM, etc.), external data providers, public databases, Internet data sources, etc. Understanding the source of the data is critical to determining the reliability and relevance of the data.
(2) Data understanding
Data understanding involves an in-depth understanding of what data means, how it is structured, and the business concepts it represents. This includes understanding the name of the data field, the type of data, the business meaning, and the logical relationships between the data.
(3) Data quality assurance
Data quality is key to the success of a BI project. Data quality assurance measures include ensuring the accuracy, completeness, consistency, and timeliness of data. This can involve processes such as data cleansing, data validation, and data quality monitoring.
(4) Data extraction
Data extraction refers to the process of extracting data from a source system. This typically involves an ETL (Extract, Transform, Load) process, in which data is extracted, undergoes the necessary transformations to suit the analysis needs, and then loaded into a data warehouse or other data storage system.
(5) Data association rules
Data association rules define relationships between different data entities, for example, sales data, customer data, product data. These rules are essential for data modeling and subsequent data analysis.
(6) Data integration
In a multi-source environment, data consolidation is the process of merging data from disparate sources into a unified view. This helps provide a comprehensive view of the data and supports cross-departmental decision-making analysis.
(7) Data security and privacy
When defining the scope of data sources, you also need to consider the security and privacy protection of your data. This includes data access control, encrypted transmission and storage, compliance with data protection regulations, and more.
(8) Data governance
Data governance is a set of processes and policies that are used to ensure the quality and availability of data. It includes rules for creating, storing, using, sharing, and deleting data.
The following diagram gives an example of scoping a data source.
By clarifying the scope of data sources, businesses can ensure that the data foundation of the BI system is solid and reliable, thus supporting effective data analysis and decision-making.
5. Interface range
Consider whether the BI system needs to be embedded in the enterprise's other information systems and implement functions such as single sign-on, and if so, clarify how the system interfaces, such as who provides it, who designs it, who develops it, etc.
An accurate definition of the scope of the project is a prerequisite for ensuring that the project runs smoothly. It requires project managers not only to have a deep understanding of the project's goals and needs, but also to be able to anticipate potential changes and challenges, and develop strategies to deal with them.
In order to achieve the project goals, the formation of the project team is also crucial. A well-structured team with complementary skills is the guarantee of the success of the project. The project team should be formed based on the specific needs of the project and ensure that the team members have the professional skills and experience required to complete the project tasks.
2. Assemble the project team: who will do it
The project team is the "brain" of the enterprise BI project construction process, and the project team with a clear division of labor and orderly coordination is the key to the success of the project. Since the construction of a BI project involves multiple departments within the enterprise and requires the recognition and participation of senior managers and business departments, the project team usually has several senior managers as the core, and a project leadership committee is set up to coordinate the entire project, and other members are led by the head of the enterprise IT department, and different teams are set up together with the contact people of various departments to participate in the planning and implementation of the BI project.
The roles of the project team are divided into four categories: team leader, business master, solution designer, and technology implementer. Each type of role can be further subdivided, for example, technology implementers can include data warehouse (data warehouse) development team and application development team. If an enterprise builds a BI project by introducing BI vendors or outsourcers, it is necessary to form a project team based on the actual situation of the enterprise, BI vendor, or outsourcer. However, it should be noted that the project leadership committee needs to be set up by the enterprise itself to ensure the overall control of the project. The specific roles and corresponding responsibilities are as follows:
1. Project Leadership Committee
- Responsibilities: The top leadership team of the project is responsible for guiding the direction of the project, making key decisions, and ensuring the authority of the project within the enterprise.
- Members: It can be composed of the CEO, the VP in charge of informatization, or the VP of the corresponding business port.
2. Consultant team
- Responsibilities: Responsible for the technical and business guidance of the whole process of BI system architecture, demand planning, and process planning.
- Members: CIO/CTO, System Architect, Business Director, Industry Consultant.
3. Project manager
- Responsibilities: Define, plan, coordinate, control and inspect all project activities, track and report progress, solve technical and business problems, and guide team members to negotiate with BI vendors, business personnel, and project sponsors.
- Members: Project Manager.
4. Data management team
- Responsibilities: Responsible for designing and monitoring the database environment required by the project, formulating data standards, and analyzing data quality.
- Members: Database Administrators, Metadata Administrators, Data Quality/Security Administrators.
5. Data warehouse development team
- Responsibilities: Responsible for ETL development and topic-dimensional modeling in the process of data warehouse construction.
- Members: ETL Engineer, Data Modeling Engineer.
6. Business analysis team
- Responsibilities: Participate in the development of business dimension models, provide data definitions, write test cases, define business requirements, and build a bridge of communication between business personnel and IT personnel.
- Members: Business Requirements Analysts, Business Representatives.
7. Application development team
- Responsibilities: Responsible for the development of front-end reports and queries, as well as data analysis and mining.
- Members: Reporting/BI Engineer, Data Mining Expert.
8. Quality control team
- Responsibilities: Responsible for process improvement and quality assurance, and responsible for testing tasks in all aspects of system development, delivery and launch.
- Members: QA Engineer, Test Engineer.
9. Train the support team
- Responsibilities: Develop and publish training materials, provide training and technical support to key users.
- Members: Technical Support, Solution Promoter.
A successful BI project is not only the success of the technical implementation, but also the success of the teamwork and project management process. Through a clear division of roles, close teamwork, effective communication and coordination, and continuous learning and improvement, enterprises can ensure that BI projects can bring the expected business value to the enterprise and promote the digital transformation and long-term development of the enterprise.
III. Summary
By delving into how to define the scope of a BI project and how to assemble an effective project team, this article aims to provide readers with a clear guide to ensure that the BI project is on the right track. A clear project scope helps to focus on key objectives and avoid wasted resources, and a diverse and complementary project team can help achieve these goals.
With the establishment of the project team and the clarification of role assignment, the next work will turn to specific project implementation strategies, including key links such as technology selection, data integration, system development, testing and deployment. Through these comprehensive preparations, companies will be able to ensure the successful implementation of BI projects that enable data-driven decision-making, improve business efficiency, and ultimately achieve long-term strategic goals.