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Top 18 Agile Business Intelligence Best Practices
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Top 18 Agile Business Intelligence Best Practices

Top 18 Agile Business Intelligence Best Practices
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With the adaptation of Agile Business Intelligence, some practices place you ahead and increase your success probabilities. Successful implementations of the BI with little or no challenges usually take an agile approach and better yet an evolutionary approach. The companies that were pleased with the success that they achieved with Agile Business Intelligence engaged in several practices that ensured their success. These best practices explain ways a company can lower overall risk in the project while also increasing probabilities that the project will meet its intended needs to the end users. BI solutions have evolved beyond the traditional data warehouse and associated multidimensional online analytical processing structures (cubes, star schemas, and so forth) in many significant ways. Beyond traditional relational databases, they can now integrate and analyze data from a number of sources. Beyond the use of traditional business graphics (bar graphs and pie charts, for example), the use of dynamic and interactive business graphics, such as real-time dashboards and interactive charts, can improve the ability for solutions to support users in increasingly demanding tasks.

Top Agile Business Intelligence Best Practices

  1. Just in Time (JIT) Modeling

Just in time modeling is whereby a business model storm the details of the project throughout the project as it continues. The business doesn’t plan the details at the start of the project.  JIT is helpful due to several reasons; details are going to change throughout the project, this will allow for agility and flexibility and result to a high-performance project, second in JIT modeling, you would have a vast amount of knowledge from the beginning of the project. Third, modeling everything upfront would result in a waterfall effect and go to significant wastage.

  1. Determine the aggregate risks inside and outside the project

There exists a cause-effect relationship in a project. This happens when an action in the project unknowingly affects another part in the project or organization because the business did not adopt an efficient project control mechanism to minimize a project specific risk. The risk should be communicated with key stakeholders in the project and organization so that they can help formulate a level of risk mitigation across all parts of the organization.

  1. Involving Support people and operations

Support people and Operations are key stakeholders in the project. Their requirements are what is building the project up. And the sooner you involve them the sooner you find out on the vision of the development team. They might have pesky details they would desire to be implemented in the DW/BI project. Getting the knowledge of these details early enough would reduce the risk of dissatisfying the stakeholders in the project.

  1. Early Architecture envisioning

Doing some initial architecture will help your team set a potential vision in building the data warehouse. As Agile Model Driven Development (AMDD) suggests, a detailed, comprehensive model isn’t required upfront but only clearly set vision is important. This will be used as a recurrent reference for the team members during the project.  The vision could be simple in the form of a deployment diagram and a high level domain mode that entail the business entities and relationships between them.

  1. Architecture and Technology should both be agile

Appropriate architecture and technology implemented in an agile manner is the key to a high-performance project and achieving the goals of the project in minimal time. Proving the architecture with 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 skeleton of your system. The leadership and the project team should set an optimum balance by laying foundations that are needed in a BI environment. This will eliminate rework and hence ensure rapid delivery

  1. Focus on Usage

To create an effective system, you need to apply a usage-centered approach to build something that people would use and recommend for others to use.  You need to understand what people would like to see and have to achieve their business objectives.  The system should be centered on use cases and usage scenarios and not driven by data models.  Data is an essential part of the system, but it should not be the focus of the system, or else the system would end up being obsolete and unused.

  1. Work Organization Based on Requirements.

The project should be planned based on requirements that are priorities. It should not be however be based on technical issues. In each iteration, you should do the work that satisfies the utmost priority stakeholder requirement which should be satisfied in that iteration.  In this manner, we are in always in a position where we are making the most out of the stakeholder’s investment by achieving maximum benefit thereby lowering the risk.

  1. Active Stakeholder participation

Each stakeholder should partake in the project and should be involved in each decision in the modeling. This will reduce rework when one stakeholder feels like the system creation hasn’t fully satisfied his needs. It is critical that they should be involved daily in the system’s progress. A stakeholder may easily change their minds as the project is ongoing and this input is adopted into the project.

  1. Embrace Change

As mentioned earlier, change is prevalent all throughout the project and it is unavoidable in most cases. The team should be prepared for changing requirements and adopt mechanisms to certain a more agile approach to change management.  This requires the development team to adapt development technique such as database regression testing, database refactoring and evolutionary data modeling.

  1. Top to bottom approach

A rapid and effective way to blueprint, prototype and build is via the vertical slice.  A vertical slice is a fully implemented, top to bottom functionality that can offer some value to an end user. This approach helps break the heavy, low-value requirements gathering and serialization that beset waterfall approaches. For an agile team, an iteration should produce a vertical slide that is tested and has been proven functional. Most important this vertical slide should be easily possible put into deployment upon request.

  1. Iterations should be time conscious

The periods should be time boxed in order to offer the chance for feedback that is essential from the stakeholders to the project. A single iteration should not surpass a time limit of a two week period. For teams that are following a lean discovery lifecycle, this period often shrinks to days and sometimes to hours with proper planning and slice being worked on. Short iterations provide motivation to focus on high value activities while stretched iterations allows for distractions.

  1. Test throughout the Project.

Testing the slices completed in the iterations is important to the database code.  It is not rare to see an agile projects do more than testing throughout the project lifecycle. Taking a test-first approach is advised in the development. In short database regression testing is critical to your success. This will require that you can have access to the main legacy data sources, that the extract-transform load strategy functions and that the reporting tools can gain access to the DW.

  1. Documentation

Heavy documentation is essential for communication purposes among the stakeholders. And also for reference in the organization plans.

Top Agile Business Intelligence Best Practices

Top Agile Business Intelligence Best Practices

  1. Take an evolutionary approach

To create a model that will meet the specifics of the stakeholders you will need to take an evolutionary approach to development.  Set the requirements and architecture needed to begin the project but model the system just in time (JIT)

  1. Use good tools.

Business Intelligence development is a complex task and it would be made more complex if the right tools for the job are not used.  In addition for the standard tools applied in data modeling, you might also need tools that support evolutionary development techniques.  All these agile tools are readily available at the software marketplace for purchase.

  1. Adopt organizational development standards

Match the approach of the team to the customs and culture of the organization. You should follow your organizations common development guidelines. This will set the tone for the whole project period and create consistency. Consistency is an important quality in development as it ensures the developers have regular input into the model. This includes naming conventions, modeling style guidelines, report design guidelines and so on.

  1. Adopt a lean approach to data governance

Data governance should be approached in such a way that the data will not be too slow to respond, too difficult to work with or that the data group does not provide sufficient value to justify its use. The development team should opt to work with data that is responsive in practice and not on paper, the lean data management.

  1. Planning for legacy data challenges

Legacy data is more often a mess than not.  The team will be forced to cleanse the data sources in order for them to be ready for use in the system. Data cleansing should be viewed as a necessary process which would result in a better database evolution for the legacy data source owners.  On paper, you will need to refactor the source to repair any data quality issue.

Conclusion

Some of the practices above are straightforward and well known, but are not well applied. They bear repeating and advocating by the team leaders. An integrated approach of all these practices will help your organization in their efforts to connect their information management initiatives with their overall business strategy. Others involve a keen understanding of balancing project, business and technology imperatives in a different way to achieve a far superior outcome.

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