Business Intelligence Task Chances – When it comes to implementing and managing a successful BI strategy, we have always said: start small, use the right BI tools and involve your team. We know that the best approach is an iterative and flexible approach, regardless of the size of your company, industry or simply department. When we encourage these BI best practices, we are actually advocating for flexible business intelligence and analytics.
That said, in this article we will go through both agile analytics and BI, starting with the basic definitions, and moving on to methodologies, tips and tricks to help you implement these processes and give you a clear overview of how to use them . In our opinion, both terms, agile BI and agile analytics, are interchangeable and mean the same thing. That’s why we’ll walk you through this beginner’s guide to agile business intelligence and analytics to help you understand how they work and the methodology behind them. Without further ado, let’s get started.
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Agile analytics (or agile business intelligence) is a term used to describe software development methodologies used in BI and analytics processes to create flexibility, improve functionality, and adapt to new business requirements in BI and analytics projects.
It is necessary to say that these processes are recurring and require a continuous evolution of reports, online data visualization, dashboards and new functionalities to adapt current processes and develop new ones. Essentially, these processes are divided into smaller parts, but have the same purpose: to help companies, small businesses and large enterprises quickly adapt to business goals and ever-changing market conditions. To grow your business even further, we recommend reading our article on business software applications.
It is often the case that companies need to develop an agile BI methodology to successfully meet the demands of both strategic and operational developments. Whether you want to develop a comprehensive online data analysis process or reduce operational costs, agile BI development will certainly be high on your list of options to get the most out of your projects.
The term “agile” was originally coined in 2011 as a software development methodology. 17 software developers met to discuss lightweight development methods and then produced the following manifesto:
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Individuals and interaction over processes and tools Working software over comprehensive documentation Customer collaboration over contract negotiations Responding to change over following a plan
That is, while there is value in the items on the right, we value the items on the left more.
And so agile was born. As a software development methodology, agile is a time-boxed, iterative approach to software delivery that builds software incrementally, rather than attempting to deliver the entire product at the end. Thanks to the success of the methodology, Agile has successfully migrated beyond its original scope and is now being used successfully as a project management methodology in numerous industries. With an emphasis on adaptability over rigidity and collaboration over hierarchy, it’s easy to see why agile is becoming the methodology of choice for so many.
To look at these processes in more detail, we will now explain both agile BI methodology and analytics and provide steps for agile BI development.
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Business Intelligence deviates from the traditional engineering model: analysis, design, construction, testing and implementation. In the traditional model, communication between developers and business users is not a priority. Furthermore, developers are more focused on data and technology than on answering more important questions:
Through agile adoption, organizations see a faster return on their BI investments and can quickly adapt to changing business needs. To fully leverage agile business analytics, we will go through a basic agile framework related to BI implementation and management. You may find different versions of this, but the underlying methodology is the same. Let’s start with the concept.
This is the phase in which you start developing a separate BI vision. The agile BI implementation methodology starts with light documentation: you don’t need to map this out heavily. A whiteboard meeting will suffice, where you can explain the original architecture, consider the practicalities of project delivery, and determine the priorities between them. Details will be considered later. Therefore, concentrate on the concept and develop from there.
The initial phase is the critical initiation phase. This is when you first implement active stakeholder participation. You too:
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During construction, you deliver a working system that meets the changing needs of stakeholders. You progress through this phase continuously to phase 4 in fixed steps, usually over 1-3 weeks. Finally, after phases 3 and 4 are completed, you move to phase 5 (production). But before production, you need to develop documentation, perform test-driven design (TDD), and implement these important steps:
During this phase, you bring the previous construction iteration into production. You then return to the iteration and then return to the transition again to release those changes to production. During the transition:
These steps are critical in adopting agile in business intelligence and it is important to emphasize that while you should support your team in delivering value in a timely manner, you should not adhere to a ‘single truth’ as different departments have different ways and styles of approach. to work. After tinkering with the transition and iterations is complete, move on to the next step in BI and agile analytics development.
In production you operate and support everything that has emerged from the construction and transition iterations to production. During this phase:
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Essentially, production is the phase where you need to monitor the overall system, use a dashboard maker, and support the release.
These basic steps will help you put agile data analysis and BI methodology into practice, regardless of the size of your company. Always remember to focus on users and understand how people will likely use your BI system and achieve your business goals, both in the short and long term.
Now that you know the basic framework and how it works, we’ll shift our focus to additional tips to ensure you don’t miss any important part of successfully developing an agile analytics methodology and increasing the quality of final projects.
To ensure that your BI and agile data analytics methodologies are successfully implemented and deliver real business value, here are some additional tips that will ensure you stay on track and don’t forget a single key point in the process, starting with the stakeholders .
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It is so important that we emphasize it again. Stakeholder involvement is critical at every stage of your BI project. With agile, stakeholders and product owners experience team progress at regular intervals throughout the process, and greater stakeholder input means better overall business value. Stakeholders are crucial throughout the project and should be involved in most steps because you need regular feedback, whether it is the direct user in question, senior manager, employee, developer or program manager. Typically, you need to work closely with stakeholders to ultimately update the solution based on their feedback and a general understanding of what they really need. When dealing with stakeholders, remember to be flexible, educate senior management and understand their importance. That way you can save yourself a lot of potential bottlenecks in delivering the final project and results.
It’s a given: the requirements, or at least your understanding of them, will change throughout the life cycle of your project for a variety of reasons. To best develop a solution that meets stakeholder needs, take an evolutionary (iterative and incremental) development approach. Consider the need for methodological flexibility as every team is unique, different technologies require different techniques and there is no ‘one size fits all’ approach to agile methodology in data analytics and BI. It is possible to work with different teams, regardless of whether their focus is on data management or the implementation of agile business intelligence platforms. The important idea is that you must be willing to work in an evolutionary way and deliver your project incrementally over time, rather than in one big release. This concept may be new to both data professionals and traditional programmers, but it will certainly help in modern software processes.
This tip should be a favorite. While traditional methods take a lot of time to plan and write documentation, Agile relies on daily scrums and face-to-face interactions for team communication. By keeping documentation to a minimum, teams can quickly respond to project obstacles and eliminate redundancies. We’re not saying that we should lose documentation completely, but that we should only focus on what is necessary. Effective teams usually focus on activities such as developing reports rather than just documenting what you need to deliver at a given time. You measure your success by the delivery of the project, not by the amount of documentation you produce. Therefore, documentation should only be developed when necessary. It’s better to get regular feedback on the final product so you know what needs updating
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