The world of digital technology is always evolving. The smartphones, AI-based analytics, and Internet of Things are some of the biggest innovations of recent times. These technologies are driving massive digital transformation at all levels.
The massive growth in the use of the Internet, social media, and other platforms has seen unprecedented growth in data across the world. Most organizations, including those in the public and private sector, are now relying on Business Intelligence (BI) and data warehouse applications to assist them in making key data-based decisions.
Various data formats are organized and processed to help identify patterns for making future decisions. AI-based big data testing is already in use and making major strides in different domains. It is here to stay and evolve to enable futuristic digital transformation and ensure digital quality assurance.
The Covid-19 pandemic has brought about a major transformation in the way the global economy works. Businesses, employees, and even daily routines of people have been transforming to adapt to the new conditions created by the pandemic. The challenges that have arisen out of this changing environment have created a need for new technologies. Digital transformation is no longer just limited to creating a digital presence, but it is also about providing customized user experience (UX) to the target audience or end-users to meet their needs. Most of these organizations are now ready to consider and embrace new technologies, no matter what their current technological statuses are.
Embracing Big Data & AI
The habits and preferences of consumers or end-users are always changing. The growing volume of analytical data that businesses have to process continues to grow at a staggering rate. It is just too big for normal analytics applications to analyze. This is where artificial intelligence (AI) comes into the picture to help achieve digital quality assurance. AI and big data testing are considered the key tools that will be driving futuristic digital transformation.
AI & Its Role in Analyzing UX Data
Testing techniques need to keep improving in ways that take into account how users interact with a product. However, this has been a challenge for a long time. The most important reason being that it was difficult to record the ever-evolving market standards and needs. Traditional, human-based testing systems focused on the functionality of products, not the market perception. This is where AI-backed big data testing comes in. AI-based systems can learn and upgrade algorithms based on UX data.
The Connection Between AI & Big Data
AI testing requires a regular inflow of data to become successful. And this is possible only with big data analytics. This creates an element of interdependence between the two systems, contributing to digital quality assurance. The result is the creation of a self-sustaining system that is always evolving.
Why is AI the Right Approach for Big Data Testing?
AI applications have the processing power that makes them capable of processing large volumes of data.
- The human brain is incapable of matching the power of AI
- AI is incomprehensibly more accurate and faster
- AI has a much greater capacity for memory and storage compared to human testers
- AI can never miss out on critical components when testing products
- AI is capable of being automated
- Frequent testing can be carried out in much shorter time frames
- It helps in reducing overhead costs associated with testing
Organizations can fine-tune products before release. The result is greater quality products can be produced and the industry standards can be improved based on what is most suitable for the market.
Big Data Testing
Big data testing has, in itself, been a major stride in the digital transformation process. Find out how to adopt big data testing, how it can help improve digital quality assurance, and what is involved in it.
Adoption of Big Data Testing
Some of the key aspects of adopting big data testing include:
- Implementing Live Integration: It is crucial to implement live integration, as data can be received from multiple sources. It enables end-to-end testing.
- Data Validation: Data validation in the distributed file system. This includes comparing source data and added data.
- Process Validation: Once the data is compared, process validation involves:
- Business logic validation
- Data aggregation
- Data segregation
- Output Validation: This step focuses on the following:
- Eliminating data corruption
- Effective data loading
- Maintaining data integrity
The different ways in which a big data testing strategy can ensure digital quality assurance are as follows:
- Ensuring data accuracy
- Minimizing losses
- Increasing revenues
- Improving business decisions
- Improving market strategizing and targeting
How Does AI-Based Big Data Testing Help?
AI algorithms and big data testing can come together to help you in so many ways:
- Creating smart asset creation with data repositories
- Identifying relationships between application requirements and test assets
- Predicting incidents and responding in a proactive way
How Can AI Support Transformation in Testing?
AI can bring about transformation in testing and facilitate digital quality assurance in a number of ways:
i. Dynamic Analytics
It is well known that decision making is significantly affected by access to the right analytics tools and data. Machine learning (ML), a component of AI, is capable of processing tens of thousands of data points in real-time. ML allows you to develop models and predictive analytics capable of testing and retesting at a much faster rate compared to humans. Thus, the use of AI-based big data testing means the digital optimization process can become much more effective. It is more so the case with the use of automation in big data testing.
ii. Faster Processing
There are 2 areas where ML excels compared to human testers:
- Analyzing a much greater volume of data at a given time
- Ensuring a much higher quality of analysis
This means that AI-based big data testing is capable of streaming and expediting multiple digital processes and helping achieve digital quality assurance. ML is also capable of discovering a wide range of new relationships between the datasets. This can further help organizations in identifying new opportunities for revenue generation.
iii. AI Drives Economies at Scale
ML algorithms can learn from data and patterns at a much greater speed than humans or conventional applications. Thus, AI can help scale processes in ways that are simply not possible with traditional systems and teams. A model that can take months for data analysts can be processed by AI-based platforms within minutes or even seconds.
AI is capable of driving processes at scale and helps in making informed decisions. All these make it a crucial tool in the process of digital transformation. It is, however, important to ensure that the business use-case and the goal are clear. If business use cases are not clear, the situation can paralyze your organization despite technological expertise. Once this clarity has been ensured, AI and digital transformation can bring groundbreaking benefits.
How AI Enables Better Insights?
Combining big data with AI has created new needs around data preparation, infrastructure, and administration. In many scenarios, AI and ML technologies have become an integral part of how enterprises address different operational challenges.
There are different ways in which AI allows you to get better insights and ensure digital quality assurance:
i. New Data Analysis Techniques
Traditionally, deriving insights from big data was a major challenge. AI has created new techniques for big data analysis to help provide deeper insights. AI is the next higher step in the process of using query or SQL.
ii. Less Labor-Intensive Data Analytics
AI has made big data analysis and management much less labor-intensive and time-consuming. Human testers are still important for data management and analytics, but these processes should no longer take days or weeks to complete. AI is improving the world of data analytics with completely new capabilities by taking automated decisions based on training data. This is especially seen in the areas of specific use cases. It is revolutionizing how rules, predictions, and decisions are made without the need for sophisticated human know-how. In simple terms, AI-based big data testing allows insights and decisions to be made faster.
iii. Alleviating Common Data Issues
AI is digital quality assurance by addressing poor or low-quality issues in data. ML is capable of cleansing data. The algorithms can detect missing or outlier values, and duplicate records. If the big data is not clean, the insights derived from it cannot be relied on. This is where AI-based big data testing is helping organizations. When it comes to ML projects, most of the time is spent on the cleansing and preparation of data. This is a critical role that AI is playing and it is making a huge difference in enabling better business decisions.
Big data has been one of the biggest technological trends in recent years, revolutionizing not just organizations, but also societies. It is important for organizations to understand the importance of digital transformation in ways other than the technology itself. It is required to identify the right transformation goals while keeping in mind the ever-changing market and growth objectives.
It is important to ensure that the projects are not singular efforts but make contributions to a far-reaching strategy. Big data testing alone cannot help serve the purpose. It is the intelligent, AI-based big data testing models that can bring about true digital transformation through digital quality assurance.