#big data and analytics testing

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Although there are a lot of factors at play that determine the viability of a customer-focused strategy, big data analytics significantly increase the probability of a favorable outcome.

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How Big Data Testing Is The Ultimate Fuel For Business Success

The businesses of today operate in a competitive ecosystem where various conduits coming into their operational workflow can bring in data. These can comprise channels like social media, mobile, and cloud among others. If the entire data that streams into a business enterprise is captured and analyzed, it can yield significant business value. The value stream so generated can help improve speed, efficiency, agility, productivity, quality, and revenue. Big data analytics can help enterprises work faster, deliver better user experiences, comply with regulatory agencies and protocols, stay productive, take immediate decisions, and beat the competition.

How does big data analytics help

Business enterprises are sitting on a pile of data, which they can harness to draw meaningful inferences such as finding new opportunities. This can lead to developing efficient workflows, quicker and wiser business moves, better profits, and great customer experiences. The value addition to be accrued to business organizations is as follows:

· Reduction in cost: Technologies such as cloud computing and Hadoop can offer vast repositories for data and significant cost benefits. These can help companies to innovate and seek new efficient avenues of conducting business. It can identify redundancies or poor performing assets/services and stop the leaking sieve of revenue.

· Better decision making: Since big data analytics is done on real data (and historical ones), it allows enterprises to analyze the information quickly and take quick decisions. For example, eCommerce companies can find out products that are selling like hot cakes and others that are lying there without drawing any traction. Also, companies can gain insights into the changing dynamics of customer behaviour and find details such as bounce rates, problem areas, and performance glitches.

· Better products and services: As enterprises understand the needs of the customers and their pain areas, they can create new products or services that address the issues. In today’s world of competitive business, if a company is not aligned to the needs of the customers, it is bound to fail. This is where big data analytics, backed by big data testing, can help businesses in designing new products or services that are aligned to the customers’ needs.

· User experiences: This attribute ultimately decides whether the products or services brought out by a company is going to be accepted by the end users or not. It is a fact that competition has put the end customer in an enviable position. For he or she has plenty of options to choose from, be it the high-end cars, electronic gadgets, or even flights among others. However, should the performance of these suffers, the user experience would follow suit as well. This is where big data analytics, supported by big data and analytics testing, can help matters by identifying the defects and problem areas in the product or service and apply correctives.

Technologies enabling big data analytics

Big data analytics cannot function in a vacuum but needs to be supported by a slew of technologies:

· Machine learning: This subset of Artificial Intelligence (AI) lets machines to learn and quickly deliver prototypes to analyze bigger data patterns in a fast and accurate manner. By developing such models, enterprises can avoid taking risks and embrace profitable opportunities. However, big data testing helps such models to function seamlessly.

· Data mining: This technology helps businesses to analyse large volumes of data, especially their patterns and correlations to find solutions to complex business issues. Data mining can help enterprises to remove redundancies and repetitions in data and use the same to get positive outcomes that are aligned to business objectives. Furthermore, data mining technology can help businesses to accelerate their decision-making process. However, to ensure its successful functioning, data mining technology should pass through big data application testing.

· Hadoop: This open-source distributed data processing framework stores vast amounts of data in clustered systems. It is part of the big data technologies and is aimed at harnessing data mining, predictive analytics, and machine learning models. Hadoop possesses the ability to collect, process, and analyze structured or unstructured data better than data warehouses and relational databases.

·  Predictive analytics: This technology leverages machine learning techniques, statistical algorithms, and historical data to predict future outcomes. Predictive analytics can give insights into future scenarios that are likely to happen based on current business decisions. This strengthens business decision making, especially in businesses like insurance or activities like fraud detection.

As companies garner big data from multiple channels like the cloud, mobile, and social media, it becomes a herculean task to store, process, and mine data. However, to stay competitive and draw positive inferences for businesses, big data and analytics testing should be undertaken to harness the potential of big data that is generated through business applications.

So, how does big data testing benefits business enterprises?

The various benefits accrued by big data testing are as follows:

·  Data accuracy: Most data generated through omnichannels is unstructured. Since such data is generally unusable when using traditional BI or data warehousing tools, big data testing helps in the mining of such data, while ensuring its accuracy.

· Better strategizing: Businesses can collect a vast amount of data on user behaviour and draw insights. These can be used to deliver better personalized experiences to the target users. This is where big data testing of applications can help businesses to strategize better and derive positive outcomes.

· Increased ROI: Unstructured data sans processing can become useless unless big data testing turns it into a valuable asset by sifting good data from the bad. It helps businesses to deliver better user experiences, strategize better, and achieve high ROI.

Conclusion

Big data has become an important cog in the wheel for businesses when it comes to executing a host of business activities like strategizing, delivering services, improving productivity, and reducing waste. However, these can only be made possible by using a big data automation testing framework.

Diya works for Cigniti Technologies, Global Leaders in Independent Quality Engineering & Software Testing Services to be appraised at CMMI-SVC v1.3, Maturity Level 5, and is also ISO 9001:2015 & ISO 27001:2013 certified.

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This blog discusses the Big data and analytics testing are the fuel that keeps a content recommendation engine running for a digitally-evolving media and entertainment industry.

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Big Data to the Rescue of the Banking and Financial Sector

Digital transformation, though an enabler of increasing productivity, efficiency, and managing services, has challenges galore mostly in terms of a growing curve of cybercrime and the need to adhere to regulations. The banking and financial sector has been tasked with accessing, analyzing, and managing vast data volumes while it goes about improving efficiency and performance. Also, banks are increasingly focusing on revenue generation, risk management, and enhancing the customer experience, both in retail and business banking. The sector aims at increasing revenue – based on interests and fees. In recent times, the areas of operations for banks have expanded phenomenally – from the traditional retail banking to the higher portfolio of wealth management offering differentiated services. Managing internet based online banking services encompassing social media, mobility, ATMs, and digital wallets has necessitated the use of analytics and information management.

With the banking and financial sector embracing digitization in a big way, the amount of data swirling around has grown exponentially. In fact, apart from the quantum of data and the methodology to collect the same, its type has become even more complex. The data can emanate from sundry sources as mentioned below.

Customer touchpoints such as ATMs, mobile banking, branches, call centres, credit  and debit cards, loans etc.

For financial data, the sources can be the stock markets, news, regulatory agencies, analytics reports, industry, trade, and social media.

As the rate of data generation grows, business analysts have their tasks cut out. They want the growing volumes of data to be analyzed quickly and stored for a longer period. This is where big data solutions can come to the rescue of the banking and financial sector by offering a next generation data management architecture that is dynamic, swift, secure, and all encompassing. 

Big data applications to the rescue of the BFS sector

Infusing agility: As the level of competition increases with the entry of new players and the existing ones undergoing digitization, banks aim at enhancing the delivery of customer services. With customer experience becoming the differentiator as well as enabler of revenue generation, deploying big data management systems in managing data warehouses using Hadoop and/or NoSQL databases can garner better insights into data and drive better decision making. To ensure the seamless functioning of big data management system, emphasis should be accorded to big data testing.

Risk management: Traditional banking architectures have helped the sector to mitigate operational risks, manage credit, capital, and market liquidity, and meet the Basel norms quite effectively so far. However, as the sector goes into an overdrive to dispense credit, predicting the creditworthiness of individuals/businesses by analysing the loan application data has become critical. Moreover, with a growing number of NPAs turning the balance sheets of individual banks red, the focus is on the lack or near absence of due diligence exercised by banks and financial institutions. To gather a better insight into the creditworthiness of individuals/enterprises, big data solutions can leverage P2P payment data from mobile devices, mobile services data purchase, payment for utility services etc.

Also, banks can simulate various risk factors to derive better outcomes using big data technologies at low costs. Big data applications, on their part, can carry out predictive analysis to identify regions notorious for mortgage frauds. The heat maps so generated can help banks and financial institutions to zero in, both at the zip code and individual level, on habitual defaulters. Thus, new loan applications can be properly analyzed backed by correct property evaluation and occupation status. The analysis can help banks get a better insight into the customer’s ability to pay back the loan amount besides identifying opportunities for up-selling and cross-selling of banking products. The efficacy of big data solutions can only be ensured through big data and analytics testing.

Improving customer experience: The customer of today is likely to have multiple relationships with a number of banks. For example, they may have an account with a bank offering no fees followed by a bank with the highest interest on savings, or availing loan from a bank with the least EMI rate. Thus, successful banking products are replicated across banks with customers availing them based on a slew of factors such as the felicity of customer experience, transparency, cost of product etc. Given the competition, banks must ensure customers to stay with them for long. To enable this, banks must anticipate customer needs and preferences and design a product portfolio customized to their needs. No point in guessing that big data solutions can execute the steps anticipating customer needs. This calls for adopting a rigorous big data application testing to ensure the system delivers a seamless customer experience across multiple channels.

Conclusion

The growing footprint of data in the banking and financial sector needs the adoption of big data solutions to infer meaningful decisions. Since big data has the potential to enhance customer experiences while protecting the industry from frauds, big data testing should be made a part of the SDLC.

Diya works for Cigniti Technologies, which is the world’s first Independent Quality Engineering & Software Testing Services, to be appraised at CMMI-SVC v1.3, Maturity Level 5, and is also ISO 9001:2015 & ISO 27001:2013 certified.

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Big Data and Analytics brings tremendous business significance for enterprises, which makes Big Data Testing absolutely critical. Check our latest post to understand how it will help digital enterprises to derive maximum business value.