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AGILE AND BIG DATA – A MATCH WINNING COMBINATION

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AGILE-BIG-DATA

AGILE BIG DATA

AGILE BIG DATA-A business needs a result-based framework that is supported by tools and technologies to be able to draw adequate analysis of the data available for taking required strategic, tactical and operational decisions. A data analysis could be drawn from data found in OLAP reports, dashboards, or scorecards.

BI/Big Data analytics/predictive analytics/mining models provides adequate operational insights. These insights are crucial for decision-making and can have far-sighted implications on a business’ outcomes.

However, in a typical software industry, the general perception is that BI/Big Data typically works well with a waterfall or iteration model. These traditional models comprise a hierarchy, which begins with analysis, moves to design and development, and then ends at the deployment stage. But the issue is, most businesses don’t consider the risk, costs involved, and time required until the end of the project life cycle. So, if any correction needs to be incorporated, businesses have to wait till the end of a project.

Hence, Agile methodology is required to achieve higher success rate of BI projects.

 

Agile Methodology

Agile is a software development methodology to execute a software project incrementally and systematically within a fixed time frame so that businesses can see the benefits within a short period than waiting for a longer duration. The fun of using agile is, it offers periodical output so that the results can be validated, and corrective action can be taken. The traditional methods like iterative or waterfall start with analysis, design, development till the deployment takes place but the issue is, that business doesn’t realize any value till the end of the project life cycle. These methods are risky, costly and less efficient as the business must wait till the end of the project for any corrective action.

As mentioned above, Agile is a methodology or philosophy of an organization to develop a software solution incrementally using short cycles of 2 to 4 weeks so that the development process is aligned with the changing business needs. Instead of a big bang/single-pass development to deployment for several months, all the requirements and risks are discussed upfront. Agile follows a process or framework of frequent feedback where a workable product is delivered after 2 to 4 weeks of iteration.

What A Regular Project Flow Looks Like

This project begins with an analysis of various components, followed by an ETL strategy approach, then towards data model designing, ending at report development. These components from analysis to development/testing and deployment takes several months before the business can see any result or benefits. The impact of this project are:

•   Going back and changing the design post testing and before deployment is quite difficult

•   Studying if the product is in a working condition is not possible until the last day

•   Some of the risks remain unknown until the last day

•   Any delay hamper downstream applications

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Article Credit: Analytics Insight

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