A Primer to Augmented Analytics For the Beginners
With the evolution of smart technologies like artificial intelligence and machine learning, every industry, right from healthcare to manufacturing, defence, education, retail, construction, and almost every vertical, is found taking advantage of modern solutions. Augmented Analytics is also one of such emerging technologies that have redefined the entire idea of automating data manipulation, analysis, monitoring, and of course, data literacy. Since Augmented analytics has rapidly gained a huge reputation for adding value to the three core pillars of the analytics lifecycle, the entire process related to Preparation, Analysis, and Insight Delivery has jumped to another level. As per the reports shared by a popular research website called markets and markets, The Augmented Analytics Market size is expected to jump from USD 4.8 billion in 2018 to USD 18.4 billion by 2023, which means a Compound Annual Growth Rate (CAGR) of 30.6% during the forecast period. Nevertheless, there is still a huge group of IT enthusiasts and other industry operational experts who are not so familiar with the concept of Augmented Analytics. Since there are some benchmarks involved when it comes to implementing Augmented Analytics technology, this blog will help you understand the definition, benefits, challenges, and testing best practices involved with the process. Augmented Analytics: The Definition Augmented Analytics could be defined as a class of analytics that is powered by AI and ML technology, expanding human’s ability to process data at a contextual level. It is more of an assembly of software and tools that offers analytical capabilities related to guidance, insights, and recommendations on a particular type of query. As per Gartner, Augmented analytics is defined as the use of machine learning technology in collaboration with artificial intelligence to aim for data preparation, generating insights, and creating explanations on how people analyze data using analytics and Business Intelligence solutions. The Objective Of Using Augmented Analytics Augmented Analytics has proved to be a boon for business executives and data analytics experts, who need to generate value from data by taking deep and technical insights into business information. Augmented Analytics have helped businesses to create questions, track data, and have the most essential and profitable insights for their business profits. Though augmented analytics is meant to enable research and business enthusiasts to have analytical expertise, analysts and business developers could take advantage of the data fetched using Augmented Analytics to prepare business plans and act faster. Benefits of Augmented Analytics Augmented Analytics has the potential to help business analysts perform better with more efficient and accurate information. Especially when it is designed to run the best of Machine Learning and NLP technology, domain experts could overcome any technical barriers related to their business. Besides, it is one of those advanced techniques that has matured the data skills and has provided the necessary expertise to tackle a dynamically changing business environment. Some of the key benefits of using Augmented Analytics for business could be mentioned as: Accelerated Insights The first and foremost reason for adopting Augmented Analytics for business is all about aligning with the Agile business environment. Especially when businesses need to trim all the search time and have immediate access to the most relevant data, Augmented Analytics helps take the right information to the right place at the right time. It can track the user behavior to help experts personalize the action plan and tune it as per the user response from the past. This adds more rapidity to the information processing, making way for quicker and more appropriate strategy formed out of data insights. Precision At Its Best When any research or business needs access to a complete picture, Augmented Analytics could help work on all the calculations using AI and ML. Besides, it has the capability to run over any repetitive tasks to have the most precise data for making decisions. In short, an augmented analytics software meeting all the Quality Assurance benchmarks could help overcome any bias and have more decision-oriented conclusions. Efficiency Generated from Automation Since the use of AI and ML for Augmented Analytics is all about pairing the progressive algorithms to work on specialized tasks, the technology helps to save time and resources required to collect and process data with rapid reporting. This means a much efficient approach to data collection, data preparation, statistical analysis, and reporting. Confident Approach Towards Context Analysis Augmented technology is everything that helps to simplify things for humans. From modeling data to driving context-based information, Augmented Analytics uses ML and AI to mimic human instinct to drive valuable decisions. Since it is not feasible for the human mind to run through all the possible permutations and combinations when feeding on data, Augmented analytics help with productive findings. Besides, the technology paired with advanced Business Intelligence tools and practices could help establish an integrated workflow making people have advanced insights on business data. What Features Make Augmented Analytics So Unique? Since Augmented Analytics is more of a modern business approach, yielding maximum benefits with your data and decisions planning needs extensive focus on the system development and software testing practices. If done with precision, Augmented analytics could bring you an exclusive range of business inclusive features to offer: Automated Data Detection: Augmented Analytics technology implemented for business purposes could help with automated data detection for anything related to specific data attributes detection. Either formatting requirements or processing information, all could be easily worked using the Business Intelligence platforms using Augmented Analytics.   Statistical Operations: The next interesting feature that you can find with your Augmented Analytics system is the advantage of forecasting on data clusters with the help of statistical operational algorithms. This is something that can be automated in certain systems, while others could be manually instructed to underline specific sets of valuable data. In short, it does not need users to work on extensive calculations or have any coding expertise.  Intelligent Data Processing: Another interesting feature offered by BI systems using Augmented Analytics is related to data preparation with minimal cleanup requirements. Augmented analytics has the potential to help users work on indexing information without any manual intervention. This simplifies the whole task of updating fields and values, overcoming any null values making their way to data.  Advanced Recommendations: One of the most advanced features of using Augmented Analytics is AI-driven recommendations. These recommendations could help simplify the entire process of data preparation to analysis and reporting. Besides, AI and ML embedded into the system could help segment the analytical content for different user groups based on their roles and requirements.  NLP-based Query Processing: last but not least, Augmented Analytics brings the NLP capabilities to its users where plain language can be processed for data interpretation. Moreover, the system could even translate the text into queries filling the gap between intent and context of the information. Besides, Natural Language generation can help create textual descriptions for data along with data visualizations. This means an easier understanding of the data with quicker interpretations. The Challenges Related To Augmented Analytics The Myths Surrounding AI & ML Since AI and ML are highly complex technologies, most business enthusiasts believe it to be difficult interacting with Augmented Analytics systems. Besides, there are misconceptions related to AI taking over human jobs that have made people lose their trust in adopting such advanced technology. The Limitations of Augmented Analytics Though the expectations and outcomes associated with Augmented Analytics are very high, the inability to assess costs could make big investments sink. Therefore, it is necessary for users to understand the purpose and then implement the technology after careful consideration of Quality Assurance and testing. System Literacy Requirements Another important challenge associated with the use of Augmented Analytics is the system literacy requirements. This means users need to understand what type of data needs to be fed into the system to avoid waste of resources. Therefore, the system literacy challenge is more of a requirement that needs people with critical thinking to act on the system for maximum analytics proficiency. This would not only help to work on the confident consumption of data while ensuring correlation. Governance & Management The next big challenge that business experts and AI enthusiasts need to handle while working on Augmented Analytics based technology is management and governance. This is mainly related to the reliability of the data for added traction during deployment. The Ethics With AI technology turning to be more sophisticated than ever, it is critical that any use of information for research should be ethical. This means the use of non-biased models and process only data that adds value and can be trusted for significant decisions. Augmented Analytics Testing Best Practices Prepare A Solid Foundation Since Business Intelligence is made to upgrade business skills, it is necessary that your software testing journey should have a well-defined plan for development, security, and governance. As AI and ML are the extensions of the Augmented Analytics process, it is vital that your test plan and implementation environment should be defined on solid grounds, even if you plan a collaboration with a software testing company. Attain Scope Before Perfection Since Augmented Analytics is all about making bold attempts related to information using AI technology, the process should start with the definition of scope. This can be any use case or defined scope that must be attained before you aim for a broader approach to perfection. Educate Your Teams Since Augmented Analytics is a high-value system that is meant to process information for decisions involving extensive investment, it is vital that your test teams should be educated to build trust and meet data literacy objectives. Besides, the team working on development, implementation, and testing should have all the creative abilities required to judge the system for offering an easy-to-navigate experience for novice users. Nurturing Collaborations Last but not least, as AI-based systems need embedding features into the workflows, the testing process should involve effective communication between data and domain experts for system success. Right from the analysts working on dashboards to developers working on ML integrations should have clear communication within them as well as testers to meet the context requirements associated with the project. This would help lead solutions that are interactive and adaptive when it comes to data analysis and decision modeling. The Crux When we talk about Augmented Analytics, the technology has the scope to streamline business operations for the sales team, executives, IT experts, as well as business analysts. Moreover, other common use cases of the industry could be easily aligned with the industry operations associating supply chain, hospitality, marketing, and other essential components of the business. Therefore, it is mandatory that the process of developing and implementing the technology should undergo extensive automation software testing. After all, handling an intelligent system that imitates human instincts for any data fed to the system needs some intelligent approach with testing and operational process before it is set to fetch on high value and confidential business data. More importantly, having the right approach to the process could help businesses of any size to leverage technology like Augmented Analytics to scale their analytics programs for rapid identification of query and data patterns that could add the most value to their business as well as people behind the operations. Summary The blog discusses the right approach to leverage technology like Augmented Analytics while proceeding towards goals related to analytics programs for rapid query tracking or identifying data patterns that could add the most value to the future of humans.
With the evolution of smart technologies like artificial intelligence and machine learning, every industry, right from healthcare to manufacturing, defence, education, retail, construction, and almost every vertical, is found taking advantage of modern solutions.    Augmented Analytics is also one of such emerging technologies that have redefined the entire idea of automating data manipulation, analysis, monitoring, […]
With the evolution of smart technologies like artificial intelligence and machine learning, every industry, right from healthcare to manufacturing,
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["AI", "artificial intelligence", "machine learning", "ML", "NLP", "analytics", "Aim", "RAG", "R", "Go"]
Kanika Vatsyayan is the Vice-President, Delivery, and Operations at BugRaptors, one of the top QA &amp; software testing companies. She herself is a QA professional with a grip across several leadership positions such as test program planning, innovation, and process transformations. From quality control to test leadership, test practices, and assurance strategies, Kanika is a seasoned expert with influential tech skills. Besides this, she has a knack for writing and therefore has published countless articles and blogs educating audiences across the software testing industry