Article published by : Alisha Herson on Wednesday, November 22, 2017 - Viewed 241 times

Category : Software

Things To Be Consider For Big Data Application Testing



2015 is also known as the year of Big Data. Those leveraging big data are assured to rise ahead while those who really do not will fall behind.

Viewpoint Report says that “76% (of companies) are planning to expand or preserve their investment in Big Data over 3 – 4 years”. Data emerging from mobile, social networks, purchase histories, CRM records etc.

Provide organizations with valuable insights to open hidden patterns that can help companies chart their growth story. Simply, when we are thinking about data, we are talking about big volumes that amount to almost exabytes, petabytes, and sometimes even zettabytes.

Big data is the picture with a big bang. Nowadays, we are continually barraged with data. We like our data to be managed, this is exactly we want a relevant information. Still, with the overload of data from different resources, we are losing sight of structured data.

Analyst firm's new report says that International Data Group, 70 percent of companies have either expanded or are planning to expand, big data projects and programs in the coming years due to the increase in the amount of data they have to manage. Old databases are incomplete and will no more be around to hold such an avalanche of data.

Many companies are looking for methods to streamline difficulties and get more out of their data-related expenditures. At the same time, these organizations are understanding the value of the power of big data analytics testing and how it can be employed for growth and expansion.

Here’s a smart to-do list for the tester turned data scientist:

• Deploy adequate configuration management devices and procedures.

• Make sure right-time company of changes in reporting standards and demands. This calls for continuous collaboration and conversations with stakeholders. Compare the changes with the metadata model as well.

• Identify KPIs and a set of validation dashboards after careful analysis of algorithms and computational logic.

• Use prophetic analytics to figure out the client concerns and requirements. Derive patterns and learning mechanisms from drill-down charts and aggregate data. Make a sure correct behaviour of the data model by including alerts and analytics with predefined result data sets.

• Avoid the sampling approach. It may appear simple and clear but is not that easy. It’s better to plan load coverage at the outset and think deployment of automation devices to ingress data across different layers.

Big data analytics testing has very potentials to profit companies in any industry, everywhere across the world. Big data is much more than just a lot of data and particularly combining different data sets will give companies with real insights that can be used in the decision-making and to enhance the financial position of an industry. Before we can learn how big data can help your company, let's see what big data actually is.

It is usually accepted that big data can be explained by three V's: Velocity, Variety, and Volume. Though, I would like to add a few more V's to better describe the meaning and implications of a well thought through big data procedure.

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Keywords: Big data analytics testing, power of big data analytics testing

By: Alisha Herson

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