With the adaptation of Agile Business Intelligence, several methods help you stay ahead and increase your chances of success. Successful BI implementations with little or no challenge often adopt an agile approach, and better yet, an evolutionary approach. Companies that are satisfied with the success they have achieved with Agile Business Intelligence have engaged in several practices to ensure their success.
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These best practices explain ways in which a company can reduce the overall risk in a project while increasing the likelihood that the project will meet the expected needs of its end users. BI solutions have evolved beyond traditional data warehouses and related multidimensional online analytical processing structures (cubes, star schemas, and more) in many important ways.
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Agile Business Intelligence (BI) refers to a method of delivering BI solutions in an iterative, flexible and collaborative manner, by incorporating Agile project management principles. This approach involves regular interaction and feedback between BI team members and business stakeholders, focused on delivering added value and responding to changing requirements. Flexible BI enables organizations to react quickly to changing market conditions and business needs, while providing data-driven insights and decision-making capabilities in a timely manner. By using Agile methods, organizations can deliver more effective BI solutions that meet business needs.
In addition to traditional relational databases, they can now integrate and analyze data from a number of sources. In addition to using traditional business graphics (e.g., bar and pie charts), the use of dynamic and interactive business graphics, such as real-time dashboards and interactive charts collaboration, can improve solutions’ ability to support users in increasingly demanding tasks.
Just-in-time modeling is whereby a business model attacks project details throughout the project as it goes on. Enterprises do not make detailed plans at the beginning of the project.
JIT is useful for a number of reasons; details will change throughout the project this will enable agility and flexibility and lead to a high performance project second in JIT model you will have a huge amount of knowledge from the very beginning of the project. Third, pre-modeling everything leads to a waterfall effect and significant waste.
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There exists a cause-and-effect relationship in a project. This occurs when an action on a project inadvertently affects another part of the project or organization because an enterprise does not have effective project controls in place to mitigate project-specific risks.
Risks should be communicated to key project and organizational stakeholders so they can help build risk mitigation across all parts of the organization.
Supporters and Operations are the key stakeholders in the project. Their request is what is building the project. And the sooner you involve them, the sooner you find the vision of the development team.
They may have annoying details that they wish were implemented in a DW/BI project. Gaining knowledge of these details early enough reduces the risk of dissatisfaction among project stakeholders.
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Doing some initial architecture will help your team establish a potential vision for building a data warehouse. As Agile Model Driven Development (AMDD) suggests, a detailed, comprehensive model is not required in advance but only a clearly established vision is important.
This will be used as a recurring reference for team members on the project. The vision can be as simple as an implementation diagram, and the top-level domain mode requires business entities and the relationships between them.
The right architecture and technology implemented flexibly is the key to a high-performance project and achieving project goals in minimal time. Proving the architecture in code is the only proof that the architecture works. Processes such as Unified Process (UP) and Disciplined Agile Delivery (DAD) suggest that you are building a working framework for your system.
The leadership and project team should strike the optimal balance by laying the necessary foundation in the BI environment. This will eliminate rework and thus ensure prompt delivery
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To create an effective system, you need to take a usage-centric approach to building something that people will use and recommend for others to use. You need to understand what people want to see and must achieve their business goals.
The system must focus on use cases and use scenarios, not driven by data models. Data is an essential part of the system, but it should not be the focus of the system, otherwise the system will become obsolete and unused.
Projects should be planned based on prioritized requirements. However, it should not be based on technical issues. In each iteration, you should perform work that meets the highest-priority stakeholder requirements. This requirement will be met in that iteration.
In this way, we are always in a position where we are getting the most out of our stakeholders’ investments by achieving maximum benefits thereby minimizing risks.
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Each stakeholder should be involved in the project and should participate in every decision in the model. This will reduce rework when a stakeholder feels like the system creation has not fully met his needs. It is important that they participate daily in the progress of the system.
Stakeholders can easily change their minds as the project progresses and this input is included in the project.
As mentioned earlier, change is common throughout the project and is unavoidable in most cases. The team should be prepared for changing requirements and put in place mechanisms to ensure a more flexible approach to change management. This requires the development team to adapt development techniques such as database regression testing, database refactoring, and evolutionary data modeling.
A quick and efficient way to design, prototype, and build is through the vertical slice. A vertical slice is a fully implemented, top-to-bottom functionality that can provide some value to the end user. This approach helps to circumvent the collection and serialization of heavy, low-value requests that hinder the waterfall approach. For an agile team, one iteration produces a tested and proven vertical slide. Most importantly, this vertical slide can be easily implemented on demand.
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The timeframes should be time-framed to provide an opportunity to get needed feedback from stakeholders on the project. One repetition should not exceed the time limit in a two-week period. For teams that are following the lean discovery lifecycle, this often shortens to days and sometimes hours with proper planning and slicing in progress. Short repetitions motivate to focus on high-value activities while long repetitions create distraction.
Checking for completed slices in iterations is important for database code. It’s not uncommon to see a flexible project doing more than testing throughout the life of the project. Taking a test-first approach is advised during development. In a nutshell, database regression testing is crucial to your success. This will require you to be able to have access to primary legacy data sources, active extract transform load strategies, and reporting tools that may have access to the DW.
Heavy documentation is essential for stakeholder communication purposes. And also for reference in organizational plans.
To create a model that meets stakeholder specifics, you’ll need to take an evolutionary approach to development. Set the required architecture and requirements for project start but just-in-time (JIT) system modeling.
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Developing Business Intelligence is a complex task and it becomes more complicated without using the right tools for the job. In addition to the standard tools applied in data modeling, you may also need tools that support evolutionary development techniques. All these versatile tools are available in the software market for purchase.
Align the team’s approach to the customs and culture of the organization. You should follow your organization’s general development guidelines. This will set the tone for the entire project duration and create consistency.
Consistency is an important quality during development as it ensures developers have regular input to the model. This includes naming conventions, pattern guidelines, report design guidelines, and more.
Data governance should be approached in such a way that data either doesn’t respond too slowly, is too hard to work with, or the data pool doesn’t provide enough value to justify its use. The development team should choose to work with responsive data in practice rather than on paper, with lean data management.
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Legacy data is often more of a mess than it isn’t. The team will be forced to clean the data sources so that they are ready for use in the system. Data cleaning should be seen as a necessary process to create a better database
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