How Data Science Industry is changing - A view from 2022

VISHNU U | January 12, 2022 | 86 views

The industry of Data Science has been popular since a decade or so. The aim and workflow of the field has undergone a lot of changes since then. From basic reporting and analytics to predictive and cognitive analytics, data science revolutionized the concept of “Computers that can think”. As of today, Data Science and its subfields are one of the greatest in demand and has a great competition in the industry. Apart from improving businesses, Analytics has proved its capability in various sectors and applications. This has changed the overall structure of the field of Data Science and the opportunities available. The large amount of data and its wide scope comes with plenty of various opportunities and developments.

AI, AI everywhere
AI started as a boom few years back wherein it saw great potential, but now it’s everywhere. From Research Labs to Education, Healthcare and even in personal devices, AI has taken up various forms, solving many problems and improving various products and services. Even now, experts states that the full potential of AI is not completely utilised and is expected to be used in a few years’ time. The wide use of Artificial Intelligence has motivated many start-ups to focus on the use of AI to build solutions and products. Industries at all scales have taken a move to include AI in their services / products to increase efficiency through intelligent behaviour. The “Mimicabilty” of human brain functionality comes with great potential – Namely Automation and Optimization of various tasks.

Data Centric is the trend
With a large amount of data generated daily on servers, there comes a need of shifting from model-centric to data-centric. Let us have a glimpse into what each approach means. In model-centric approach, data is kept constant while the model is tweaked to adjust to the data and get a good result. What’s the drawback of this? Not performing good on real world data. But, then why the model gave good results? That is because, model completely got adjusted to the data you gave instead of generalizing to the real-world problem. This issue came in the recent past, thus the trend of Data-centric came in. Experts like Andrew Ng often hosts talks and campaigns to shift the focus on ML Practitioners and Industries to Data rather than model. According to him, “data is like food for the model”.

Data Engineers on Rise
As a trailing topic to the previous one, Data Engineers will have a good rise in the coming future. As seen before, there is a copious amount of existing data and is getting generated at an un-imaginable rate. This might sound good since, “more is good” for analytics, but comes with the disadvantage of difficulty in ensuring data quality. As more data comes in, maintaining quality can be challenging. One reality here is that not all times a cloud pipeline can be used to ensure data quality and automated cleaning. There are times where raw data is logged as it is. This calls for more data engineers to perform cleaning and tuning of data to ensure it meets quality standards. With the oncoming of Data-Centric AI approach, this will have a great hype in the coming recent time. And speaking from the Industry Point of View, Data is one of the core part of Data Science and has no replacement. No good data, no good results and the crash of Data Science.

“Artificial” becoming more on the point for AI
AI started as a research topic long back. It all started like a big-bang – Basic linear regression to Neural Networks.

The latest AI algorithms can now see like a human (Computer Vision), speak like a human (Natural Language Processing) and assist in decision making (Inferencing using Models). The newer models are much more advanced that AI seems to be – Natural Language Processing algorithms can now carry out tasks on speaking, predict a sentence and even tell what sentiment it has.

Computer Vision Algorithms are now assisting doctors to detect defects and diseases through X-rays and MRI images. This all points to the immense capability in the field of AI. In future it is predicted that setting up an AI system will be equivalent to setting up the mind of a human being – A situation called singularity.

More opportunities coming up
As seen previously, there are a lot of opportunities coming up in Data Science field. With its vast number of applications in every sector, there are a lot of openings coming up. Start-ups and developed industries are now shifting to AI solutions because of its dynamic nature and intelligent decision-making capability. The number of jobs in Data Science is expected to rise in the coming years. Especially Post COVID, organizations experienced a surge in various technologies, out of which Data Science is one of the major fields. Data Science and Artificial Intelligence is one of the most demanded fields for research to happen. Aspiring researchers have a good demand in the AI and ML field. Tech giants have marked their presence in improving AI algorithms, making the systems more efficient and “Intelligent”. Artificial General Intelligence is one of the applications of AI which is focused on varied problem solving rather than a restricted problem domain and is expected to bring a significant change in the AI focused problem solving.

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PeopleCloud

PeopleCloud is a products and services software company aimed at providing the next generation Cloud, Mobile, Web, and Desktop solutions to customers around the world. We specialize in building native mobile applications for iPhone, Android, Blackberry and Windows Mobile as well as develop using a variety of cross platform frameworks like Titanium and Phonegap.

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DATA SCIENCE

How is Data Virtualization Shifting the Tailwind in Data Management?

Article | March 31, 2022

Over the past couple of years, a significant rise in the trend of digitalization has been witnessed across almost all industries, resulting in the creation of large volumes of data. In addition, an unprecedented proliferation of applications and the rise in the use of social media, cloud and mobile computing, the Internet of Things, and others have created the need for collecting, combining, and curating massive amounts of data. As the importance of data continues to grow across businesses, companies aim to collect data from the web, social media, AI-powered devices, and other sources in different formats, making it trickier for them to manage this unstructured data. Hence, smarter companies are investing in innovative solutions, such as data virtualization, to access and modify data stored across siloed, disparate systems through a unified view. This helps them bridge critical decision-making data together, fuel analytics, and make strategic and well-informed decisions. Why is Data Virtualization Emerging as A New Frontier in Data Management? In the current competitive corporate world, where data needs are increasing at the same rate as the volume of data companies hold, it is becoming essential to manage and harness data effectively. As enterprises focus on accumulating multiple types of data, the effort of managing it has outgrown the capacity of traditional data integration tools, such as data warehouse software and Extract Transform Load (ETL) systems. With the growing need for more effective data integration solutions, high-speed information sharing, and non-stop data transmission, advanced tools such as data virtualization are gaining massive popularity among corporate firms and other IT infrastructures. Data virtualization empowers organizations to accumulate and integrate data from multiple channels, locations, sources, and formats to create a unified stream of data without any redundancy or overlap, resulting in faster integration speeds and enhanced decision-making. What are the key features that make data virtualization a new frontier in data management? Let's see: Modernize Information Infrastructure With the ability to hide the underlying systems, data virtualization allows companies to replace their old infrastructure with cutting-edge cloud applications without affecting day-to-day business operations. Enhance Data Protection Data virtualization enables CxOs to identify and isolate vital source systems from users and applications, which assists organizations in preventing the latter from making unintended changes to the data, as well as allowing them to enforce data governance and security. Deliver Information Faster and Cheaper Data replication takes time and costs money; the "zero replication" method used by data virtualization allows businesses to obtain up-to-the-minute information without having to invest in additional storage space, thereby saving on the operation cost. Increase Business Productivity By delivering data in real time, the integration of data virtualization empowers businesses to access the most recent data during regular business operations. In addition, it enhances the utilization of servers and storage resources and allows data engineering teams to do more in less time, thereby increasing productivity. Use Fewer Development Resources Data virtualization lowers the need for human coding, allowing developers to focus on the faster delivery of information at scale. With its simplified view-based methodology, data virtualization also enables CxOs to reduce development resources by around one-fourth. Data Virtualization: The Future Ahead With the growing significance of data across enterprises and increasing data volume, variety, complexity, compliance requirements, and others, every organization is looking for well-governed, consistent, and secure data that is easy to access and use. As data virtualization unifies and integrates the data from different systems, providing new ways to access, manage, and deliver data without replicating it, more and more organizations are investing in data virtualization software and solutions and driving greater business value from their data.

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DATA SCIENCE

How Artificial Intelligence Is Transforming Businesses

Article | February 16, 2022

Whilst there are many people that associate AI with sci-fi novels and films, its reputation as an antagonist to fictional dystopic worlds is now becoming a thing of the past, as the technology becomes more and more integrated into our everyday lives.AI technologies have become increasingly more present in our daily lives, not just with Alexa’s in the home, but also throughout businesses everywhere, disrupting a variety of different industries with often tremendous results. The technology has helped to streamline even the most mundane of tasks whilst having a breath-taking impact on a company’s efficiency and productivity.However, AI has not only transformed administrative processes and freed up more time for companies, it has also contributed to some ground-breaking moments in business, being a must-have for many in order to keep up with the competition.

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DATA SCIENCE

DRIVING DIGITAL TRANSFORMATION WITH RPA, ML AND WORKFLOW AUTOMATION

Article | January 29, 2022

The latest pace of advancements in technology paves way for businesses to pay attention to digital strategy in order to drive effective digital transformation. Digital strategy focuses on leveraging technology to enhance business performance, specifying the direction where organizations can create new competitive advantages with it. Despite a lot of buzz around its advancement, digital transformation initiatives in most businesses are still in its infancy.Organizations that have successfully implemented and are effectively navigating their way towards digital transformation have seen that deploying a low-code workflow automation platform makes them more efficient.

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AI and Predictive Analytics: Myth, Math, or Magic

Article | February 10, 2020

We are a species invested in predicting the future as if our lives depended on it. Indeed, good predictions of where wolves might lurk were once a matter of survival. Even as civilization made us physically safer, prediction has remained a mainstay of culture, from the haruspices of ancient Rome inspecting animal entrails to business analysts dissecting a wealth of transactions to foretell future sales. With these caveats in mind, I predict that in 2020 (and the decade ahead) we will struggle if we unquestioningly adopt artificial intelligence (AI) in predictive analytics, founded on an unjustified overconfidence in the almost mythical power of AI's mathematical foundations. This is another form of the disease of technochauvinism I discussed in a previous article.

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Spotlight

PeopleCloud

PeopleCloud is a products and services software company aimed at providing the next generation Cloud, Mobile, Web, and Desktop solutions to customers around the world. We specialize in building native mobile applications for iPhone, Android, Blackberry and Windows Mobile as well as develop using a variety of cross platform frameworks like Titanium and Phonegap.

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DATA VISUALIZATION

Opaque Systems, Pioneer in Confidential Computing, Unveils the First Multi-Party Confidential AI and Analytics Platform

Opaque Systems | December 08, 2022

Opaque Systems, the pioneers of secure multi-party analytics and AI for Confidential Computing, today announced the latest advancements in Confidential AI and Analytics with the unveiling of its platform. The Opaque platform, built to unlock use cases in Confidential Computing, is created by the inventors of the popular MC2 open source project which was conceived in the RISELab at UC Berkeley. The Opaque Platform uniquely enables data scientists within and across organizations to securely share data and perform collaborative analytics directly on encrypted data protected by Trusted Execution Environments (TEEs). The platform further accelerates Confidential Computing use cases by enabling data scientists to leverage their existing SQL and Python skills to run analytics and machine learning while working with confidential data, overcoming the data analytics challenges inherent in TEEs due to their strict protection of how data is accessed and used. 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. "Traditional approaches for protecting data and managing data privacy leave data exposed and at risk when being processed by applications, analytics, and machine learning (ML) models, The Opaque Confidential AI and Analytics Platform solves this challenge by enabling data scientists and analysts to perform scalable, secure analytics and machine learning directly on encrypted data within enclaves to unlock Confidential Computing use cases." -Rishabh Poddar, Co-founder & CEO, Opaque Systems. Strict privacy regulations result in sensitive data being difficult to access and analyze, said a Data Science Leader at a top US bank. New multi-party secure analytics and computational capabilities and Privacy Enhancing Technology from Opaque Systems will significantly improve the accuracy of AI/ML/NLP models and speed insights. 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. This combination ensures that the overall computation is secure, fast, and scalable. The MC2 technology and Opaque innovation has already been adopted by several organizations, such as Ant Group, IBM, Scotiabank, and Ericsson.

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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT

Veritonic Added to List of Acast’s Preferred Audio Attribution Partners

Veritonic | December 09, 2022

Veritonic, the industry’s comprehensive audio analytics and research platform, announced today that they have been approved as an attribution partner by Acast, the world’s largest independent podcast company. As a result, the more than 2,400 advertisers and 88,000 podcasters that use the Acast platform to distribute their podcast content can elect to utilize Veritonic’s robust attribution capabilities to optimize and further increase the ROI of their audio campaigns. “We are pleased to partner with Acast to support brands, agencies, and publishers with the holistic data and analytics they need to increase their reach and ROI with audio. "The powerful combination of our attribution and brand lift technology provides unparalleled and comprehensive measurement of audio campaigns from top to bottom in one unified and intuitive platform.” Scott Simonelli, chief executive officer of Veritonic "Veritonic shares our commitment to arming brands and agencies with actionable and insightful audio performance data,” said Kevin McCaul, Global Head of Ad Operations at Acast. “Our partnership is an important step for the open ecosystem of podcasting as we continue to work together to provide independent measurement insights to prove the effectiveness and efficiency of podcasting as a marketing channel.” Veritonic’s Attribution solution enables users to glean actionable insights from top-of-the-funnel branding initiatives through bottom-of-the-funnel conversions & transactions. Through an intuitive and interactive dashboard, brands can determine which publisher and specific ads had the highest impact and use that data to optimize ad performance. About Veritonic World-renowned brands, agencies, publishers, and platforms rely on Veritonic’s comprehensive audio research and analytics platform to research, test, and measure the ROI of their audio assets and campaigns pre-market, in-market, and post-campaign. The resulting insight enables clients to gain confidence in their audio investment, mitigate risk through optimization, and increase their return as they engage consumers with compelling audio experiences. About Acast Acast is the world’s largest independent podcast company. Founded in 2014, the company has pioneered the open podcast ecosystem ever since – making podcasts available on any listening platform. Acast provides a marketplace, helping podcasters find the right audience to monetize their content. When our podcasters make money, we make money. Today, Acast hosts nearly 88,000 podcasts, with more than 430 million listens every month.

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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Quantiphi announces partnership with Databricks to help drive enterprise-wide AI adoption

Quantiphi | November 15, 2022

Quantiphi, an AI-first digital engineering company, today announced its partnership with Databricks, the lakehouse company and pioneer of this new data paradigm. Together, Quantiphi and Databricks will focus on helping enterprise customers to optimize their business workflows with AI which will be enabled by a strong foundation of the Lakehouse platform. Businesses today are embracing multi-cloud and hybrid cloud environments. As a result, it has become imperative to modernize their data foundation for seamless operations across different environments, and deploy advanced cloud-based technologies – including analytics tools with advanced machine learning (ML) and MLOps capabilities. As Databricks Consulting Partners, Quantiphi will accelerate AI-driven innovation for clients across industries. Quantiphi's in-depth knowledge and deep expertise in helping enterprise customers modernize and democratize their Data and AI footprint at scale are valuable for the partnership with Databricks. Databricks' Lakehouse Platform is cloud-agnostic and enables users to unify their data warehousing and AI use cases on a consistent platform across multiple infrastructures simultaneously. "We are delighted to enter into a strategic partnership with Databricks. "By combining the power of Databricks' Lakehouse Platform and Quantiphi's advanced AI/ML capabilities, our teams will empower customers to leverage data-driven MLOps and enable enterprise AI success." Asif Hasan, Co-founder, Quantiphi The collaboration is set to actively support customers with various data and AI services such as digital transformation strategy, AI innovation roadmap, MLOps, data modernization and migration, data management, security, and governance implementations. Quantiphi's team of dedicated applied AI Databricks experts will further help customers implement and scale data engineering, collaborative data science, full-lifecycle machine learning, and business analytics initiatives. "Today, there is a rising need for every business to have a strong foundation of data and AI. By combining the strength of Databricks' Lakehouse Platform in data engineering, data science and analytics and Quantiphi's AI-first digital engineering capabilities, we can help companies transform their businesses through the power of data", said Mohak Moondra, Practice Leader - Applied AI, Quantiphi. About Quantiphi Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Quantiphi solves the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed. We are passionate about our customers and obsessed with problem-solving to make products smarter, customer experiences frictionless, processes autonomous and businesses safer by detecting risks, threats, and anomalies. Together with partners and customers, we embark on a data and AI-led transformation journey that delivers impactful and measurable results.

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DATA VISUALIZATION

Opaque Systems, Pioneer in Confidential Computing, Unveils the First Multi-Party Confidential AI and Analytics Platform

Opaque Systems | December 08, 2022

Opaque Systems, the pioneers of secure multi-party analytics and AI for Confidential Computing, today announced the latest advancements in Confidential AI and Analytics with the unveiling of its platform. The Opaque platform, built to unlock use cases in Confidential Computing, is created by the inventors of the popular MC2 open source project which was conceived in the RISELab at UC Berkeley. The Opaque Platform uniquely enables data scientists within and across organizations to securely share data and perform collaborative analytics directly on encrypted data protected by Trusted Execution Environments (TEEs). The platform further accelerates Confidential Computing use cases by enabling data scientists to leverage their existing SQL and Python skills to run analytics and machine learning while working with confidential data, overcoming the data analytics challenges inherent in TEEs due to their strict protection of how data is accessed and used. 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. "Traditional approaches for protecting data and managing data privacy leave data exposed and at risk when being processed by applications, analytics, and machine learning (ML) models, The Opaque Confidential AI and Analytics Platform solves this challenge by enabling data scientists and analysts to perform scalable, secure analytics and machine learning directly on encrypted data within enclaves to unlock Confidential Computing use cases." -Rishabh Poddar, Co-founder & CEO, Opaque Systems. Strict privacy regulations result in sensitive data being difficult to access and analyze, said a Data Science Leader at a top US bank. New multi-party secure analytics and computational capabilities and Privacy Enhancing Technology from Opaque Systems will significantly improve the accuracy of AI/ML/NLP models and speed insights. 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. This combination ensures that the overall computation is secure, fast, and scalable. The MC2 technology and Opaque innovation has already been adopted by several organizations, such as Ant Group, IBM, Scotiabank, and Ericsson.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT

Veritonic Added to List of Acast’s Preferred Audio Attribution Partners

Veritonic | December 09, 2022

Veritonic, the industry’s comprehensive audio analytics and research platform, announced today that they have been approved as an attribution partner by Acast, the world’s largest independent podcast company. As a result, the more than 2,400 advertisers and 88,000 podcasters that use the Acast platform to distribute their podcast content can elect to utilize Veritonic’s robust attribution capabilities to optimize and further increase the ROI of their audio campaigns. “We are pleased to partner with Acast to support brands, agencies, and publishers with the holistic data and analytics they need to increase their reach and ROI with audio. "The powerful combination of our attribution and brand lift technology provides unparalleled and comprehensive measurement of audio campaigns from top to bottom in one unified and intuitive platform.” Scott Simonelli, chief executive officer of Veritonic "Veritonic shares our commitment to arming brands and agencies with actionable and insightful audio performance data,” said Kevin McCaul, Global Head of Ad Operations at Acast. “Our partnership is an important step for the open ecosystem of podcasting as we continue to work together to provide independent measurement insights to prove the effectiveness and efficiency of podcasting as a marketing channel.” Veritonic’s Attribution solution enables users to glean actionable insights from top-of-the-funnel branding initiatives through bottom-of-the-funnel conversions & transactions. Through an intuitive and interactive dashboard, brands can determine which publisher and specific ads had the highest impact and use that data to optimize ad performance. About Veritonic World-renowned brands, agencies, publishers, and platforms rely on Veritonic’s comprehensive audio research and analytics platform to research, test, and measure the ROI of their audio assets and campaigns pre-market, in-market, and post-campaign. The resulting insight enables clients to gain confidence in their audio investment, mitigate risk through optimization, and increase their return as they engage consumers with compelling audio experiences. About Acast Acast is the world’s largest independent podcast company. Founded in 2014, the company has pioneered the open podcast ecosystem ever since – making podcasts available on any listening platform. Acast provides a marketplace, helping podcasters find the right audience to monetize their content. When our podcasters make money, we make money. Today, Acast hosts nearly 88,000 podcasts, with more than 430 million listens every month.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Quantiphi announces partnership with Databricks to help drive enterprise-wide AI adoption

Quantiphi | November 15, 2022

Quantiphi, an AI-first digital engineering company, today announced its partnership with Databricks, the lakehouse company and pioneer of this new data paradigm. Together, Quantiphi and Databricks will focus on helping enterprise customers to optimize their business workflows with AI which will be enabled by a strong foundation of the Lakehouse platform. Businesses today are embracing multi-cloud and hybrid cloud environments. As a result, it has become imperative to modernize their data foundation for seamless operations across different environments, and deploy advanced cloud-based technologies – including analytics tools with advanced machine learning (ML) and MLOps capabilities. As Databricks Consulting Partners, Quantiphi will accelerate AI-driven innovation for clients across industries. Quantiphi's in-depth knowledge and deep expertise in helping enterprise customers modernize and democratize their Data and AI footprint at scale are valuable for the partnership with Databricks. Databricks' Lakehouse Platform is cloud-agnostic and enables users to unify their data warehousing and AI use cases on a consistent platform across multiple infrastructures simultaneously. "We are delighted to enter into a strategic partnership with Databricks. "By combining the power of Databricks' Lakehouse Platform and Quantiphi's advanced AI/ML capabilities, our teams will empower customers to leverage data-driven MLOps and enable enterprise AI success." Asif Hasan, Co-founder, Quantiphi The collaboration is set to actively support customers with various data and AI services such as digital transformation strategy, AI innovation roadmap, MLOps, data modernization and migration, data management, security, and governance implementations. Quantiphi's team of dedicated applied AI Databricks experts will further help customers implement and scale data engineering, collaborative data science, full-lifecycle machine learning, and business analytics initiatives. "Today, there is a rising need for every business to have a strong foundation of data and AI. By combining the strength of Databricks' Lakehouse Platform in data engineering, data science and analytics and Quantiphi's AI-first digital engineering capabilities, we can help companies transform their businesses through the power of data", said Mohak Moondra, Practice Leader - Applied AI, Quantiphi. About Quantiphi Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Quantiphi solves the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed. We are passionate about our customers and obsessed with problem-solving to make products smarter, customer experiences frictionless, processes autonomous and businesses safer by detecting risks, threats, and anomalies. Together with partners and customers, we embark on a data and AI-led transformation journey that delivers impactful and measurable results.

Read More

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