Is Augmented Analytics the Future of Big Data Analytics?

Shaivi Chapalgaonkar | August 3, 2021 | 208 views

We currently live in the age of data. It’s not just any kind of data, but big data. The current data sets have become huge, complicated, and quick, making it difficult for traditional business intelligence (BI) solutions to handle. These dated BI solutions are either unable to get the data, deal with the data, or understand the data. It is vital to handle the data aptly since data is everywhere and is being produced constantly.

Your organization needs to discover any hidden insights in your datasets. Going through all the data will be doable with the right tools like machine learning (ML) and augmented analytics.

According to Gartner, augmented analytics is the future of data analytics and defines it as:

“Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.”

Augmented analytics is different from BI tools because ML technologies work behind the scenes continuously to learn and enhance results. Augmented analytics facilitates this process faster to derive insights from large amounts of structured and unstructured data to gain ML-based recommendations. In addition, it helps to find patterns in the data that usually go unnoticed, removes human bias, and allows predictive capabilities to inform an organization of what to do next.
 
Artificial intelligence has brought about an augmented analytics trend, and there has been a significant increase in the demand for augmented analytics.

Benefits of Augmented Analytics

Organizations now understand the benefits of augmented analytics which has led them to adopt it to deal with the increasing volume of structured and unstructured data. Oracle identified top four reasons organizations are opting for augmented analytics:

Data Democratization

Augmented data science availability to everyone has become a possibility thanks to augmented analytics. Augmented analytics solutions come prebuilt with models and algorithms, so data scientists are not needed to do this work. In addition, these augmented analytics models have user-friendly interfaces, making it easier for business users and executives to use them.

Quicker Decision-making

You will receive suggestions and recommendations through augmented analytics about which datasets to incorporate in analyses, alert users with dataset upgrades, and recommend new datasets when the results are not what the users expect. With just one click, augmented analytics provides precise forecasts and predictions on historical data.

Programmed Recommendations

Natural language processing (NLP) is featured on the augmented analytics platforms enabling non-technical users to question the source data easily. Interpreting the complex data into text with intelligent recommendations is automated by natural language generation (NLG), thus speeding up the analytic insights. Anyone using the tools can find out hidden patterns and predict trends to optimize the time it takes to go from data to insights to decisions using automated recommendations for data improvement and visualization. Non-expert users can use NLP technology to make sense of large amounts of data. Users can ask doubts about data using typical business terms. The software will find and question the correct data, making the results easy to digest using visualization tools or natural language output.

Grow into a Data-driven Company

It is more significant to understand data and business while organizations are rapidly adjusting to changes. Analytics has become more critical to doing everything from understanding sales trends, to segment customers, based on their online behaviors, and predicting how much inventory to hold to strategizing marketing campaigns. Analytics is what makes data a valuable asset.

Essential Capabilities of Augmented Analytics

Augmented analytics reduces the repetitive processes data analysts need to do every time they work with new datasets. It helps to decrease the time it takes to clean data through the ETL process. Augmented analytics allows more time to think about the data implications, discover patterns, auto-generated code, create visualizations, and propose recommendations from the insights it derives.

Augmented analytics considers intents and behaviors and turns them into contextual insights. It presents new directions to look at data and identify patterns and insights companies would have otherwise missed out on completely- thus altering the way analytics is used. The ability to highlight the most relevant hidden insights is a powerful capability.

Augmented analytics, for example, can help users manage the context at the explanatory process stage. It understands the values of data that are associated with or unrelated to that context, which results in powerful and relevant suggestions that are context-aware.

Modern self-service BI tools have a friendly user interface that enables business users with low to no technical skills to derive insights from data in real-time. In addition, these tools can easily handle large datasets from various sources in a quickly and competently.

The insights from augmented analytics tools can tell you what, why, and how something happened. In addition, it can reveal important insights, recommendations, and relationships between data points in real-time and present it to the user in the form of reports in conversational language.

Users can have data queries to get insights through the augmented analytics tools. For example, business users can ask, “How was the company’s performance last year?” or “What was the most profitable quarter of the year?” The systems provide in-depth explanations and recommendations around data insights, clearly understanding the “what” and the “why” of the data.

It enhances efficiency, decision-making, and collaboration between users and encourages data literacy and data democracy throughout an organization.

Augmented Analytics: What’s Next?

Augmented analytics is going to change the way people understand and examine data. It has become a necessity for businesses to survive. It will simplify and speed up the augmented data preparation, cleansing, and standardization of data, thus assist businesses to focus all their efforts on data analysis.

BI and analytics will become an immersive environment with integrations allowing users to interact with their data. New insights and data will be easier to access through various devices and interfaces like mobile phones, virtual assistants, or chatbots. In addition, it will help decision-making by notifying the users of alerts that need immediate attention. This will help businesses to stay updated about any changes happening in real-time.

Frequently Asked Questions

What are the benefits of augmented analytics?

Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs.

How important is augmented analytics?

Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors.

What are the examples of augmented analytics?

Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics.

Spotlight

Videology, Inc.

Videology (videologygroup.com) is a leading software provider for converged TV and video advertising. By simplifying big data, we empower marketers and media companies to make smarter advertising decisions to fully harness the value of their audience across screens. Our math and science-based technology enables our customers to manage, measure and optimize digital video and TV advertising to achieve the best results in the converging media landscape.

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Opaque Systems | December 08, 2022

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The Opaque platform advancements come on the heels of Opaque announcing its $22M Series A funding, Confidential Computing – projected to be a $54B market by 2026 by the Everest Group – provides a solution using TEEs or 'enclaves' that encrypt data during computation, isolating it from access, exposure and threats. However, TEEs have historically been challenging for data scientists due to the restricted access to data, lack of tools that enable data sharing and collaborative analytics, and the highly specialized skills needed to work with data encrypted in TEEs. The Opaque Platform overcomes these challenges by providing the first multi-party confidential analytics and AI solution that makes it possible to run frictionless analytics on encrypted data within TEEs, enable secure data sharing, and for the first time, enable multiple parties to perform collaborative analytics while ensuring each party only has access to the data they own. 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The Opaque Confidential AI and Analytics Platform is designed to specifically ensure that both code and data within enclaves are inaccessible to other users or processes that are collocated on the system. Organizations can encrypt their confidential data on-premises, accelerate the transition of sensitive workloads to enclaves in Confidential Computing Clouds, and analyze encrypted data while ensuring it is never unencrypted during the lifecycle of the computation. Key capabilities and advancements include: Secure, Multi-Party Collaborative Analytics – Multiple data owners can pool their encrypted data together in the cloud, and jointly analyze the collective data without compromising confidentiality. Policy enforcement capabilities ensure the data owned by each party is never exposed to other data owners. Secure Data Sharing and Data Privacy – Teams across departments and across organizations can securely share data protected in TEEs while adhering to regulatory and compliance policies. Use cases requiring confidential data sharing include financial crime, drug research, ad targeting monetization and more. Data Protection Throughout the Lifecycle – Protects all sensitive data, including PII and SHI data, using advanced encryption and secure hardware enclave technology, throughout the lifecycle of computation—from data upload, to analytics and insights. Multi-tiered Security, Policy Enforcement, and Governance – Leverages multiple layers of security, including Intel® Software Guard Extensions, secure enclaves, advanced cryptography and policy enforcement to provide defense in depth, ensuring code integrity, data, and side-channel attack protection. Scalability and Orchestration of Enclave Clusters – Provides distributed confidential data processing across managed TEE clusters and automates orchestration of clusters overcoming performance and scaling challenges and supports secure inter-enclave communication. Confidential Computing is supported by all major cloud vendors including Microsoft Azure, Google Cloud and Amazon Web Services and major chip manufacturers including Intel and AMD. About Opaque Systems: Commercializing the open source MC2 technology invented at UC Berkeley by its founders, Opaque System provides the first collaborative analytics and AI platform for Confidential Computing. Opaque uniquely enables data to be securely shared and analyzed by multiple parties while maintaining complete confidentiality and protecting data end-to-end. The Opaque Platform leverages a novel combination of two key technologies layered on top of state-of-the-art cloud security—secure hardware enclaves and cryptographic fortification. 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Veritonic | December 09, 2022

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Infleqtion Unveils SupercheQ, a Quantum Advantage for Distributed Databases

Infleqtion | December 09, 2022

Infleqtion, the global quantum ecosystem leader, today unveiled SupercheQ: Quantum Advantage for Distributed Databases, a scientific advance that extends the power of quantum computation to new applications involving distributed data. The emergence of commercial quantum hardware has been accompanied by new approaches to benchmarking quantum computers. In addition to application-centric benchmarking approaches such as Infleqtion's SupermarQ suite, scientists have developed benchmarks based on sampling from random quantum circuits. These benchmarks, including Quantum Volume, have enabled effective cross-platform comparisons, but until now have been disconnected from specific applications of quantum computers. The launch of SupercheQ changes this by endowing these random circuit sampling experiments with their first application. "SupercheQ achieves an exponential advantage for one of the most fundamental tasks in distributed computing: checking if two files are identical. "We leveraged recent advances in quantum information theory to show that the same families of circuits behind quantum volume can be used to realize this advantage." Pranav Gokhale, Vice President of Quantum Software at Infleqtion Gokhale will present SupercheQ on December 8th at Q2B in Santa Clara. Q2B, the world's largest non-academic quantum industry conference, brings together over 1,000 attendees from commercial companies and research institutions from around the world. SupercheQ has been experimentally validated by execution on superconducting quantum hardware from IBM Quantum, which also pioneered the invention of the Quantum Volume benchmarking metric. "The development of SupercheQ is an exciting step forward that starts to connect the dots between quality as measured by Quantum Volume to applications," said Jay Gambetta, IBM Fellow and Vice President of IBM Quantum. "The experimental validation on IBM Quantum hardware demonstrates the need for reliable and available hardware to advance quantum and build this industry together." In addition to the experimental validation on quantum hardware, the team performed large-scale simulations by leveraging NVIDIA GPUs, as well as the cuQuantum software development kit. "The launch of SupercheQ expands the possibilities of the tasks and types of data that can be addressed by a quantum computer," said Tim Costa, Director of HPC and Quantum Computing Products at NVIDIA. "The team's use of NVIDIA GPUs and cuQuantum has enabled them to validate SupercheQ's practical value at the scale of future quantum computers with hundreds of qubits." SupercheQ has been integrated into SuperstaQ, Infleqtion's flagship cloud quantum software platform, and the technical details behind SupercheQ have now been released in an academic paper, "SupercheQ: Quantum Advantage for Distributed Databases." Customers can get started today with a qiskit-superstaq tutorial notebook. SupercheQ is also in a private release as a new benchmark available in the SupermarQ suite. The development of SupercheQ—which requires both quantum computers and quantum networks—originates in Infleqtion's platform approach, spanning multiple quantum technologies. "We believe that the greatest advances in quantum will arise at the intersection of quantum computing, sensing, networking, and clock technologies," said Paul Lipman, Infleqtion's President of Quantum Information Platforms. "SupercheQ is an exemplar of this approach." About Infleqtion Infleqtion is building an ecosystem of quantum technologies and commercial products for today, that will drive the company and the entire industry toward tomorrow. The company believes in taking quantum to its limit and leading from the edge. Infleqtion is built on 15 years of pioneering quantum research from ColdQuanta. Its scalable and versatile quantum technology is used by organizations around the globe and deployed by NASA on the International Space Station. Infleqtion is based in Boulder, CO, with offices in Chicago, IL; Madison, WI; Melbourne, AU and Oxford, UK.

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