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An Insight into Agile Analytics

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@saikumar_talariSaikumar Talari

Agile Analytics is structured on a set of guiding principles and core values. It is not a robust or prescriptive methodology; while it is a way of constructing data marts, data warehouse, analytics applications, and BI applications that aim at primary and consistent productivity of business value all through the development life-cycle. Practically, agile analytics has a set of highly disciplined techniques and practices, a few of which are tailored to enhance the unique (DW/BI) project needs found in your organization.

Agile Analytics comprises practices for the planning of a project, monitoring and management; for efficient collaboration with your management stakeholders and business customers; and the delivery team ensuring technical excellence.

Agile is considered a reserved word while describing a development style. It implies something particular. Unfortunately, “agile” once in a while gets misused as a moniker for forms that are slipshod, ad hoc and without regulation. Agile depends on rigor and discipline; although, it is not highly ceremonious or heavyweight process regardless of the efforts of a few methodologists to classify it with those trappings. Relatively, Agile stands at some place in the middle, with just enough flexibility and just enough structure.

Agile looks to be simple but not easy as its being said, explaining the truth that it is structured with a set of principles and values, but requires rigor and a degree of discipline to execute correctly. It is prominent to understand a few sets of characteristics that distinguish a true agile process from which they form to be too rigid or too unstructured.

Besides, Agile Analytics is not a prescriptive methodology, but a development style that instructs you precisely what and how you need to do it. The performance of each activity within each enterprise requires implementations that can be trimmed accordingly to the environment. Remember, the initial objective is a high-value, high-quality, working DW/BI system. These components surpass that goal:

  • Iterative, incremental, evolutionary
  • Value-driven development
  • Production quality
  • Barely sufficient processes
  • Automation, automation, automation
  • Collaboration
  • Self-organizing, self-managing teams

The difficulties involved in Agile Analytics

Applying Agile strategies to DW/BI isn’t without challenges. Unfortunately, a few practices and tools used to custom develop programming in languages like Java, C++, or C# don’t always efficiently transfer to system integration utilizing proprietary technologies such as Oracle, Informatica, Cognos, and others. Among the issues that make Agile hard to apply to DW/BI development are the following:

Tool support

There aren’t numerous instruments that help specific practices, for example, ETL development or test-driven database, data warehouse build automation, database refactoring, and others that are introduced. The tools present are less developed than the ones used for development of software. Although, this present condition of tool support keeps on doing better, through both commercial as well as open-source tools.

Data volume

It needs innovative ideas to use lightweight development practices to develop BI systems and high-volume data warehouses. The implementation of small, representative data samples is required to rapidly test and build our work, while consistently ensuring that our designs collaborate with production data volumes. This is a higher amount of an obstacle to our method for moving towards the issue instead of a barrier that is intrinsic in the domain issue. Impediments are those difficulties that can be destroyed or worked around; obstructions are unrealistic.

Heavy lifting

Although Agile Analytics refers to a feature-driven (features of BI) approach, the back-end data marts or data warehouse can be the most delayed aspects of building DW/BI systems. In the initial stages of this project, it may look like it consumes a lot of “heavy lifting” on the back end only to present a nearly simple BI feature on the front end.

Continuous deployment

The potential to deploy new features into production regularly is an objective of agile advancement. This advancement is delayed by DW/BI systems which are previously in output with vast volumes of data. Periodically improving a production data warehouse (DW) with a fundamental data model revision can be in need of careful execution and significant time. Constant deployment seems to be difficult in DW/BI while comparing with software development.

Conclusion

Agile Analytics teams develop the best framework design by persistently adapting and enhancing to suggest form the enterprise group. Agile Analytics adjusts the perfect amount of formality and structure towards an adequate measure of adaptability, with a continuous aim of building the right solution.

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