Big data analytics is a great help in boosting the software development project

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An effective software development service gives reliability and peace of mind as well as cost benefit and flexibility to any business organisation. Software companies cater to clientele across industries and various sizes. The relationship is made of mutual trust, benefit and respect. It has become important for a software development organisation to stay updated on the current technologies and systems to be able to cater to the changing requirements of the clientele and to be more effective in the entire software development procedure.

Big Data in software development

Big data analytics software is described best as big volumes of data or huge chunks of data, which require special processes to be analysed and processed. Today is a world that’s data-driven. People all contribute to big data generation as huge data amounts are produced daily. Huge data refers to holistic data that’s produced in form of text messages, images, documents, videos, emails and more. Even a single individual creates massive data amount every day. Imagine this for a whole organisation that has hundreds of individuals.

Relevance of data analytics

The new benefits that a data analytics software brings to the table are efficiency and speed. This greatly helps in developing software solutions. It helps enterprises harness data and utilise it to determine new opportunities. This in turn leads to smart business moves, higher profits, and more efficient operations and of course happier clients. Technologies, such as the Hadoop and cloud-based analytics bring considerable cost savings in terms of storing big amounts of data, and they could identify more efficient business performance.

Use Big Data in boosting software development projects

These days, in keeping up with the evolving needs and technology, organisations invest in creating customised software that will integrate all data. One of the most relevant elements of data analytics approach to software development is gathering good data. A good place to begin with is one’s completed projects. One could input the project’s start data, the end data, the effort extended and peak staff and also any more metrics collected into SLIM-DataManager.  As present projects wind down, one could perform a postmortem on project completion wherein one could collect data on all interest metrics. In addition one could use the review tab to enter an accomplished project data for scheduling, the effort, the cost and growth size to calculate schedule overrun and slippage.                                                                                                                                                                                                                       

Extending agile development to Big Data

Software developers nowadays like to fail fast. They do not have the luxury of spending several months to develop and test a new app only to discover that it does not or no longer meets the business needs. This is something they need to find out as soon as possible and it needs agility. The data analysts, scientists and developers working with big data analytics have the same requirements. To succeed, IT organisations have to extend the concepts of agile software development to big data and Hadoop. They should allow data developers and scientists to get data and analytics they need as soon as possible.

Need for speed

Before, to create an enterprise-grade apps, numerous software development teams must independently work on the application components. When all individual building and testing is accomplished, the pieces are together tested and combined.  Usually there would be issues and these different pieces will go back to the development teams for rework and more testing among others and could occur numerous times. The app finally would be forwarded to the IT operations team for staging and deployment. Nevertheless, the processes could take weeks to months.                               

Today, these timeframes are no longer tenable, particularly with the mandate of today for faster business innovation, quicker response to market changes and faster development of new products. An agile environment is adaptive and promotes evolutionary development and continuous enhancement. Furthermore, it fosters flexibility as well as champions quick failures. Probably the most important is that it helps the development team of a software development organisation to create and deliver solutions as fast as possible.

Agile development provides an adaptive delivery approach

The practices and principles gathered under the agile umbrella all concentrate on validating assumptions as early as possible, significantly minimising risk exposure as the project continues. Each software engineering project piles up assumptions one after the other. The whole process of planning is based on an assumption that all will go as expected, even padding that’s added in planning is based on assumptions that that the padding is sufficient. The functionality that’s implemented is based on assumptions that functionality would provide the business value expected.

The architecture and design is based on an entire bunch of assumptions and each code line ultimately is written based on the assumption that code line does not have any bugs. Through work delivery in small increments of working, even production ready-software, the assumptions all are validated early on. All design, code, architecture and requirements and validated each time a new increment is delivered, even a plan’s validated as development teams acquire real and accurate data around the project’s progress.

Relevance of software architecture in Big Data systems

A lot of software systems, including big data apps lend themselves to highly iterative and incremental development approaches. System requirements in essence are addressed in small batches, allowing delivery of functional releases of the system towards the end of each increment, which is typically once a month. The benefits of this are numerous and varied. Foremost is the fact that it forces validation of requirements and designs constantly before a lot of progress is created in inappropriate directions. Change and ambiguity in requirements and uncertainty in design approaches, could be explored quickly via working with software systems, not just documents and models. Necessary modifications could be efficiently and cost-effectively carried out.

The nature of creating long-lived, highly scalable big data apps influences incremental and iterative design approaches. With big data, new infrastructure would enable software developers to build and deploy apps.

Dhrumit Shukla, Business Development Manager, TatvaSoft
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