Role of Big Data Testing for Driving Great Customer Experience

Role of Big Data Testing for Driving Great Customer Experience

Today customer experience (CX) has emerged as one of the most crucial elements of competitive differentiation. With the most recent developments in big data analytics (BDA), marketers have been able to unlock various customer insights for customer experience management. To make it more precise, here we are incorporating two concepts BDA and CX to analyse how a perfect combination of these two can deliver the best results for your business. Many researchers have identified the huge gap that exists between these domains that requires identification of opportunities and direct management practices for future research that benefit the goals of an individual organisation.

The top motive of every other business, no matter the size, is to ensure a robust customer experience for their users. Tracking business success was a task a decade ago when marketers usually collected tons of data but had no clue how to leverage it into building effective strategies. But with the latest technologies like AI, ML, Big data and analytics, this entire data junk can now be easily extracted and converted into useful dashboards that can help the management get better business insights and efficient strategy building.

While it’s clear that big data and analytics play a major role in enhancing the ultimate experience of your customers, it’s essential to verify the sanctity of the data being processes as well. This is where big data testing services come in.

To work towards an improved customer experience, one must know how to handle effective data analysis, meaningful use of collected data and a holistic approach towards addressing the issues arising with data management.

Let’s take a deeper look into big data and how big data testing can help bridge this gap between Big Data Analytics and Customer experience optimisation.

What is Big Data?

Though obvious, for starter’s sake, Big Data is the field that renders ways to analyse, extract or manage datasets that are too large or complex to be dealt with by traditional data processing and application software. Its large volumes of data, both structured and unstructured that directly impacts a business’s day to day activities.

While analysing, it’s what companies choose to do with such volumes of data that matters. Analysing this data and deriving better results for your business and aligning informed business moves is termed Big Data Analytics.

There are three primary formats of big data

  • Structured
  • Semi-structured
  • Unstructured

What is Big Data Testing?

The process of testing big data applications for appropriate quality checks is called big data testing. Its typically related to database testing, infrastructure and performance testing and functional testing. The techniques, tools and methodologies involved in big data testing are way different from our testing computing techniques because the data sets involved here are junk of large datasets that need to be cleaned and filtered first. Ideally, it means your big data testing strategy must include big data testing techniques, methodologies and a mix of big data automation tools. Despite having every updated technology, having a clear test strategy is one of the biggest success parameters for any software testing services provider.

 

Big Data Testing Benefits

The various advantages of big data testing are given below. Let’s have a look one by one.

1. Data Accuracy

Big data testing ensures that data being used in the process is accurate that could further be utilised for business planning, forecasting and better decision making. It’s necessary to ensure that this data is validated for its correctness before inputting it in any big data application.

2. Cost-Effective Data Storage

Every big data app requires multiple big data machines used in data storage. These data storage units come with a cost hence analysts need to ensure the data is thoroughly cleaned and tested before storing. The more unsorted or unstructured your data will be the more space it will take.

3. Enhanced Decision-Making

Big data testing helps ensure accuracy in data and hence help in effective decision making by the management. Decisions made on vague data compilation can turn out to be a disaster for enterprises. Big data testing helps filter and sort only those elements that are necessary to be included in the decision making.

4. Right Data Access

Various components involve big data testing. Any of these components can be a contributor to bad data performance while loading or processing information. Big data testing ensures the right kind of data is available at the right place and at the right time enabling better app performance. Ultimately it enhances customer services and boosts business revenues to a great extent.

 

Improving Digital Customer Experience with Big Data Analytics

Having looked at the various benefits of big data testing let’s move a step forward in understanding how big data testing and analysis can help improve digital customer experience. There are various touchpoints from where customer insights can be collected. These insights work like a complete biodata of their customers with data ranging from their name, age, d.o.b., to other sensitive details like their Salary bracket, purchase history, travel preferences etc. Big data analysis helps sort this entire data by processing it by using a set of techniques or programming models and subsequently extracts useful information for supporting and providing decisions.

Below mentioned are few sure shot ways to improve the digital customer experience of your customers.

  1. Workaround an effective buyer’s persona of your customer
  2. Understand their sentiments and buying behaviour to connect on an emotional level.
  3. Analyze their experiences through an open-ended framework.
  4. Enhance your targeted marketing practices by constantly updating and refining your data

Having looked at the various benefits of big data testing above let’s look into how big data can help improve the Consumer experience in a little more detail.

 

1. Personalization Through Right Data Access When Required

By delivering personalised experiences, we get better customer loyalty and revenue growth but can adversely wrong if not handled correctly. In cases when a customer has already shared some data and continuously asked to fill it again can result in a bad customer experience. Big data testing can ensure the right data is delivered where it’s required within an application to avoid such instances and enhance your overall customer delivery.

2. Better Responsiveness With Clear Data Integration

You need an extremely active and real-time information support for today’s hyperactive businesses. Big data testing ensure you deliver quality in the rapidly changing market environments and customer demands by ensuring you have real-time access to critical systems. Proper data integrity and movement of data across systems are thoroughly tested before releasing it on any software.

3. Predictive Insights Through Data Accuracy

One of the most essential elements of customer experience is the ability to predict a fall or rise in demand for a product before it actually happens. This gives businesses enough time to be well prepared and meet customer expectations. Making such prediction about the future requires a combination of data mining, statistical analysis, machine learning, and artificial intelligence.

With the constantly growing volumes of business data and increasing complexity, it becomes mandatory to ensure data accuracy throughout the system. Big data testing helps ensure this data accuracy and processes this data in a manner that helps get better insights into decision making.

4. Effective Supply-Chain Maintenance

We need to reward the automated IT and workload automation that help support the delivery of critical business services such as order fulfilment, credit card processes, stock replenishment, etc. The more automated these processes are, the better it is for any digital business to keep moving forward and seamlessly transport data across the ERP CRM and other business systems. Big data testing helps ensure all these activities are coordinated in an adaptable manner and ensure all automated functions are well integrated to avoid discrepancies.

 

What tests must be included in your Big Data Testing Strategy??

Below mentioned is a list of tests that are mostly included in every big data testing project by all major software testing companies makes before starting a project in hand.

Functional Testing: Functional testing service allow you to gauge the results produced by an application against its expected results as well as gain insights into its various components.

Performance Testing: Helps you to test your application under different conditions and volumes of data. It ensures the components involved in your big data application provides enough storage, processing and retrieval capabilities for large data sets.

Data Ingestion Testing: This helps you verify that all your data is extracted and loaded correctly within your big data application.

Data Processing Testing: helps test how your data is processed and validate if clear business logic is implemented throughout the system or not.

Data Storage Testing: Helps verify if the output data is correctly entered by comparing output data with the warehouse data.

Data Migration Testing: Helps validate the migration of data from the old system to the new system that experiences minimal downtime with no data loss.

 

Summing Up

As mentioned above, to ensure a robust customer experience, you require a comprehensive testing strategy. Big data analytics can help businesses derive better customer insights and hence require thorough big data testing best practices. We at ImpactQA leverage our experience of having tested a variety of data warehousing and business intelligence applications to offer a host of Big Data Testing services and solutions. Schedule a call with our big data testing experts and we’ll get in touch with you.

 

 

 

 

 

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