Category Archives: Data Science
The Best RapidMiner Tutorials on YouTube to Watch Right Now – Solutions Review
This list of the best RapidMiner tutorials on YouTube will introduce you to one of the most popular data science platforms.
RapidMiner offers one of the most popular data science platforms that enables people of all skill levels across the enterprise to build and operate AI solutions. The product covers the full lifecycle of the AI production process, from data exploration and data preparation to model building, model deployment, and model operations. RapidMiner provides the depth that data scientists need but simplifies AI for everyone else via a visual user interface that streamlines the process of building and understanding complex models.
Learning RapidMiner can be a complicated process, and its not easy to know where to start. As a result, our editors have compiled this list of the best RapidMiner tutorials on YouTube to help you learn about the platform and hone your skills before you move on to mastering it. All of the videos here are free to access and feature guidance from some of the top minds and biggest brands in the online learning community. All of the best RapidMiner tutorials listed tout a minimum of 5,000 views.
Note: Dont forget to subscribe to Solutions Review on YouTube!
Author: Data Science at INCAE
Description: With more than 57,000 views, this is the most popular RapidMiner tutorial on YouTube. It shows you how to perform a simple cluster analysis, interpret clusters and decide how many to use. Our editors highly recommend this resource.
Author: Pallab Sanyal
Description: This video provides a brief introduction to the RapidMiner Studio interface and shows how to import datasets into RapidMiner Studio, as well as how to create, run and save a process. Instructed by thought leader Pallab Sanyal and with more than 44,000 views, this is worth the watch.
Author: Data Science at INCAE
Description: Once youve decided on the most suitable model for a particular prediction problem, how do you predict for new data? How do you save our new predictions to Excel to share them? This video, with more than 34,000 views, outlines these processes.
Author: Nasir Soft
Description: More than 6,000 lifetime YouTube views qualifies this video resource for the list, and it offers a solid but basic tutorial for brand new RapidMiner users. If youre just getting started using the pla
Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.
Read the original:
The Best RapidMiner Tutorials on YouTube to Watch Right Now - Solutions Review
UW-developed, cloud-based astrodynamics platform to discover and track asteroids – University of Washington
News releases | Research | Science
May 31, 2022
A novel algorithm developed by University of Washington researchers to discover asteroids in the solar system has proved its mettle. The first candidate asteroids identified by the algorithm known as Tracklet-less Heliocentric Orbit Recovery, or THOR have been confirmed by the International Astronomical Unions Minor Planet Center.
The Asteroid Institute, a program of B612 Foundation, has been running THOR on its cloud-based astrodynamics platform Asteroid Discovery Analysis and Mapping, or ADAM to identify and track asteroids. With confirmation of these new asteroids by the Minor Planet Center and their addition to its registry, researchers using the Asteroid Institutes resources can submit thousands of additional new discoveries.
A comprehensive map of the solar system gives astronomers critical insights both for science and planetary defense, said Matthew Holman, dynamicist and search algorithm expert at the Center for Astrophysics | Harvard & Smithsonian and the former director of the Minor Planet Center. Tracklet-less algorithms such as THOR greatly expand the kinds of datasets astronomers can use in building such a map.
THOR was co-created by Mario Juri, a UW associate professor of astronomy and director of the UWs DiRAC Institute, and Joachim Moeyens, a UW graduate student in astronomy. They and their UW collaborators unveiled THOR in a paper published last year in The Astronomical Journal. It links points of light in different sky images that are consistent with asteroid orbits. Unlike current state-of-the-art codes, THOR does not require the telescope to observe the sky in a particular pattern for asteroids to be discoverable.
Visualizing the trajectories through the solar system of asteroids discovered by ADAM and THOR.B612 Asteroid Institute/University of Washington DiRAC Institute/OpenSpace Project
The Asteroid Institutes ADAM platform is an open-source computational system that runs astrodynamics algorithms at large scale using Google Cloud, in particular the scalable computational and storage capabilities in Google Compute Engine, Google Cloud Storage and Google Kubernetes Engine.
The work of the Asteroid Institute is critical because astronomers are reaching the limits of whats discoverable with current techniques and telescopes, said Juri, who is also a senior data science fellow with the UW eScience Institute. Our team is pleased to work alongside the Asteroid Institute to enable mapping of the solar system using Google Cloud.
Researchers can now begin systematic explorations of large datasets that were previously not usable for discovering asteroids. THOR recognizes asteroids and, most importantly, calculates their orbits well enough to be recognized by the Minor Planet Center as tracked asteroids.
Moeyens searched a 30-day window of images from the NOIRLab Source Catalog, a collection of nearly 68 billion observations taken by the National Optical Astronomy Observatory telescopes between 2012 and 2019, and submitted a small initial subset of discoveries to the Minor Planet Center for official recognition and validation. Now that the computational discovery technique has been validated, thousands of new discoveries from the catalog and other datasets are expected to follow.
Discovering and tracking asteroids is crucial to understanding our solar system, enabling development of space and protecting our planet from asteroid impacts, said Ed Lu, executive director of the Asteroid Institute. With THOR running on ADAM, any telescope with an archive can now become an asteroid search telescope. We are using the power of massive computation to enable not only more discoveries from existing telescopes, but also to find and track asteroids in historical images of the sky that had gone previously unnoticed because they were never intended for asteroid searches.
The B612 Foundation recently announced an additional $2.3 million in leadership gifts to advance these efforts.
The collaborative efforts of Google Cloud, B612s Asteroid Institute and the University of Washingtons DiRAC Institute make this work possible.
For more information, contact Juri at mjuric@uw.edu and Moeyens at moeyensj@uw.edu.
Adapted from a press release by the B612 Foundation.
Excerpt from:
Harnessing and actioning data to drive business growth – Tech Wire Asia
In partnership witheTail Asia 2022,Tech Wire Asiahad the pleasure to interview Dr Angshuman Ghosh, Head of Data Science for Sony Research India. Together we discussed how new sources of data, fed into systems powered by machine learning and AI, are at the heart of the digital transformation for many businesses across most industries.
Around the world, Covid-19 has accelerated retails transition to a digital future that we are living through today. It is also clear that the Asian region will get there first simply because of its advanced digital maturity. At this point, the region is generating about three-quarters of global retail growth and about two-thirds of online growth. In short, Asia is already the worlds consumption growth engine and the position is likely to be reinforced over the next decade.
As McKinsey Global Institute puts it, disregarding Asias consumer markets would mean missing half the global consumption story. Futuristic retail topics of such have been an ongoing discussion, especially since the pandemic struck. This time, it is also hot on the agenda at eTail Asia 2022, an event that is making a live and in-person return after two years, this 7-9 June 2022 at Resorts World Convention Center Singapore.
As the industrys very best gathers in the three-day summit, eTail Asia will bring together the top international and home-grown retailers and eCommerce brands as well as subject matter experts in Asia. Among them is Dr Angshuman Ghosh, Head of Data Science for Sony Research India. Dr Angshuman will be part of a panel discussion, sharing insights and best practices based on his experiences on how data science and digital technologies be used to optimize business decision making and engage customer experience.
Tech Wire Asia (TWA) discussed with Dr Angshuman how these new sources, fed into systems powered by machine learning and AI, are at the heart of the digital transformation for many businesses across most industries. However, the sheer abundance of information and a lack of know-how can be overwhelming for most organizations.
What will you be sharing at eTail Asia?
Dr Angshuman Ghosh, Head of Data Science for Sony Research India
I am very happy and excited to join the eTail Asia conference as a speaker. I will be part of a panel discussion on 7th June at 14:05 pm SGT. I have significant work experience in both offline retail and eCommerce domains. I will share insights and best practices based on my experiences about how we can use data science and digital technologies to optimize business decision-making and engage customer experience.
What are the key topics you are keen to listen to and why is eTail a must not miss event?
eTail is one of the best conferences globally. Senior executives from the topmost global brands will be speaking at the conference. I am particularly interested to know about future trends in the retail and eCommerce industries and how data and digital technologies are creating value for top global companies and brands.
With that said, what are the common mistakes organizations make when it comes to understanding their customers?
As data grows exponentially, most companies take a reactive and unorganized approach. Companies may not capture data across the lifecycle or may not have processes for ensuring data validity and quality.
Without the right data and good data quality, the analysis may be incomplete or misleading. Without the right data analysis, customer understanding will be incorrect leading to suboptimal or wrong decisions.
For large enterprises, what complexities arise when managing input from various sources today?
There are three main complexities in a large company when trying to manage data from different sources. Firstly, different types of data require different tools and technologies to capture specific data to be compatible with each other.
Secondly, this information must be stored using a standardized format and have processes for ensuring data refresh and quality. Thirdly, data access and governance have to be managed efficiently to ensure proper usage and unlock its value.
How does a large company use information from consumers to not only improve their products and services, but also understand their demands?
Data helps to understand customer demand. Demand Forecasting is a critical application for many large companies including those in retail and media industries. In retail and eCommerce industries, data helps us predict the future demand for different products and plan all the operations and supply chain activities.
In the Media industry, user viewership and interaction data help us understand content preferences and this, in turn, helps us create more relevant content or build recommendation engines.
How has emerging technologies in data management and the enhancements in machine learning capabilities enhanced its use by both large and small enterprises today?
Emerging tools and technologies have transformed the entire data ecosystem. Open source and free tools such as Python or R have played a big role in this transformation. Earlier only large companies with big budgets could analyze lots of data using costly licensed software. But now any company, big or small, can make use of their data using free and open-source tools.
Cloud technologies such as AWS or Azure are also playing a key role by allowing every enterprise to make use of the best data-related services on-demand and pay for only their usage without spending a fortune on infrastructure.
Where do organizations draw the line when it comes to privacy, especially with collecting information and predicting what customers need?
User privacy is a very important topic and finally, it is getting its due importance. Europe has implemented stringent data privacy laws to protect user data and privacy. Even big-tech companies such as Google and Facebook have paid hefty fines for violating data privacy regulations. It is better to take a proactive approach to data privacy and security.
Organizations must classify input into different groups based on privacy requirements and regulations. Access to each class of data should be given to only appropriate stakeholders and any inappropriate usage should be prohibited by design.
With perfecting the customer experience the goal for most organizations today, is AI enough to solve this or do businesses still need to offer a personalized human approach towards them?
While AI and Data Science have revolutionized many aspects of the business world, human focused approaches are still very much relevant. There are use cases where AI can do much better than humans and those tasks should be handled by AI.
However, customers are human beings after all and humans are emotional and social beings who value connection with other humans. So, there are cases where customers may still prefer a human-centric approach. Balancing the usage of AI and a personalized human approach may be ideal for most customer-facing businesses.
Continue reading here:
Harnessing and actioning data to drive business growth - Tech Wire Asia
U of T and Sinai Health announce new gift from Larry and Judy Tanenbaum to establish the Tanenbaum Institute for Science in Sport – EurekAlert
Established through a generous $20-million gift from the Larry and Judy Tanenbaum Family Foundation, the Tanenbaum Institute for Science in Sport at the University of Toronto will be a global centre of excellence for high-performance sport science and sports medicine. The Tanenbaum Institute will yield new knowledge at the intersection of research and practice, translating discoveries into innovations that dramatically impact health and performance across all athlete populations.
The Tanenbaum Institute will bring together the leading sport science research of the University of Torontos Faculty of Kinesiology & Physical Education, the sports medicine research expertise of the Temerty Faculty of Medicine, and the renowned clinical and research leadership of the Dovigi Orthopaedic Sports Medicine Clinic and the Lunenfeld-Tanenbaum Research Institute at Sinai Health.
Today marks a monumental step forward in support of Canadian high-performance athletics, one that will lead to improved athlete performance, safety and well-being, said University of Toronto President Meric Gertler. Thanks to the extraordinary generosity of Larry and Judy Tanenbaum, the Tanenbaum Institute for Science in Sport will become one of the worlds leading centres in the field. And the Institute will be truly unique, combining the strengths of U of Ts top-ranked research programs and sports medicine departments with leading clinical care centres at Sinai Health, all in the heart of one of the worlds most celebrated sporting cities.
The Tanenbaum Institute for Science in Sport will help model and predict athlete performance and improve health outcomes based on a wealth of data from across the Greater Toronto Area. This new knowledge will support high-performance athletes across a spectrum that includes world-class professional, non-professional and para athletes, including from diverse and underrepresented communities, as well as athletes striving for high-performance optimization in recreational sports.
The Institute will catalyze U of T and Sinai Healths sport science and sports medicine expertise, generating novel insights and innovative technologies and interventions that improve athlete performance, health, safety, and well-being; reduce risk of injury; accelerate and optimize recovery and rehabilitation; and advance high-performance sport in a manner that is safe, welcoming, inclusive, and accessible to all.
To this end, the Tanenbaum Institute will work in partnership with sports clinics, associations, and organizations, including Maple Leaf Sports & Entertainment (MLSE) and its teams: the Toronto Maple Leafs, Toronto Raptors, Toronto FC and the Toronto Argonauts, as well as the Toronto Marlies, Raptors 905 and TFC 2.
I truly believe that sport unites us, inspires us, and offers all people a path toward becoming their best selves, said Larry Tanenbaum, chairman of Tanenbaum Family Foundation and MLSE. The Tanenbaum Institute will bring together sports medicine, sport science and data science to encourage athletic engagement, enhance performance and accelerate recovery and rehabilitation. Im proud to join with U of T and Sinai Health in transforming athlete health and well-being.
Larry and Judy Tanenbaums gift will be combined with more than $20 million in additional support from U of T and Sinai Health. This investment will establish a Directorship and Research Acceleration Fund to support bold, innovative research across the Institute, the University, and Sinai Health; create a groundbreaking new Chair in Sport Science and Data Modelling, a Chair in Musculoskeletal Regenerative Medicine, and a Professorship in Orthopaedic Sports Medicine; and provide funding for a range of cutting-edge research, innovations, and clinical programs.
The Tanenbaum Institute will enjoy a remarkable head start, thanks to the amazing research and clinical sports medicine leadership we have amassed here at Sinai Health through the Dovigi Orthopaedic Sports Medicine Clinic and across U of T, said Dr. Gary Newton, President and CEO of Sinai Health. Establishing this landmark Institute is only the beginning. We look forward to transforming high-performance sport together with our many industry, government, and community partners.
The Tanenbaum Institutes cutting-edge research will play a leading role in advancing high performance sport in a manner that is safe, welcoming, inclusive and accessible to all, said Gretchen Kerr, Dean of the Faculty of Kinesiology & Physical Education at U of T. We are so excited to be joining in this important research enterprise by pooling together our academic research, large and diverse athlete base and training facilities with the world-class clinicians of Sinai Health.
Were incredibly excited by the potential for the Tanenbaum Institute to transform sports medicine across Canada and to train future generations of sport science and sports medicine leaders, said Trevor Young, Dean of the Temerty Faculty of Medicine at U of T. By bringing together so many disciplines, the Tanenbaum Institute will make breakthrough big data-driven findings that will lead to better athlete health, safety and performance.
The Institute combines a diverse array of sport science and sports medicine talent. The Institutes research and clinical foci will include mild traumatic brain injuries, orthopaedics, regenerative medicine, biomechanics, wearable physiological and training monitoring technologies, technologiesin para sport, mathematical and statistical modelling applied to individual athlete and team analytics, nutrition, individual and team psychology and health, exercise physiology and more.
This latest gift from the Tanenbaum Family Foundation builds on an impressive philanthropic legacy at U of T, Sinai Health and beyond. Larry and Judy Tanenbaum and the Tanenbaum family have been long-time supporters of the University of Toronto. In 2014, they helped establish the Anne Tanenbaum Centre for Jewish Studies at the Faculty of Arts & Science, one of North Americas leading programs of its kind. They also have also established several scholarships in support of student athletes at the University of Toronto.
At Sinai Health, Larry and Judy Tanenbaum have made several transformative investments. In 2013, the Tanenbaums gave $35 million to rename the Lunenfeld-Tanenbaum Research Institute (LTRI), accelerating Sinai Healths biomedical research institute.
Larry and Judy Tanenbaum have also made major gifts in support of cutting-edge physical and mental health research across Canada. Their generosity led to the creation of the Tanenbaum Open Science Initiative at McGill University and the Tanenbaum Centre for Pharmacogenetics at theTanenbaum Centre for Pharmacogenetics at the Centre for Addiction and Mental Health.
See more here:
Call for Enhanced Disaster Risk Data Governance and Data-driven Analysis to Inform Policy Decisions at the Global Platform for Disaster Risk Reduction…
Governments, the UN system, and all disaster risk reduction stakeholders gathered for the seventh session of the Global Platform for Disaster Risk Reduction 2022 in Bali, organized by the United Nations Office for Disaster Risk Reduction and hosted by the Government of Indonesia. As part of the official GP/DRR programme, the thematic session on [data challenges and solutions for disaster risk management was held on 26 May 2022. The session was moderated by Ms. Letizia Rossano, Director of the Asian and Pacific Centre for the Development of Disaster Information Management (APDIM), a subsidiary body of the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP).
"Despite great advances in technology and data science, gathering and applying disaster-relevant data and statistics to investment decisions to reduce disaster risk is still a challenge. We need to be able to account for the full scale of losses from disasters small, recurring, and major extreme events. And we need to have clear, data-based overviews on disaster risks, including those induced by climate change" said APDIM Director Ms. Letizia Rossano during the session which drew over 400 participants attending in-person and over 2500 attended online.
The thematic session panellists discussed ways to improve data governance in a way that the benefits of the resultant policies, programmes, and investments enable the governments to ensure effective risk reduction measures are in place and to reach the most vulnerable in all scenarios. They also discussed the role of data community in enhancing the use of disaster risk data across development and humanitarian planning. H. E. Dr Raditya Jati, Deputy Minister for Systems and Strategy, Indonesian National Board for Disaster Management (BNPB); H. E. Mr. Renato U. Solidum, Jr., Undersecretary for Scientific and Technical Services, Department of Science and Technology of the Philippines; Ms. Rhonda Robinson, Deputy Director Disaster and Community Resilience Programme, Acting Director GEM Division, Pacific Community; Dr Jakub Ryzenko, Head of Crisis Information Centre SRC, Poland; Mr. Kassem Chaalan, Director, Disaster Risk Reduction, Lebanese Red Cross; and Ms. Sithembiso Gina, Senior Programme Officer, DRR Unit, SADC Secretariat presented their views and remarks as panellist of the Session.
The discussion at the session highlighted that:
Effective collection, dissemination and application of relevant data and statistics about disaster risk and impact is of paramount importance at national and local level to form the evidence base for policy, planning, implementation and investment in development and humanitarian domains across different sectors and to ensure that no-one is left behind.
Strong data governance is required to access quality disaster and climate data by working across departmental silos and administrative levels to produce relevant analysis and products to inform disaster risk policy and investments, ideally in the context of a national disaster data policy is key.
Building of capacities is necessary to increase the demand for disaster risk data and information and its application towards risk assessment and climate change analysis, including by leveraging on strategic partnerships and latest technology.
The outcomes of the thematic session on data challenges and solutions for disaster risk management was reflected in the Global Platform co-Chair's summary and will contribute to the inter-governmental midterm review of the Sendai Framework scheduled for 2023.
About the Asian and Pacific Centre for the Development of Disaster Information Management(APDIM)
APDIM is a regional institution of the United Nations Economic and Social Commission for Asia and Pacific(ESCAP) headquartered in Tehran. APDIM's vision is to ensure that effective disaster risk information isproduced and used for sustainable development in Asia and the Pacific.
The Preliminary Assessment of the Gaps and Needs for Disaster Risk Information and DataManagement Platforms in Asia and the Pacific Region (link)
The Preliminary Assessment of the Gaps and Needs for Disaster Risk Information and Data ManagementPlatforms in Asia and the Pacific Region, is an APDIM publication. It includes inputs from regional entitiesand national institutions from four countries, provides an overview of available risk datasets covering the Asiaand Pacific region, shares the findings on challenges, gaps in supply and demand from national and regionalentities, and provides five suggestions for the way forward to support countries in enhancing understandingdisaster risk and using risk information for disaster risk reduction
For more information, please contact:
Ava Bahrami, Public Information Officer, ava.bahrami@un.org
Original post:
Global Data Science Platform Market Key Companies Profile with Sales, Revenue and Competitive Situation Analysis The Greater Binghamton Business…
This market report gives clarification about the detailed market analysis with contributions from industry experts. This worldwide market report identifies and analyses the emerging trends and patterns alongside real drivers, difficulties, and opportunities in this industry with an analysis of merchants, geographical regions, types, and applications. This market report presents information on examples and improvements and targets business parts and materials, cutoff points, and progressions. This market research report likewise gives advertise conjecture data, considering the history of the industry, the future of the business concerning respect to what situation it may face, it will grow or it will fail.
This market report is an amazing asset to gain a top to bottom investigation of the present and upcoming opportunities to clear up the future interest in the market. This report demonstrates to be an irreplaceable archive for every market enthusiast, policymaker, investor, and player. The market research report performed here also provides information about producers, market competition, and cost market effect factors for the estimated time. This market report has a chapter on the global market and all its related organizations with their profiles, which gives profitable information identified with their viewpoint as far as accounts, item portfolios, product portfolios, and marketing and business strategies.
Increase in the need to extract in-depth insights from voluminous data, growing adoption of cloud based solutions by small and medium scale organisations and rising prevalence of digitization especially in the developing economies are the major factors attributable to the growth of thedata science platform market. Data Bridge Market Research analyses that the data science platform market will exhibit a CAGR of 29.98% for the forecast period of 2021-2028. This means that the data science platform market would stand tall at a market value of USD 392.98 billion by 2028.
Request A Sample PDF Brochure + All Related Graphs & Charts @https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-data-science-platform-market&AM
(The sample of this report is readily available on request)
What this report sample includes:
List of Key Players Profiled in the study includes market overview, business strategies, financials, Development activities, Market Share and SWOT analysis:
The major players covered in the data science platform market report are Oracle., VMware Inc., Google LLC, IBM, Microsoft, Domino Data Lab, Inc., DataRobot, Inc., Wolfram Alpha LLC, Anaconda Inc., Dataiku., Bridgei2i Analytics, Feature Labs, Inc., Datarpm, Rexer Analytics., Civis Analytics, Sense, Inc., Alteryx, Inc., Rapidminer, Inc., Snowflake Inc., MeritB2B., Cazena Inc., and Teradata., among other domestic and global players. Market share data is available for global, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America separately. DBMR analysts understand competitive strengths and provide competitive analysis for each competitor separately.
Data Source & Research Methodology:
Data collection and base year analysis are done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. In addition, market share analysis and key trend analysis are the major success factors in the market report. The key research methodology used by the DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, GCC Vs Regional, and Vendor Share Analysis. Please request an analyst call in case of further inquiry.
Against challenges Faced by Industry, Data Science Platform Market Study discusses and shed light on:
The resulting overview to understand why and how theGlobal Data Science Platform Marketis expected to change.
Where the Data Science Platform industry is heading and what are the top priorities. To elaborate it, DBMR turned to the manufacturers to draw insights like financial analysis, the survey of Data Science Platform companies, and from interviews with upstream suppliers and downstream buyers and industry experts.
How Data Science Platform Company in this diverse set of players can best navigate the emerging new industry landscape and develop strategy to gain market position.
Know More about the Study | Visit @https://www.databridgemarketresearch.com/reports/global-data-science-platform-market?AM
Key Market Segmentation
By the component type, the data science platform market is segmented into platform and services. Services segment has been further segmented into managed services and professional services. Professional services segment has been further bifurcated into training and consulting, integration and deployment and support and maintenance.
Based on function division, the data science platform market is segmented into marketing, sales, logistics, finance and accounting, customer support, business operations and others.
Based on deployment model, the data science platform market is segmented into on-premises and cloud based.
On the basis of organization size, the data science platform market is segmented into small and medium-sized enterprises (SMEs) and large enterprises.
Based on the end user application, the data science platform market is segmented into following sectors banking, financial services, and insurance (BFSI), telecom and IT, retail and e-commerce, healthcare and life sciences,manufacturing, energy and utilities, media and entertainment, transportation and logistics, government and others.
This comprehensive report provides:
Browse Summary and Complete Table of Content @https://www.databridgemarketresearch.com/toc/?dbmr=global-data-science-platform-market&AM
Explore Trending Reports By DBMR
Clientless remote support software marketwill reach at an estimated value of USD 2017.34 million by 2028 and grow at a CAGR of 14.45% in the forecast period of 2021 to 2028. Rise in the need to handle issues such as firmware software, battery optimization and malware detection is an essential factor driving the clientless remote support software market https://www.databridgemarketresearch.com/reports/global-clientless-remote-support-software-market
Theprivate LTE marketis expected to witness market growth at a rate of 13.10% in the forecast period of 2021 to 2028. Data Bridge Market Research report on the private LTE market provides analysis and insights regarding the various factors expected to be prevalent throughout the forecast period while providing their impacts on the markets growth. Rapid digitization across various industries is escalating the growth of the private LTE market https://www.databridgemarketresearch.com/reports/global-private-lte-market
Thecluster computing marketis expected to witness market growth at a rate of 11.9% in the forecast period of 2021 to 2028. Data Bridge Market Research report on cluster computing market provides analysis and insights regarding the various factors expected to be prevalent throughout the forecast period while providing their impacts on the markets growth. The rapidly generating data globally is escalating the growth of cluster computing market https://www.databridgemarketresearch.com/reports/global-cluster-computing-market
Data Bridge Market Research analyses thetherapeutic robots marketwill exhibit a CAGR of 22.10% for the forecast period of 2022-2029 and is likely to reach 283.3 million in 2029 https://www.databridgemarketresearch.com/reports/global-therapeutic-robots-market
Globalautomotive cybersecurity marketwas valued at USD 1.75 billion in 2021 and is expected to reach USD 7.90 billion by 2029, registering a CAGR of 20.73% during the forecast period of 2022-2029. Passenger Vehicles is expected to witness high growth in the respective market owing to the rise in the sales of mid-luxury and luxury vehicles. The market report curated by the Data Bridge Market Research team includes in-depth expert analysis, import/export analysis, pricing analysis, production consumption analysis, and pestle analysis https://www.databridgemarketresearch.com/reports/global-automotive-cybersecurity-market
About Data Bridge Market Research, Private Ltd
Data Bridge Market ResearchPvtLtdis a multinational management consulting firm with offices in India and Canada. As an innovative and neoteric market analysis and advisory company with unmatched durability level and advanced approaches. We are committed to uncover the best consumer prospects and to foster useful knowledge for your company to succeed in the market.
Data Bridge Market Research has over 500 analysts working in different industries. We have catered more than 40% of the fortune 500 companies globally and have a network of more than 5000+ clientele around the globe. Our coverage of industries includes
Contact Us
US: +1 888 387 2818UK: +44 208 089 1725Hong Kong: +852 8192 7475Email corporatesales@databridgemarketresearch.com
More:
Drawing data: I make art from the bodily experience of long-distance running – The Conversation Indonesia
In 1979, the American artist Allan Kaprow wrote Performing Life, an important essay in the history of Western art arguing for the blurring of art and life.
Kaprow suggested we perform art in our everyday living by paying attention to invisible sensations and the details of existence we take for granted.
He wanted us to notice the way air and spit are exchanged when talking with friends; the effects of bodies touching; the rhythm of breathing. Kaprows essay has served as an instructive piece for living. As an artist, I see and make art from anything and everything.
Take long-distance running, for example.
When a friend began coaching me to run long distances, I consequently began drawing as well.
Running in the 21st century involves drawing lines. More important than sneakers are GPS devices (like smartphones and watches that sync with exercise apps) to track and analyse every meaningful and meaningless detail of your performance.
I use the app Strava. It visualises my routes, average pace, heart rate, elevation and calories burned.
Driven by competition, self-improvement and the well-being revolution, it is easy to become fixated on this data. But I am fixated for other reasons as well.
When I run, Strava maps my route with a meandering GPS line. I have become absorbed in this line literally sensing it when Im running.
As each foot touches the ground I feel myself drawing the GPS line slowly and incrementally. This embodied connection to my data changes my runs. I spontaneously vary my routes to achieve a particular line.
Run around a pole ten times, burst into a zig-zag, make a circle in the park. I manoeuvre to affect the graphic form to one of my imagining.
Cyclists and runners worldwide have recently discovered the creative possibilities of GPS data. This is called GPS Art or Strava Art. Cycling or running routes to visualise a predetermined shape or thing proved popular during the pandemic. Rabbits, Elvis and the middle finger have been plotted, cycled and run. The data version of skywriting.
As novel as Strava Art is, I am not creating it. Running and drawing are both body techniques connected to gesture, touch, feeling, listening, looking and imagining.
While GPS data can visualise every quantifiable detail about my run, it cant tell me how it felt to run.
Anyone who runs long distances knows that performance is impacted by how you feel on the day, what the run is soothing. Life problems, levels of stress, hormones, depression, happiness, whether you slept well, how much you ate and the weather all impact a run.
Read more: Yearning for touch a photo essay
While self-tracking data appears infallible that is, numerical, scientific and objective we know it is biased. Data scientists Catherine D'Ignazio and Lauren Klein argue in their book Data Feminism that data science is skewed towards to those who wield power, which is disproportionately elite, straight, white, able-bodied, cisgender men from the Global North.
Data feminism reveals how systems of counting and classification hide inequalities. The role of data feminism is to use this understanding of what is hidden to visualise alternatives, and they suggest converting qualitative experience into data.
This is where the outmoded art of drawing and the antiquated technologies of charcoal and pencil can extend exercise data and bring new meaning to the personal experience of running.
I have been re-drawing my data to make visible what Strava cannot. The unheroic stuff: emotions, persistent thoughts, body sensations like the pressing of my bladder, the location of public toilets, social interactions with strangers, lyrics from the songs I listen to, and the weather.
The drawings are deliberately messy scribbles, diaristic and fragmented, smudged and imprecise. They re-label information to reflect what is missing that Im a middle-aged woman artist mother who is happy to remain mediocre at running.
Picture a nagging bladder instead of speed; scribble the arrival and departure of joy and anxiety instead of pace; zig-zag through neurotic repetitive thoughts instead of calories burned; trace desires, dreads and dreams instead of personal bests.
Thats not to say I dont enjoy Strava or that I dont have running goals. But the drawings offer different data that short circuits the dominance of quantifying every aspect of human experience.
The effects of bringing art and life (or running and drawing) together are confrontational. As Kaprow noted, anyone who has jogged seriously [] knows that in the beginning, as you confront your body, you face your psyche as well.
The longer more testing runs produce more data; they also produce more complex encounters with the self and more convoluted drawings.
These drawings offer alternative data not tied to endurance or personal glory. They undo the slick corporate aesthetic of exercise apps and their socially networked metrics, leader boards, badges and medals.
This is a feminist practice that builds on the work of Catriona Menzies-Pike and Sandra Faulkner who give alternative accounts of running from the perspective of women.
Running and drawing can be autotelic activities: activities where the purpose of doing it is doing it. In this highly surveilled, techno-obsessed, capital driven, multi-tasking time, the value of sporting or creative activities delivering no measurable (or financial) results may appear old-fashioned.
But then again, so can running without a self-tracking device. This is something I am willing to try.
Read more: The 'runner's high' may result from molecules called cannabinoids the body's own version of THC and CBD
Original post:
Building the connected utility of the future – Utility Dive
Todays utilities bear little resemblance to the utility companies from decades ago.
For most of the last century, electric utilities operations were built around an architecture of one-directional flow of power from generation to customers, under fairly predictable conditions. Now, however, the environment in which utilities operate looks far different.
Climate change is contributing to wildfires and temperature extremes, forcing many utility companies to run in conditions they never planned for.1At the same time, the integration of renewable generation, storage and other DERs is driving a shift to a DSO model in which the grid is more distributed and electricity flows bidirectionally between utilities and consumers. Finally, through consolidations and mergers, utilities have grown more organizationally complex, adding yet another hurdle to dynamically responding to these new conditions.
Addressing the breadth of these challenges requires an unprecedented level of connectivity across distributed organizations. To harden systems, predict when equipment will fail and communicate thoughtfully with tens of thousands of customers during emergency events, utilities need an enterprise-wide data-driven operating system that spans across operators, analysts and leaders.
Utilities today have access to a rich set of data, but they may not be leveraging it to its fullest extent. With the right systems in place, utilities can gather real-time signal about grid operations from smart meters, leverage LiDAR to achieve highly accurate GIS records and deploy drones to remotely inspect asset conditions. To harness this data, many utilities are investing in data science teams and new ML/AI off-the-shelf toolkits. While these efforts frequently create impressive data assets and promising models, many prototypes fail to make it to production and actually achieve the promised operational improvements.
For example, imagine a utility that has begun a pilot project to deploy new sensors to help manage DER integration. The data science team has leveraged industry-standard data science tools to detect power quality faults and recommend where maintenance inspections should begin looking to find the source of the problem.
To get this off the ground, each day, the data science teams business counterparts export spreadsheets of model outputs for maintenance engineers.
Engineers then review recommendations in conjunction with context from other applications a time-consuming and error-prone process before committing decisions to a legacy scheduling system. As part of the review, engineers cross-check the modeling outputs with various maintenance schedules, often performing ad-hoc analysis based on their real-world experience. Moreover, stale historical assumptions are often baked into these models, requiring the engineers to manually correct the same discrepancies every time the data science team delivers a fresh set of outputs.
For the data science team, developing models is also a manual and painful process, involving manual data cleaning and feature engineering. The data scientists struggle to run the model across all feeders and to leverage models from other analytic projects. Most crucially, they dont get timely feedback from their consumers. Without frequent updates on which recommendations were accurate and which werent, it proves difficult to iteratively improve the models over time and ensure that they were accurate.
With so many distributed sources of information and the messy process of combining human insights with data, utilities may find it challenging to make the most of their investments in new data sources and analytical capabilities. Many utility companies may face technical and organizational barriers that prevent their data, analytics, engineering and operational teams from iterating closely together.
For decades, utilities have relied on foundational software systems that are critical to the electric power infrastructure, but also highly constraining. Purpose-built on-premises systems like DMS, SCADA and GIS are foundational for managing grid infrastructure data, but theyre specialized for a narrow set of data and operational processes, making them of limited use for utilities grappling with an ever-evolving set of challenges.
Newer iterations on these systems, like ADMS and DERMS, promise to help utilities manage a more distributed, DER-friendly grid. However, they are still too specialized to deal with the larger issues facing utilities. These systems don't typically integrate external data, leverage custom models, or help utility companies build new workflows. Utilities will need these capabilities and more, to adapt their operations in the face of two-way power flow across distributed energy resources, more complex weather events, and other challenges of the twenty-first century.
Outside of these systems, utilities have followed the lead of other industries in investing in new cloud systems like data warehouses and analytics engines. These perform capably at collecting data from different sources, but they are removed from the core operations that made the legacy systems, like DMS and EMS, so valuable. Cloud-based data warehouses may summarize a complex reality, but they dont offer levers to act or respond to new threats. They don't go the last mile of getting these analytics into the hands of field crews and grid operators so that they can make better decisions day after day. For instance, even after years of investment in cloud modernization, when faced with an emergency operation or urgent new work program, many utilities rely heavily on spreadsheets, emails and file shares, which quickly become outdated and don't leave a trail of where information came from. Often, the newer cloud tools don't actually improve the operational bottom line when they're needed most.
In the face of massive disruption, analytics alone arent enough: utilities need a platform that can help them power complex operations by combining the best of data and human insight. This platform should break down data silos and ultimately enable a feedback loop between data teams, analysts, engineering and planning teams and grid operators.
Pacific Gas and Electric (PG&E) uses Palantir Foundry to layer together different sets of information, such as real-time grid conditions and risk models. The utility can also conduct preventive maintenance by developing models to predict equipment health, analyzing smart meter data to understand where to prioritize repairs. PG&E credits Foundry with helping the company manage the risk of wildfires caused by electrical equipment, ultimately keeping Californians safer.
Palantir has partnered with many utilities to deploy Foundry across the value chain, from emergency operations to predictive maintenance to EV planning. To learn more about how Foundry can help your utilitys decision-making capabilities, check out our website and meet us at DISTRIBUTECH in Dallas, TX from May 23-25.
1) Jones, Matthew W., Adam Smith, Richard Betts, Josep G. Canadell, I. Colin Prentice, and Corinne Le Qur. "Climate change increases the risk of wildfires." ScienceBrief Review 116 (2020): 117. https://www.preventionweb.net/files/73797_wildfiresbriefingnote.pdf
Continue reading here:
Data Science and Machine-Learning Platforms Market Key Segments to Play Solid Role In A Booming Industry| Databricks, TIBCO Software, MathWorks The…
Latest Study on Industrial Growth of Global Data Science and Machine-Learning Platforms Market 2021-2027. A detailed study accumulated to offer Latest insights about acute features of the Data Science and Machine-Learning Platforms market. The report contains different market predictions related to revenue size, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market. It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary and SWOT analysis.
The Major Players Covered in this Report: SAS, Alteryx, IBM, RapidMiner, KNIME, Microsoft, Dataiku, Databricks, TIBCO Software, MathWorks, H20.ai, Anaconda, SAP, Google, Domino Data Lab, Angoss, Lexalytics, Rapid Insight & ?Data Science and Machine-Learning PlatformsMarket Scope and Market Breakdown
Data Science and Machine-Learning Platforms Market Study guarantees you to remain / stay advised higher than your competition. With Structured tables and figures examining the Data Science and Machine-Learning Platforms, the research document provides you a leading product, submarkets, revenue size and forecast to 2027. Comparatively is also classifies emerging as well as leaders in the industry.Click To get SAMPLE PDF of Data Science and Machine-Learning Platforms Market (Including Full TOC, Table & Figures) @https://www.htfmarketreport.com/sample-report/4053689-global-data-science-and-machine-learning-platforms-market-7
This study also covers company profiling, specifications and product picture, sales, market share and contact information of various regional, international and local vendors of Global Data Science and Machine-Learning Platforms Market. The market proposition is frequently developing ahead with the rise in scientific innovation and M&A activities in the industry. Additionally, many local and regional vendors are offering specific application products for varied end-users. The new merchant applicants in the market are finding it hard to compete with the international vendors based on reliability, quality and modernism in technology.
Read Detailed Index of full Research Study at @https://www.htfmarketreport.com/reports/4053689-global-data-science-and-machine-learning-platforms-market-7
The titled segments and sub-section of the market are illuminated below:In-depth analysis of Global Data Science and Machine-Learning Platforms market segments by Types: , Open Source Data Integration Tools & Cloud-based Data Integration ToolsDetailed analysis of Global Data Science and Machine-Learning Platforms market segments by Applications: on, Small-Sized Enterprises, Medium-Sized Enterprise, Large Enterprises, Channel, By Channels, Market has been segmented into, Direct Sales, Distribution Channel, Regional & Country Analysis, North America Country (United States, Canada), South America (Brazil, Argentina, Peru, Chile, Rest of South America), Asia-Pacific (China, Japan, India, South Korea, Australia, Singapore, Malaysia, Indonesia, Philippines, Thailand, Vietnam, Others), Europe (Germany, United Kingdom, France, Italy, Spain, Switzerland, Netherlands, Austria, Sweden, Norway, Belgium, Rest of Europe) & Rest of World [United Arab Emirates, Saudi Arabia (KSA), South Africa, Egypt, Turkey, Israel, Others]
Major Key Players of the Market:SAS, Alteryx, IBM, RapidMiner, KNIME, Microsoft, Dataiku, Databricks, TIBCO Software, MathWorks, H20.ai, Anaconda, SAP, Google, Domino Data Lab, Angoss, Lexalytics, Rapid Insight & ?Data Science and Machine-Learning PlatformsMarket Scope and Market Breakdown
Regional Analysis for Global Data Science and Machine-Learning Platforms Market: APAC (Japan, China, South Korea, Australia, India, and Rest of APAC; Rest of APAC is further segmented into Malaysia, Singapore, Indonesia, Thailand, New Zealand, Vietnam, and Sri Lanka) Europe (Germany, UK, France, Spain, Italy, Russia, Rest of Europe; Rest of Europe is further segmented into Belgium, Denmark, Austria, Norway, Sweden, The Netherlands, Poland, Czech Republic, Slovakia, Hungary, and Romania) North America (U.S., Canada, and Mexico) South America (Brazil, Chile, Argentina, Rest of South America) MEA (Saudi Arabia, UAE, South Africa)
Furthermore, the years considered for the study are as follows:Historical year 2015-2020Base year 2020Forecast period** 2021 to 2027 [** unless otherwise stated]
**Moreover, it will also include the opportunities available in micro markets for stakeholders to invest, detailed analysis of competitive landscape and product services of key players.
Buy Latest Edition of Market Study Now @https://www.htfmarketreport.com/buy-now?format=1&report=4053689
Key takeaways from the Global Data Science and Machine-Learning Platforms market report: Detailed considerate of Data Science and Machine-Learning Platforms market-particular drivers, Trends, constraints, Restraints, Opportunities and major micro markets. Comprehensive valuation of all prospects and threat in the In depth study of industry strategies for growth of the Data Science and Machine-Learning Platforms market-leading players. Data Science and Machine-Learning Platforms market latest innovations and major procedures. Favorable dip inside Vigorous high-tech and market latest trends remarkable the Market. Conclusive study about the growth conspiracy of Data Science and Machine-Learning Platforms market for forthcoming years.
What to Expect from this Report On Data Science and Machine-Learning Platforms Market:1. A comprehensive summary of several area distributions and the summary types of popular products in the Data Science and Machine-Learning Platforms Market.2. You can fix up the growing databases for your industry when you have info on the cost of the production, cost of the products, and cost of the production for the next future years.3. Thorough Evaluation the break-in for new companies who want to enter the Data Science and Machine-Learning Platforms Market.4. Exactly how do the most important companies and mid-level companies make income within the Market?5. Complete research on the overall development within the Data Science and Machine-Learning Platforms Market that helps you elect the product launch and overhaul growths.
Enquire for customization in Report @https://www.htfmarketreport.com/enquiry-before-buy/4053689-global-data-science-and-machine-learning-platforms-market-7
Detailed TOC of Data Science and Machine-Learning Platforms Market Research Report-
Data Science and Machine-Learning Platforms Introduction and Market Overview Data Science and Machine-Learning Platforms Market, by Application [on, Small-Sized Enterprises, Medium-Sized Enterprise, Large Enterprises, Channel, By Channels, Market has been segmented into, Direct Sales, Distribution Channel, Regional & Country Analysis, North America Country (United States, Canada), South America (Brazil, Argentina, Peru, Chile, Rest of South America), Asia-Pacific (China, Japan, India, South Korea, Australia, Singapore, Malaysia, Indonesia, Philippines, Thailand, Vietnam, Others), Europe (Germany, United Kingdom, France, Italy, Spain, Switzerland, Netherlands, Austria, Sweden, Norway, Belgium, Rest of Europe) & Rest of World [United Arab Emirates, Saudi Arabia (KSA), South Africa, Egypt, Turkey, Israel, Others]] Data Science and Machine-Learning Platforms Industry Chain Analysis Data Science and Machine-Learning Platforms Market, by Type [, Open Source Data Integration Tools & Cloud-based Data Integration Tools] Industry Manufacture, Consumption, Export, Import by Regions (2015-2020) Industry Value ($) by Region (2015-2020) Data Science and Machine-Learning Platforms Market Status and SWOT Analysis by Regions Major Region of Data Science and Machine-Learning Platforms Marketi) Global Data Science and Machine-Learning Platforms Salesii) Global Data Science and Machine-Learning Platforms Revenue & market share Major Companies List Conclusion
Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, MINT, BRICS, G7, Western / Eastern Europe or Southeast Asia. Also, we can serve you with customize research services as HTF MI holds a database repository that includes public organizations and Millions of Privately held companies with expertise across various Industry domains.
About Author:HTF Market Intelligence consulting is uniquely positioned empower and inspire with research and consulting services to empower businesses with growth strategies, by offering services with extraordinary depth and breadth of thought leadership, research, tools, events and experience that assist in decision making.
Contact US:Craig Francis (PR & Marketing Manager)HTF Market Intelligence Consulting Private LimitedUnit No. 429, Parsonage Road Edison, NJNew Jersey USA 08837Phone: +1 (206) 317 1218[emailprotected]
View post:
Informatica World 2022 Showcases Global Customer Adoption of the Intelligent Data Management Cloud (IDMC) – Yahoo Finance
Customers Volvo, Pepsi, ADT, Telus and FreddieMac to share Cloud Modernization success with IDMC at Informatica's annual customer conference
Informatica deepens partnerships with AWS, Microsoft, Google Cloud, Snowflake and Databricks to accelerate cloud migrations with IDMC
IDMC powered by Informatica's AI engine, CLAIRE processes 32 trillion transactions per month on the cloud
REDWOOD CITY, Calif., May 23, 2022 /PRNewswire/ --Informatica(NYSE:INFA), an enterprise cloud data management leader will be kicking off its annual customer conference, Informatica Worldas a hybrid event in Las Vegas today with over 60-plus global customers presenting how the Informatica Intelligent Data Management Cloud(IDMC) has accelerated their cloud journeys and driven better business outcomes.
Informatica Corp. (PRNewsfoto/Informatica Corp.)
IDMC was launched at Informatica World 2021 as the industry's first end-to-end data management platform designed to help businesses truly innovate with their data on any system, any cloud, multi-cloud and multi-hybrid and for all users across the enterprise. IDMC is a comprehensive, AI-powered, cloud data management platform offering over 200-plus intelligent cloud services to help organizations reimagine and redefine how they can innovate business functions from customer experience, e-commerce, supply chain, manufacturing to analytics and data science, with a mission to create a world where every organization's data is poised for greatness and empowered to deliver outcomes.
Since its launch last year, IDMC has been recognized as a leader in iPaaS, 2021's best new product of the year by the Business Intelligence group and also recognized in Fast Company's most innovative list in the enterprise category. Informatica's cloud annual recurring revenue has grown 43% year-over-year as of March 31, 2022 with global customers across a wide variety of industry verticals investing in the IDMC platform. CLAIRE, Informatica's AI engine powers the IDMC platform to process over 32 trillion transactions (as of March 31, 2022) on the cloud each month and that number is doubling every six to 12 months.
Story continues
At Informatica World this week, the company will be unveiling new industry-verticalized capabilities for IDMC to help customers in the financial services, healthcare and life sciences address industry-specific data management and governance needs. With over $1Billion in investments in R&D over the last six years, Informatica has designed and delivered an iPaaS cloud platform that is helping customers like Pepsi, Volvo, ADT, Telus, FreddieMac, Blue Cross Blue Shield of Kansas City and Hershey, modernize to the cloud with IDMC. Over 3,000 attendees joining the hybrid event will hear from these customers at Informatica World on how IDMC is helping with their data-led business transformation.
Informatica's rich ecosystem of partners which include all the major hyperscalers, AWS, Microsoft, Google Cloud, Snowflake, Databricks as well as GSI partners like Accenture, Deloitte, CapGemini, Wipro and Infoverity has deep integrations with the IDMC platform to bring best-of-breed cloud data management solutions to joint customers. The company will be announcing deeper product integrations with major hyperscalers this week at Informatica World and will also be featuring Scott Guthrie, SVP, Cloud +AI Group, Microsoft; Thomas Kurian, CEO, Google Cloud; Matt Garman, SVP, Sales, Marketing and Global Sales at AWS as keynote speakers.
"When we embarked on the company's transformation six years agao, we completely rearchitected our entire product portfolio to be cloud-first, cloud-native,"said Amit Walia, CEO, Informatica. "Today with IDMC, we bring an unparalleled cloud data management platform that is helping our customers build an intelligent data enterprise to stay competitive in a digital-first economy. I am very excited to see the fantastic lineup of speakers including our customers across multiple industries and our global partners talking about how they are driving business value and transforming how they run their business with IDMC, at Informatica World."
Join Informatica World virtually to listen to cloud and data industry leaders and visionaries at http://www.informaticaworld.com
About InformaticaInformatica (NYSE: INFA), an Enterprise Cloud Data Management leader, empowers businesses to realize the transformative power of data. We have pioneered a new category of software, the Informatica Intelligent Data Management Cloud(IDMC), powered by AI and a cloud-first, cloud-native, end-to-end data management platform that connects, manages, and unifies data across any multi-cloud, hybrid system, empowering enterprises to modernize and advance their data strategies. Over 5,000 customers in more than 100 countries and 85 of the Fortune 100 rely on Informatica to drive data-led digital transformation.
Contact: Informatica Public Relationsprteam@informatica.com
Cision
View original content to download multimedia:https://www.prnewswire.com/news-releases/informatica-world-2022-showcases-global-customer-adoption-of-the-intelligent-data-management-cloud-idmc-301552667.html
SOURCE Informatica
Read the original here: