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Ford, With a Renewed Focus on Innovation, Recruits Globally Detroit Jewish News – The Jewish News

In the biography of Steve Jobs on Gil Gur Aries bookshelf, author Walter Isaacson shares a story about the day Jobs unveiled the Macintosh computer. A reporter from Popular Science asked Jobs what type of market research he had done. Jobs responded by scoffing, Did Alexander Graham Bell do any market research before he invented the telephone?

Fords CEO Jim Farley, with decades of experience in marketing, is a strong believer in innovation but not the kind where you just trust your gut. His vision is for Ford to be a data-first company and to keep data in mind for everything the company does, both miniscule and major. Since becoming president and CEO in October 2020, hes been recruiting near and far to help the company determine how data can guide the direction of the companys future. These new recruits have come from noted names like Apple, the technology company founded by Jobs.

Gil Gur Arie, who became an executive at Ford on May 1, 2020, is another such recruit. He now serves as the chief data and analytics officer for Ford. His first day at the office, during the start of the pandemic, ended up also being his last day at the office as he transitioned quickly to remote work.

The Israeli native now has a home base in West Bloomfield, along with his wife, Hagit, and four children (ages 8, 10, 14 and 17). As I reflect on my first year here with my family (relocating to Michigan),it was tough at the beginning. I would say the pandemic definitely didnt help connecting, but I found a nice community here in West Bloomfield. The Jewish community as well. We got a bunch of challahs the very first Friday and my wife, who is a great cook and baker, made some for the neighbors as well, Gur Arie told the Jewish News.

I would say, despite the pandemic, we have felt a warm welcome and a sense of belonging, he added. His children, who began school on Zoom, now are back to in-person learning in the Bloomfield Hills School District.

Gur Arie and his wife decided to tap into downtown areas on the weekends. So far, theyve hit several locations, including Plymouth, Birmingham, Novi and Northville.

Wesley Sherwood, on the mobility communications team at Ford, mentioned that Ford had the distinction of having a prior CEO from the Jewish community, Mark Fields, as well as a previous treasurer within the community, Neil Schloss. With Gils appointment, he is now the most prominent Israeli and Jewish executive at the firm and also one of the most prominent Israel executives within the automotive world.

Ford, with the largest revenue and employee base of any company in Michigan, has a history of global recruitment in recent decades. Norman Lewis, as one such example, previously served as the director of information systems at the company after having impressed the U.S. executives with his work around computer systems in the European office of Ford. The South Africa native, who made aliyah to Israel and completed a Ph.D. at Hebrew University, continues to reside in Metro Detroit.

Gur Aries role is focused on transforming Ford into a data-led business, which includes modernizing all aspects of the company, including translating connected vehicle data into quicker actions for customers.

Sherwood shares that Gur Aries team is leading Fords advanced artificial intelligence applications and is integrating artificial intelligence to improve its manufacturing efficiencies as well as its massive logistics and shipping operations. Fords team of global data and analytics members now spans 1,000 data scientists globally. The objectives for the team focus on key company priorities including the development of mobility, electrification, connective vehicles and autonomous driving technologies.

A key objective for the global data team has been on logistics over the last year with significant shipping challenges confronting the industry. Gur Arie mentioned how a year ago, Ford launched a system that uses historical data and machine learning algorithms to optimize shipments around the globe, which spans over 600,000 different parts, to send less freight while making sure the parts arrive at the right time. While optimizing all these moving parts across 80 plants has been challenging, the new system already saves more than $20 million a year.

Gur Arie is a retired colonel in the Israeli Military Intelligence Corps, Unit 8200, who comes to Detroit with more than two decades of experience in data science, research and development, cybersecurity and intelligence technologies experience. His work in the IDF included establishing a technological unit with more than 1,000 employees, advancing hundreds of solutions within the intelligence community and establishing the profession of data engineer within the IDF where he was appointed as the data science representative.

Im proud and humble to be part of the leadership of this company, coming from my background. Ford is highly open to diversity of thoughts, diversity of culture, trying to get to the best decision on day-to-day meetings and, overall, on the strategy creation. So, Im proud to be part of that, Gur Arie said.

I do see the connection to the Israeli ecosystem and in using the research center in Israel to tap into the local talent. So, Im quite optimistic. I hope Im not the last one from an Israeli perspective into the automotive business. I hope others will follow.

Gur Arie articulated what he witnessed firsthand since he began: The pandemic changed the face of the relationship between workers and employers. The company, more than ever before, is looking for talent wherever it is and to connect with the full global ecosystem even if it means hiring remote workers.

Gur Arie said that he and his boss are passionate about talent in the tech area whether they be in Israel, India, Europe, China or other locations.

Gur Arie, 46, has come a long way from his upbringing in the Tel Aviv metropolitan area of Hod Hasharon. Gil, whose name is often given in Hebrew by parents that want their children to embody a bright promise, not only carries with him a joyful demeanor but also a sense of promise for the region he now resides in.

We see great opportunities here in Detroit.

The Ford Jewish group is part of the Ford Interfaith Network (FIN) Employee Resource Group, founded in 2000 as one of the original faith affinity groups. Prior to COVID restrictions, the FIN Jewish Group typically met for a lunch-and-learn type of event several times a year, inviting a guest speaker such as Rabbi Yisrael Pinson of Chabad in the D to lead a discussion.

The FIN Jewish Group also participates in Ford Interfaith events such as the National Day of Prayer, the monthly Interfaith Discussion Forum (where each faith presents a short explanation of its teachings on the selected topic) and a FIN annual community service event.

The Ford Fund (Fords charitable arm) has supported several Jewish charities and events in Southeast Michigan area over the years, including Yeshiva Beth Yehudahs annual dinner (Mark Fields was honored one year), the Jewish Federation of Metropolitan Detroit and support for the Yad Ezra food bank through grants and the Ford Volunteer Corps.

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Axtria Recognized Again For Its High-Trust, High-Performance Culture By The Great Place To Work Institute – GlobeNewswire

Berkeley Heights, New Jersey, Jan. 06, 2022 (GLOBE NEWSWIRE) -- Axtria, a global leader in cloud software and data analytics for the life sciences commercial business, is honored to be Great Place to Work-Certified. This certification is a significant achievement, and Axtria has doubled the reward with two designations. 2021 marks the second year Axtrias US offices have been awarded this certification, while Axtrias India offices are counting 2021 as their fourth certification in a row.

These Great Place to Work certifications, along with a previous Great Place to Work in IT & IT-BPM recognition in November 2021, are a tremendous testimony of Axtrias culture. The high-trust, high-performance culture helps Axtrias employees consistently grow and continuously impact the life sciences industry. Great Place to Work is the global authority on workplace culture and leadership behaviors. Certification is a mark of a great employee experience.

These rankings are a validation of every Axtrians attitude to be the best at what they are, said Jassi, President and CEO, when he heard the news. I would like to thank each Axtrian for their commitment and passion personally, he said.

Great Place to Work Institute is a global authority on workplace culture and has studied employee experience and people practices across organizations for over three decades. Every year, more than 10,000 organizations from over 60 countries partner with the Great Place to Work Institute for assessment, benchmarking, and planning of actions to strengthen their workplace culture.

Axtria has met the criteria of the Great Place to Workgold standard, excelling on the five dimensions of the Trust Model - Credibility, Respect, Fairness, Pride, and Camaraderie.

From the moment an Axtrian is onboarded, our people-centric processes kick in to ensure that we are not just connected for work, but also as people, said Shikha Singhal, Head of People Practices. Our attitude of endeavoring to be the best in every aspect of an individuals growth new and exciting work, high-performance culture, and every policy designed to keep our peoples well-being at the center has been the essence of Axtrias journey. We dont need to chase the milestones like this one. Our good work brings them along the way!

This certification is one of the most prestigious achievements for any organization across the globe. For an organization to get certified, 70% or more of its employee respondents must rate the organization as a great workplace through the Great Place to Work Institute.

Learn more about Axtria in Great Place To Works company directory or to discover more about Axtrias state-of-the-art products and solutions, please visitwww.axtria.com.

About AxtriaAxtria is a global provider of cloud software and data analytics to the life sciences industry. Axtria helps life sciences companies transform the product commercialization journey to drive sales growth and improve healthcare outcomes for patients. Axtria has a strong focus on sales and marketing operations in the life sciences industry. With customers in over 75 countries, Axtria is one of the biggest global commercial solutions providers in the life sciences industry.

Axtria helps customers improve operational effectiveness with solutions that leverage Big Data, cloud software, predictive analytics, and machine learning. Axtria DataMAx, Axtria InsightsMAx, Axtria SalesIQ, and Axtria CustomerIQare cloud-based software platforms that enable customers to efficiently manage data, leverage data science to deliver insights for sales and marketing planning, and manage end-to-end commercial operations.For more information, go towww.axtria.com.Connect with Axtria:

LinkedIn:https://www.linkedin.com/company/axtria/

Facebook:https://www.facebook.com/AxtriaInc

Twitter:https://twitter.com/Axtria

Instagram:https://www.instagram.com/lifeataxtria/

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Axtria,Axtria SalesIQ, Axtria CustomerIQ,Axtria InsightsMAx,and Axtria DataMAxare trademarks or registered trademarks of Axtria. Other names may be trademarks of their respective owners.

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Data Science & Analytics Predictions for 2022 – Datamation

The world of data science is expanding as new technologies and use cases push innovation to meet the rising demand for data-driven business outcomes.

2022 promises several nascent and growing data initiatives across segments of the market and within enterprises.

Read on to learn what a group of experts predict we can expect in data science and analytics in 2022:

Also read: Big Data Trends in 2022 and The Future of Big Data

Everything from machine learning (ML) models to network security monitoring tools to enterprise resource planning (ERP) software requires access to large pools of big data.

And while many organizations have been collecting and finding the data they need to fuel these tools, they have not always placed data quality management at the forefront of their priorities.

2021 was one of the first years when data quality improvement started to receive focus, but still, many organizations do not feel confident that their data is clean or usable.

Tend Yogurtu, CTO at Precisely, a big data and data quality company, believes that data quality initiatives will not only continue in the new year but will grow as many organizations expressed concerns about the operationality of their data in 2021.

Data quality and data integrity will continue to be major focuses for organizations in 2022, Yogurtu said. Organizations are becoming more data-driven, which seems obvious, but now the pressing need is ensuring the quality of that data over quantity.

And while most enterprises have established a basic foundation for data-driven decision making, they also report struggles with achieving data integrity at scale. In fact, 80% of chief data officers surveyed by Corinium have data quality issues that interfere with integration.

Businesses will enrich their data by adding context from third-party data and eliminating data silos, allowing better quality data for their organization.

Learn about data analytics trends: Top Data Analytics Trends

A number of companies have permanently or semi-permanently shifted their teams to remote work.

There are definite pros and cons to this workplace shift, with one of the most crucial drawbacks being the limited network security infrastructure and visibility when workers are on their home networks and personal devices.

Brian Robertson, senior product marketing manager for RSA NetWitness, a top security information and event management (SIEM) and extended detection and response (XDR) security solutions provider, believes there are several key threat detection technologies and techniques that can help companies improve their remote worker security posture and reduce their risk of a cybersecurity breach.

Many organizations are now supporting remote work, so now cybercriminals are finding ways to exploit that through either infiltration techniques or ransomware, Robertson said.

There are multiple approaches to address this, but a couple seem to be gaining a lot of momentum. The first is extended detection and response, and the second, which some look at as complimentary to XDR and some view it as part of XDR, is SOAR.

XDR is designed to detect threats, regardless of where they live, by using endpoint and network data and applying advanced analytics to it. We also feel that XDR should incorporate vast threat intelligence and leverage automation and orchestration to act against identified threats.

SOAR is designed to leverage vast threat intelligence to identify threats, and those threat intelligence sources can be open threat feeds, subscription threat feeds, industry intelligence, and even crowd-sourced intelligence. However, having all this intelligence is only useful if you can take direct and focused action against it, either by orchestrating activities across the security team or through automation performed by an orchestration and automation solution.

Learn about cybersecurity trends: Top Emerging Cybersecurity Trends

Natural language processing (NLP) is continuing to take new shapes as different businesses discover valuable applications of artificial intelligence (AI) technology. Several experts believe that NLP will grow across industries in the next year.

Ali Siddiqui, chief product officer at BMC, a large IT consulting and services firm, said more companies will recognize the value-add of NLP-driven customer service bots in the near future.

We can expect natural language processing to grow in the future, Siddiqui said.

Chatbots built on NLP improve efficiency and provide better customer experiences. They provide fast, relevant answers and streamline communication by closing feedback loops and improving productivity gains, allowing employees to spend time on more meaningful tasks.

Ranjan Goel, VP of products at LogicMonitor, a cloud-based infrastructure monitoring platform, said NLP has started and will continue to help with unstructured data and security management.

Natural language processing has advanced significantly in consumer use cases and is now moving into enterprise products to help with unstructured data, Goel said. Its enterprise use cases vary, including finding anomalies in the logs, cluster logs, and alerts.

Jean-Franois Gagn, head of AI product and strategy at ServiceNow, an IT service management (ITSM) and service desk software provider, said low-code/no-code applications are starting to be developed through natural language interfaces.

Fundamental research in text to SQL is paving the way for software applications to be built through natural language interfaces, without the need for any knowledge of how to write code, Gagn said. A user describes the application they want to an intelligent chatbot, along with the data to use for training, and AI can take care of building the application on the fly.

Learn about artificial intelligence trends: Artificial Intelligence Trends & Predictions

Many Internet of Things (IoT) developers and customers have explored different IoT applications for a while now, but piecemeal IoT engagement has not driven true business gains for most users.

Some experts predict now that many companies are comfortable with the basic idea of and need for IoT, theyll begin to truly strategize how IoT can meet their business goals.

Vishal Gupta, chief information and technology officer at Lexmark International, an IoT and cloud imaging solutions company, believes many organizations, including those that do not have in-house IoT expertise, will find ways to create actionable IoT products that optimize their existing products and services.

I view IoT innovation as being more integrated to key business outcomes, Gupta said. Until now, there has been a mentality to push technology forward, even if the results didnt necessarily produce any tangible benefit for the business. In other words, innovation for innovations sake.

The problem, as identified by McKinsey, is that a majority of these IoT experiments, 84%, to be exact, get stuck in the pilot phase. The foundation already exists with cloud, 5G, artificial intelligence, and machine learning. With the pandemic, there is a pressing need to make IoT matter.

In 2022, organizations who cannot build their own IoT solutions will partner with experts in software or services to start realizing predictable, repeatable, and measurable outcomes. If they dont, they risk falling behind.

Learn about IoT trends: Top Internet of Things (IoT) Trends: The Future of IoT

Artificial intelligence use cases have grown across industries in process automation, cybersecurity, and customer service, to name a few.

But AI has typically been used as a supportive technology for legacy solutions, like workflows, campaigns, and dashboards. Few companies have used AI to completely replace these technologies.

Jeff Aaron, VP of enterprise marketing for Juniper Networks, a top global networking company, believes that 2022 may see AI take over standby technologies in network monitoring, such as administrative dashboards.

AI-driven assistants will largely take over the monitoring and troubleshooting process in networks, Aaron said. They say video killed the radio star, and now artificial intelligence, natural language processing, and natural language understanding (NLU) are going to kill the dashboard star.

Looking ahead, the days of hunting and pecking or looking at charts will go by the wayside, because you can now type in a question and get an answer or have issues flagged for you and in some cases, actually fixed on their own known as self-driving.

Youre going to see a trend around AI-driven assistants replacing dashboards and changing the way we troubleshoot, essentially eliminating the swivel chair interface.

Learn about networking trends: Latest Trends & Developments in Networking

Data fabrics intend to connect and eliminate silos across different enterprise data storage setups.

In 2022, experts expect that data fabrics will become a planned, fleshed-out initiative for several companies, especially as their big data management needs grow in size and complexity.

Krishna Subramanian, president, co-founder, and COO of Komprise, an unstructured data management-as-a-service (DMaaS) company, believes that data fabrics will be solidified as more companies need a better solution for managing unstructured data.

Data fabric is still a vision, Subramanian said. It recognizes that your data is living in a lot of places, and a fabric can bridge the silos and deliver greater portability, visibility, and governance.

Data fabric research has typically focused on semi-structured and structured data. But 90% of the worlds data now is unstructured think videos, X-rays, genomics files, log files, sensor data and this data has no defined schema.

Data lakes and data analytics applications cannot readily access this dark data locked in files. So data fabric technologies need to bridge the unstructured data storage, file storage and object storage, and data analytics platforms data lakes, ML and natural language processors, image analytics, etc.

Analyzing unstructured data is becoming more important as machine learning relies on unstructured data. Data fabric technologies need to be open, standards-based, and look across environments.

In 2022, the data fabric should move from being a vision to a set of architectural principles of data management. Technology vendors need to incorporate unstructured data into their data fabric architectures, given its rising importance and sheer magnitude.

Learn more: What is a Data Fabric?

The job market is a job seekers market, where the demand for new staff makes it possible for candidates to ask for higher salaries and better benefits.

For technology workers in particular, experts are predicting that organizations will not only need to offer higher salaries in 2022, but also benefits that support their professional development and personal health.

Dice, a top technology career marketplace and research company, recently released Tech Hiring Trends for 2022 with this explanation of what technologists will expect from job offers in the next year.

But compensation isnt all technologists will be asking for. In the 2021 Dice Tech Salary Report, released earlier this year, we reported on the top benefits technologists currently have and the benefits they want. As to be expected, traditional benefits, such as health insurance, paid vacation days and 401(k) matching/pension topped the list, but there were some significant gaps midway down the list in what technologists desire and what they are receiving.

There was a 23% gap for training and education and a 17% gap for stock programs, hinting at where employers could differentiate their offerings to attract and retain tech talent. There was also a 15% gap in flexible schedule options, which is another lesson learned in 2021 that we discuss in the Flexibility in Work Structure section.

We believe these are the gaps that industrious employers, and especially those who may not have the resources of larger competitors, can look to fill through new programs or adjustments to current initiatives, giving them an advantage in conversations with technologist talent.

Learn about data science career trends: Data Science Job Market: Build a Career in Data Science

The cloud computing industry continues to grow into new sectors and lines of business, and at the same time, cloud computing continues to shift its identity to meet new corporate challenges and priorities.

Vishwas Manral, chief architect of cloud at McAfee Enterprise and co-chair of the Cloud Security Alliance, thinks that 2022 will likely bring change to the cloud security vendor landscape, specifically through consolidation.

There are currently way too many cloud security tools on the market and because of this, we are now seeing a trend towards consolidation, Manral said.

Because of this, there is now a large number of mergers and acquisitions happening in the cloud security market with even more consolidation on the horizon.

Learn about cloud security trends: Top Cloud Security Trends

New global data regulations and deadlines for compliance are already planned for the next few years and more continue to join the list.

Many companies have traditionally focused on their own industries regulations, but as global companies continue to move to new markets with stringent policies, localized data compliance and management will be even more necessary in 2022.

Sovan Bin, CEO of Odaseva, a data management and compliance solution designed for Salesforce, said more global regulations will require action by companies.

Privacy regulation will continue to go global while requiring increasing localized implementation and storage, Bin said.

2021 saw the China Personal Information Protection Law (PIPL) passed at astonishing speed, cementing this trend. The extent of the requirements will become clearer as implementing regulations are introduced in 2022.

Read next: Top Data Management Platforms and Software

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Top Data Science Jobs to Apply for in the First Week of 2022 – Analytics Insight

Apply in these data science jobs to accelerate career growth in 2022

The utilization of data dominates modern businesses. Data science, analytics, and big data have established themselves as a fundamental part of all business enterprises. The integration of data technologies has generated valuable information from the collected data and has also enabled operational efficiency and minimized the risks of facing losses through accurately forecasting the future risks. Quite obviously, this has led to an increase in the number of data science jobs. Companies are seeking skilled and highly motivated individuals who can leverage advanced technologies like AI, ML, IoT, and big data to attain organizational objectives. In this article, we have listed such top data science jobs that data professionals can apply for in the first week of January 2022.

Offered by:Everseen

Location:Hyderabad, India

Offered by:Narayana Health

Location:Bengaluru, India

Offered by:Times Internet

Location:Noida, India

Offered by:Dell Technologies

Location:Bengaluru, India (Hybrid)

Offered by:Bridgetree Research Services PVT. Ltd.

Location:Bengaluru, India (Remote)

Offered by:EY

Location:Kolkata, India

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How Data Analytics and AI Solve the Toughest Global Problems – HackRead

Can technologies combining data analytics and artificial intelligence save the world from its growing problems? Yes, it is possible!

Data has an essential place in the structure of the modern world. Every day, Internet users generate a massive amount of data by creating and sharing posts and making search queries. However, this is only a tiny part of the global data landscape, which continues to grow due to the availability of various passive data collection methods using low-cost information-sensing devices.

Up-to-date processing and analysis methods make it possible to get valuable information from the vast amount of generated and stored data. But why do we need all of this?

Information plays a vital role in improving the life of society, developing sustainable production, and preserving the environment. People from different industries are interested in using data analytics because it helps them make better decisions.

In addition, the data allows predicting various events and minimizing damage. For example, EOS Data Analytics offers data-driven solutions for agriculture and forestry that provide extensive monitoring, control, and improved management capabilities. In this article, we will talk about how data analytics and artificial intelligence technologies are helping humanity to overcome global challenges.

Data analytics and artificial intelligence are used to predict and minimize the spread of disease. The collected historical data is transmitted to AI to recognize patterns and trends and then predict the spread of the disease. To date, this primarily concerns the Covid-19 pandemic.

Data application enables minimizing the spread by taking appropriate preventive measures. Tracking the movement of people today is easier than ever, which helps to analyze and predict the viruss path. Thus, it becomes possible to control and prevent the spread of the disease in a specific region. An essential function of artificial intelligence is also to predict virus mutations and help develop vaccines.

The energy industry is facing a critical challenge. Energy must be supplied by demand, while humanity consumes a massive amount. From 2000 to 2018, energy consumption grew by 3% per year. Artificial Intelligence offers cost-effective solutions for energy consumption and clean energy production. This technology also has great potential in developing a more sustainable and efficient industry.

The planets population is growing by 1.1% every year. As a result, the demand for food products exceeded the supply. Poverty and natural disasters are the leading causes of the global food crisis. Agriculture and other industries need to follow a sustainable path to tackle this challenge, and AI technology can help. It gives a chance to predict food shortages and help achieve food security.

Artificial intelligence analyzes data faster than human experts at a much lower cost. Climate change leads to lower yields, but AI can help here too, predicting natural disasters and helping to prevent and manage them.

In addition, data analysis using artificial intelligence helps farmers find the most vulnerable areas and determine the most optimal planting sites. Also, this technology makes a significant contribution to the development of more resistant crops.

Scientists are now exploring the possibilities of applying data analytics to climate change research. Based on the data obtained, experts create models of the Earth, taking into account existing factors, including the rate of melting of glaciers and sea level and then compare them with historical climatic data.

Thus, scientists can identify patterns and accurately predict the extreme climate effects that will worsen in the next few years. Data analysis also provides an opportunity to determine the effectiveness of various measures to accelerate or mitigate the impact of climate change.

The Earths growing population needs more food and water for drinking, cooking, watering plants, bathing, and cleaning homes. For this reason, it is necessary to find new methods to promote more efficient use of water.

Data science and analytics can give service providers the tools for water use measuring and control. Data analytics technologies can help manage resource allocation better and reduce environmental impact.

Innovations in data analysis enable monitoring water resources in real-time, determining their quality and safety for drinking. It is possible to predict the quality of the water and assess its suitability for various purposes in the future, considering rainfall, pollution, and other factors. Regional and local water supply problems can be detected using data science. It is crucial for preventing various epidemics and diseases that spread through the chiefs supply systems.

Data analytics and artificial intelligence allow the opportunity to gain valuable information about various phenomena and their issues. Using this information enables us to identify patterns, monitor changes, predict and minimize the consequences of multiple events related to climate, resource consumption, public health, etc. These are valuable tools to ensure a more sustainable future for our planet and the people living on it.

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E Source acquires AAC Utility Partners to help utilities accelerate their transformation to digital, data-driven organizations – PRNewswire

BOULDER, Colo., Jan.4, 2022 /PRNewswire/ -- ESource, the solutions leader for US and Canadian utilities and cities that combines unparalleled domain expertise with best-in-class industry research, data science software, and consulting services, continues to expand its offerings through the acquisition of AAC Utility Partners.

AAC is a vendor-independent consulting firm providing services exclusively to utilities for the transformation of customer information systems and other critical operational systems. AAC assists clients in the areas of strategic software assessment, selection, project implementation, and cloud consulting.

"There is no substitute for AAC's proven expertise and track record of success in implementing large technology projects that are foundational to becoming a modern, digital utility," says ESource CEO Ted Schultz. "When combined with ESource's technology planning and implementation capabilities supporting AMI and GIS, plus our ability to make data useful using our research and predictive data science products, the addition of AAC further enhances our ability to drive value for our utility clients by delivering what's next."

AAC is the eighth company ESource has acquired in the past 24 months. The acquisition is an important step in ESource's continued effort to build a comprehensive, tech-enabled, customer-first portfolio of digital, data-driven solutions that help utilities manage their rapidly evolving customer, infrastructure, and business needs.

Since 2005, AAC has guided electric, gas, water, wastewater, and multiservice utilities across North America through successful technology projects. AAC's client base ranges from municipal utilities to investor-owned utilities with seven-figure customer counts.

"AAC Utility Partners developed the award-winning, NavigateOne methodology and tools because utilities needed superior processes to find success with their technology projects," says Edwin Crow, managing partner of AAC. "Over the years, we've had the pleasure of completing over 75 fully functional projects. By joining the ESource team, we'll be able to bring our capabilities to more utilities to help them become technology leaders."

"This acquisition brings to life an important connection from hardware to software to predictive data science," says Rob Langley, managing partner and cofounder of Align Capital Partners, which owns ESource. "This powerful combination offers utilities a partner that can connect the dots like no other."

About ESourceE Source is a leading partner to more than 500 electric, gas, and water utilities and municipalities, and their partners, across the US and Canada. We provide data science, market research, benchmarking, and consulting services. Our 35 years of technology validation, market assessment, program design, and customer experience expertise help clients make informed, data-driven decisions; plan for tomorrow's infrastructure needs; strengthen customer relationships; and meet critical business objectives while becoming more innovative and responsive in the rapidly evolving market.

About Align Capital PartnersAlign Capital Partners is a growth-orientedprivate equity firm that partners with business owners and management teams to create shared success. ACP manages $775 million in committed capital with investment teams in Cleveland and Dallas. ACP brings experience and resources to help lower-middle-market companies accelerate their growth to the benefit of management, employees, and the firm's investors. ACP makes control investments in differentiated companies within the business services, technology, specialty manufacturing, and distribution sectors. For more information, visit http://www.aligncp.com.

Public relations contactSannie Sieper, Director of Marketing, ESource[emailprotected]303-345-9138

SOURCE E Source Companies LLC

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Top online resources to learn Active Learning – Analytics India Magazine

A key requirement of machine learning is to label the data correctly to ensure the best results, but the process is long and time-consuming. This also brings about an issue when dealing with extremely large data sets in unsupervised or semi-supervised learning. The saviour here is active learning with strategies that assist developers in prioritising the data and selecting the most useful samples to label to have the highest training impact. Furthermore, it promises to reduce the samples needed by choosing the right examples.

Various strategies can be used depending on the applications and needs of the model. However, when it comes to learning active learning, the practice is generally a part of bigger machine learning modules, which is why we have created a one-stop guide to mastering active learning online through resources varying from online video tutorials to blog posts and academic papers.

YouTube

Computerphile is a popular YouTube channel that discusses computer science-related topics. Their tutorial on active learning is taught by Dr Michel Valstar, who holds a PhD in Computing and is currently a professor at the University of Nottingham. The tutorial is a foundational element for the basics of active learning, taught through diagrams and illustrations of the concepts.

ICML, the International Conference on Machine Learning, is one of the fastest-growing AI conferences that discuss the latest academic papers. During their 2019 conference, Robert Nowak and Steve Hanneke taught the basics of active learning theory and the popular algorithms to apply (the video is now available online). In addition, the tutorial focuses on sound active learning algorithms and how they can be used to reduce the labels on training data. Robert Nowak holds the Nosbusch Professorship in Engineering at the University of Wisconsin-Madison. Steve Hanneke is a Research Assistant Professor at the Toyota Technological Institute in Chicago, specialising in AI and ML.

Applied AI is a great resource for learning AI/ML online through core concepts and real-life applications. The channels collective views cross 12 million and are popular for the basic concepts thorough teachings. Their tutorial on active learning in ML breaks down the principles of the concept along with real-life examples and mathematical explanations.

PyData is an educational program of NumFOCUS, a US-based not for profit organisation that provides a forum for the international community of data science to share their ideas through conferences. Speaking at one of their events is Jan Freyberg, a machine learning software engineer at Google Health. In a detailed talk, Freyberg discusses active learning in the interactive Python environment, given the ease and comfort in the ecosystem.

Devansh is a Computer Science and Computational Math Double Major at the Rochester Institute of Technology. Through this YouTube tutorial, he comprehensively discusses the basics of active learning, its works and compares it to SSL and GANs. He further explains the concept in detail regarding its use and active learnings acquisition function.

Ranji Raj, holding a masters degree in data science, takes on Youtube to publish tutorials and classwork related to machine learning. His video on active learning gives an in-depth introduction to the subject while discussing important concepts through diagrams and demonstrations. Raj also has consequent coursework on his GitHub page for data scientists interested in learning further.

Scaleway is a French cloud computing company that creates Youtube videos consisting of short machine learning tutorials and real-world applications. In their webinar on active learning, the company collaborated with Kairntech, an AI modelling and dataset creation platform, to discuss the various applications of active learning. The video discusses training datasets and how active learning can be applied for classification. It also glossed over common issues and how to overcome them.

Blog tutorials

Ori Cohen is a PhD holder in CS, currently working as a senior director of data science at New Relic. His Towards Data Science blog post on active learning is an extensive tutorial that discusses the various scenarios possible while using active learning, the algorithms that can be used, the sample selection methods and the codings used for all.

A blog post on Data Camp, an online interactive learning platform, explains in depth the A-Zs of active learning in a moderate level of difficulty. The tutorial discusses the concept in detail with definitions, examples and visuals, and teaches how one can apply active learning on their datasets through a particular example.

Written by a CS and EE student at IIT, India, this post is an in-depth tutorial on using active learning with Python. The tutorial is technical, explaining the code and its concepts through codes and steps. In addition, the post discusses various inputs, outputs, and the Python codes needed to apply active learning correctly.

Alexandre Abraham, a senior research scientist at Dataiku and a Ph D holder in computer science, has written an extensive tutorial on active learning packages on his Medium blog post. The blog post analyses the active learning packages available through a feature comparison, their covered approaches, and their coding aspects. There are three main packages and different methods that data scientists can leverage.

Papers

The paper in discussion is written by Kai Wei, an assistant professor at UCLA, Rishabh Iyer, an assistant professor at the University of Texas, and Jeff Bilmes, a professor at the University of Washington. Their paper studies the problem of selecting a subset of data to train a classifier and how individuals can apply the active learning framework to mitigate the issue.

Online courses

The DeepLearning.AI course in ML data lifecycle has a fourth module, tagged Advanced Labeling, Augmentation and Data Preprocessing, that focuses on semi-supervised learning, dataset labelling, and the role played by active learning within. The instructor, Robert Crowe, works at TensorFlow by Google and has multiple degrees in AI, ML and data science.

Link:

Top online resources to learn Active Learning - Analytics India Magazine

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IT Sligo helping to close the data science skills gap with flexible, online learning – The Irish Times

After 20 years working in software engineering across a variety of industries and government departments, Darragh Sherwin, a development team lead at Overstock Ireland, and a current student at IT Sligo, noticed that he had a skills gapcompared to his colleagues, following a move to a different department within Overstock.

My team recently moved into the algorithms department of Overstock which employs a lot of data scientists, machine learning (ML) scientists and ML engineers. The move highlighted a skills deficit that I had in Data Science, I had always had it in the back of my head to continue further education and studying the part-time, online Masters in Data Science at IT Sligo felt like a natural alignment. I was moving into a department where data is crucial and there are moves across all industries is to be more data driven,says Sherwin.

Data Science is now the backbone of any industry and current trends indicate that it will accumulate even more importance in the coming years. If businesses want to succeed, it is critical that they bank on data science to make data-informed decisions based on insights and trends.

Due to the ever-growing importance of data, data scientists are now in high demand and IT Sligo are helping to close the current skills deficit in data science with their part-time, online Masters in Data Science.

The course includes a combination of statistical analysis, modelling, machine learning and data visualisation. Applicable to any industry, it combines techniques from mathematics, statistics, information theory, computer science and artificial intelligence. Automated driving, consumer buying habits, medical imaging, business intelligence, fraud/risk detection and speech recognition are just a few applications. Masters students will design data analytic techniques, interpret, and manage big data using software as well as machine learning, and probabilistic and statistical methods.

On why he chose the course from IT Sligo, Sherwinsays; Overstock has worked closely with IT Sligo, we hire graduates from IT Sligo and they are always of impressively high calibre. The course modules aligned with my understanding of the area and gives a good foundation to students.

Relevant to engineers who require upskilling in data science or those have already qualified with a Level 8 honours degree in Computer Science (or related disciplines), this masters is offered part-time and online with live lectures in the evening. You can study anywhere and in your own time.

It is great to have the flexibility of studying online. I have a young child so if I need to miss a lecture, I can come back and watch it later. There are great online resources for learning and college seems to have paid particular attention to ensuring online studying is very smooth with their tools like Moodle and Microsoft Teams, says Sherwin. Choosing to study a part-time, online course allows students to upskill for their career while also working full-time.

A qualification in Data Science skills can lead to an exciting career in an array of industries including IT, financial services, retail, and manufacturing.These roles are not confined to IT based positions but can lead to roles in business intelligence, analysts, or data warehouse consultants.The rewards for such positions are also inviting, starting salaries range from 40,000 with senior data scientists commanding annual salaries of more than 100,000.

Online Learning at IT Sligo is ranked number onefor Most Flexible Learning Students in the Good University Guide 2021. With more than 150 online courses available, IT Sligo is Irelands leading online provider. Through innovative online teaching methods, students anywhere in the world can study and graduate with fully accredited online qualifications matched to industry demand.

Applications are now open for the Masters in Data Science at IT Sligo, starting part-time, online on Monday, January 17th, 2022. Apply here - http://www.itsligo.ie/datascience

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IT Sligo helping to close the data science skills gap with flexible, online learning - The Irish Times

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Insights on the Healthcare Artificial Intelligence Global Market to 2026 – Featuring Google, IBM and Intel Among Others – Yahoo Finance

Dublin, Jan. 04, 2022 (GLOBE NEWSWIRE) -- The "Healthcare Artificial Intelligence (AI) Market - Global Outlook & Forecast 2021-2026" report has been added to ResearchAndMarkets.com's offering.

The healthcare artificial intelligence market is expected to reach USD 44.5 billion by 2026, growing at a CAGR of 46.21%.

Several pharmaceutical companies are implementing innovative technologies to boost their growth in the global healthcare industry. Collaboration of GSK with Exscientia identified a small compound for targeted therapeutics and its characteristics towards the specific target using the AI platform. AI is becoming an incredible platform in the pharmaceutical industry.

For instance, Novartis announced Microsoft as a strategic partner in AI and data science to set up an AI innovation lab. Since the last year, over 50+ companies have got machine learning and AI algorithms approvals. During the COVID-19 pandemic, AI played a significant role in the healthcare industry. An analytics study by Accenture combined with clinical applications demonstrated the potential of AI to reduce approximately USD 150 billion per annum by 2026 in the US healthcare system.

The following factors are likely to contribute to the growth of the healthcare artificial intelligence market during the forecast period:

Increase in patient volume & complexities associated with data fueling demand for AI.

The shrinking operational workforce in healthcare facilities propelling the need for AI.

Technological advancement & innovations in AI influencing end-users in the market.

Rising Investment in advanced drug discovery & development process augmenting the adoption of AI.

Key Highlights

The healthcare providers segment accounted for the largest market share with around 48% compared to others in 2020.

According to the research, the publisher estimated that APAC would witness the highest growth in the healthcare artificial intelligence (AI) market during the forecast period.

The study considers a detailed scenario of the present healthcare artificial intelligence market and its market dynamics for the period 2021?2026. It covers a detailed overview of several market growth enablers, restraints, and trends. The report offers both the demand and supply aspects of the market. It profiles and examines leading companies and other prominent ones operating in the market.

Story continues

Vendor Analysis

Giant players are focusing on pursuing organic growth strategies to enhance their product portfolio in the healthcare artificial intelligence (AI) market. Several initiatives by the players will complement growth strategies, which are gaining traction among end-users in the market. Rising growth of startups collaborating with key vendors in promoting their artificial intelligence in healthcare applications creating heavy competition in the market.

Key Questions Answered:1. How big is the healthcare artificial intelligence (AI) market?2. Which region has the highest share in the healthcare artificial intelligence market?3. Who are the key players in the healthcare AI market?4. What are the latest market trends in the healthcare artificial intelligence market?5. What is the use of AI in the healthcare market?

Key Topics Covered:

1 Research Methodology

2 Research Objectives

3 Research Process

4 Scope & Coverage4.1 Market Definition4.1.1 Inclusions4.1.2 Exclusions4.1.3 Market Estimation Caveats4.2 Base Year4.3 Scope Of The Study4.3.1 Market Segmentation By Component4.3.2 Market Segmentation By Application4.3.3 Market Segmentation By Technology4.3.4 Market Segmentation By End-User4.3.5 Market Segmentation By Geography

5 Report Assumptions & Caveats5.1 Key Caveats5.2 Currency Conversion5.3 Market Derivation

6 Market At A Glance

7 Introduction7.1 Healthcare Artificial Intelligence (AI)

8 Market Opportunities & Trends8.1 Rising Investments In Advanced Drug Discovery & Development Processes8.2 Mergers, Acquisitions, & Collaborations With Life Science & Medical Device Companies8.3 Influx/Emergence Of Many Startups In The Healthcare AI Industry

9 Market Growth Enablers9.1 Increase In Patient Volume & Complexities Associated With Data9.2 Shrinking Operational Workforce In Healthcare Facilities9.3 Technological Advancements & Innovations In AI9.4 Growing Need To Reduce Healthcare Costs Using It & AI Technologies

10 Market Restraints10.1 High Installation & Implementation Cost Of AI & Related Platforms10.2 Lack Of Skilled AI Workforce & Resistance Among Healthcare Professionals10.3 Stringent & Ambiguous Regulations For Healthcare Software & AI Technologies10.4 Absence Of Interoperability Among Commercially Available Ai Solutions Coupled With Data Privacy Issues

11 Market Landscape11.1 Market Overview11.2 Market Size & Forecast11.3 Five Forces Analysis11.3.1 Threat Of New Entrants11.3.2 Bargaining Power Of Suppliers11.3.3 Bargaining Power Of Buyers11.3.4 Threat Of Substitutes11.3.5 Competitive Rivalry

12 Component12.1 Market Snapshot & Growth Engine12.2 Market Overview12.3 Hardware12.3.1 Market Overview12.3.2 Market Size & Forecast12.3.3 Hardware: Geography Segmentation12.4 Software & Services12.4.1 Market Overview12.4.2 Market Size & Forecast12.4.3 Software & Services: Geography Segmentation

13 Application13.1 Market Snapshot & Growth Engine13.2 Market Overview13.3 Hospital Workflow Management13.3.1 Market Overview13.3.2 Market Size & Forecast13.3.3 Hospital Workflow Management: Geography Segmentation13.4 Medical Imaging & Diagnosis13.4.1 Market Overview13.4.2 Market Size & Forecast13.4.3 Medical Imaging & Diagnosis: Geography Segmentation13.5 Drug Discovery & Precision Medicine13.5.1 Market Overview13.5.2 Market Size & Forecast13.5.3 Drug Discovery & Precision Medicine: Geography Segmentation13.6 Patient Management13.6.1 Market Overview13.6.2 Market Size & Forecast13.6.3 Patient Management: Geography Segmentation

14 Technology14.1 Market Snapshot & Growth Engine14.2 Market Overview14.3 Machine Learning14.3.1 Market Overview14.3.2 Market Size & Forecast14.3.3 Machine Learning: Geography14.4 Querying Method14.4.1 Market Overview14.4.2 Market Size & Forecast14.4.3 Querying Method: Geography Segmentation14.5 Natural Language Processing14.5.1 Market Overview14.5.2 Market Size & Forecast14.5.3 Natural Language Processing: Geography Segmentation14.6 Other Technology14.6.1 Market Overview14.6.2 Market Size & Forecast14.6.3 Other Technology: Geography Segmentation

15 End-User15.1 Market Snapshot & Growth Engine15.2 Market Overview15.3 Healthcare Providers15.3.1 Market Overview15.3.2 Market Size & Forecast15.3.3 Healthcare Providers: Geography Segmentation15.4 Pharma-Biotech & Medical Device Companies15.4.1 Market Overview15.4.2 Market Size & Forecast15.4.3 Pharma-Biotech & Medical Device Companies: Geography Segmentation15.5 Payers15.5.1 Market Overview15.5.2 Market Size & Forecast15.5.3 Payers: Geography Segmentation15.6 Others15.6.1 Market Overview15.6.2 Market Size & Forecast15.6.3 Other End User: Market By Geography

16 Geography16.1 Market Snapshot & Growth Engine16.2 Geographic Overview

17 North America

18 Europe

19 APAC

20 Latin America

21 Middle East & Africa

22 Competitive Landscape22.1 Competition Overview22.2 Market Share Analysis22.2.1 Google22.2.2 IBM22.2.3 Intel22.2.4 Medtronic22.2.5 Microsoft22.2.6 NVIDIA22.2.7 Siemens Healthineers

23 Key Company Profiles23.1 GOOGLE23.1.1 Business Overview23.1.2 Product Offerings23.1.3 Key Strategies23.1.4 Key Strengths23.1.5 Key Opportunities23.2 INTERNATIONAL BUSINESS MACHINES (IBM)23.2.1 Business Overview23.2.2 Product Offerings23.2.3 Key Strategies23.2.4 Key Strengths23.2.5 Key Opportunities23.3 INTEL CORPORATION23.3.1 Business Overview23.3.2 Product Offerings23.3.3 Key Strategies23.3.4 Key Strengths23.3.5 Key Opportunities23.4 MEDTRONIC23.4.1 Business Overview23.4.2 Product Offerings23.4.3 Key Strategies23.4.4 Key Strengths23.4.5 Key Opportunities23.5 MICROSOFT CORPORATION23.5.1 Business Overview23.5.2 Product Offerings23.5.3 Key Strategies23.5.4 Key Strengths23.5.5 Key Opportunities23.6 NVIDIA CORPORATION23.6.1 Business Overview23.6.2 Product Offerings23.6.3 Key Strategies23.6.4 Key Strengths23.6.5 Key Opportunities23.7 SIEMENS HEALTHINEERS23.7.1 Business Overview23.7.2 Product Offerings23.7.3 Key Strategies23.7.4 Key Strengths23.7.5 Key Opportunities

24 Other Prominent Vendors24.1 ARTERYS24.1.1 Business Overview24.1.2 Product Offerings24.2 CAPTION HEALTH24.2.1 Business Overview24.2.2 Product Offerings24.3 ENLITIC24.3.1 Business Overview24.3.2 Product Offerings24.4 CATALIA HEALTH24.4.1 Business Overview24.4.2 Product Offerings24.5 GENERAL VISION24.5.1 Business Overview24.5.2 Product Offerings24.6 PHILIPS24.6.1 Business Overview24.6.2 Product Offerings24.7 STRYKER24.7.1 Business Overview24.7.2 Product Offerings24.8 SHIMADZU RECURSION PHARMACEUTICALS24.8.1 Business Overview24.8.2 Product Offerings24.9 GE HEALTHCARE24.9.1 Business Overview24.9.2 Product Offerings24.10 REMEDY MEDICAL24.10.1 Business Overview24.10.2 Product Offerings24.11 SUBTLE MEDICAL24.11.1 Business Overview24.11.2 Product Offerings24.12 NETBASE QUID24.12.1 Business Overview24.12.2 Product Offerings24.13 BIOSYMETRICS24.13.1 Business Overview24.13.2 Product Offerings24.14 SENSELY24.14.1 Business Overview24.14.2 Product Offerings24.15 INFORMAI24.15.1 Business Overview24.15.2 Product Offerings24.16 BIOCLINICA24.16.1 Business Overview24.16.2 Product Offerings24.17 OWKIN24.17.1 Business Overview24.17.2 Product Offerings24.18 BINAH.AI24.18.1 Business Overview24.18.2 Product Offerings24.19 ONCORA MEDICAL24.19.1 Business Overview24.19.2 Product Offerings24.20 QURE.AI TECHNOLOGIES24.20.1 Business Overview24.20.2 Product Offerings24.21 LUNIT24.21.1 Business Overview24.21.2 Product Offerings24.22 CARESYNTAX24.22.1 Business Overview24.22.2 Product Offerings24.23 ANJU SOFTWARE24.23.1 Business Overview24.23.2 Product Offerings24.24 IMAGIA CYBERNETICS24.24.1 Business Overview24.24.2 Product Offerings24.25 DEEP GENOMICS24.25.1 Business Overview24.25.2 Product Offerings24.26 WELLTOK INC.24.26.1 Business Overview24.26.2 Product Offerings24.27 MDLIVE24.27.1 Business Overview24.27.2 Product Offerings24.28 MAXQ AI24.28.1 Business Overview24.28.2 Product Offerings24.29 QVENTUS24.29.1 Business Overview24.29.2 Product Offerings24.30 WORKFUSION24.30.1 Business Overview24.30.2 Product Offerings

25 Report Summary25.1 Key Takeaways25.2 Strategic Recommendations

26 Quantitative Summary

27 Appendix

For more information about this report visit https://www.researchandmarkets.com/r/it4jn7

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Insights on the Healthcare Artificial Intelligence Global Market to 2026 - Featuring Google, IBM and Intel Among Others - Yahoo Finance

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An Exclusive Interview with Abhishek Rungta, Founder and CEO, INT – Analytics Insight

INT is leveraging big data analytics to provide web development, digital marketing, etc.

Big data analytics is offering huge scope to companies and individuals to unlock certain business potentials with different real-time data structured, unstructured, and semi-structured. Big data is helping in multiple departments like web development, mobile development, understanding consumers, and many more. The global big data analytics market size is expected to hit US$684.12 billion in 2030 with a CAGR of 13.5%.

Here is an exclusive interview with Abhishek Rungta, Founder and CEO, INT, to enlighten the readers about how INT is leveraging big data analytics for web development to be a full-stack software product engineering company.

INT. (Indus Net Technologies) is a full-stack software product engineering company with a team of over 750 full-stack agile solutions experts who can help a business to experience a smooth digital transformation. It has been unlocking the business potential with technology and delivering a seamless user experience since 1997.

INT. offers web development, mobile development, digital marketing, dedicated hiring, analytics, and product design services.

Garbage in Garbage Out: Organizations have a huge volume of unstructured data which at times becomes difficult to process for insight building. INT. works extensively to structure this big data so that it can be consumed by the analytical tools for the generation of insights.

Big data analytics has brought a revolution in almost every industry across the globe. One might have heard of a trending proverb that data is the new oil.

Every business wants to implement the concept of digital first as soon as possible. The usage of big data analytics by businesses is growing by the day as they want to understand customer behaviour insights, re-develop products and create new revenue streams.

According to a report by Analytics Insight, the global big data analytics market is expected to reach almost US$420.98 billion by 2027. So, more and more companies will utilize big data analytics, and data professionals will be in high demand in the coming years.

AI has opened the way for smarter job execution with real-time analysis and more interaction between humans and machines. On the other hand, the Internet of Things (IoT) has enabled communication between devices and humans but without any human intervention.

The IoT combined with artificial intelligence (AI) has the potential to create intelligent robots capable of simulating smart behaviour and aiding decision-making with little or no human intervention.

While IoT collects vast volumes of data by connecting devices to the internet, AI aids in the assimilation and evaluation of this data. Machine learning (a subset of AI) in IoT devices uses highly powerful sensors to find trends and detect any errors in data collecting.

After collecting data, big data analytics can be used for a better understanding of customer behaviour and enhancing customer experience.

The future of artificial intelligence (AI) and machine learning is bright in India. Machine learning is a subset of artificial intelligence. According to an Accenture report, AI has the potential to add US$957 billion, or 15% of Indias current gross value by 2035. As per the The AI Index 2021 Annual Report, Karnataka had the most AI start-ups in 2019, with 356, followed by Maharashtra with 215 and both Andhra Pradesh and Telangana with 111.

In India, IoT (Internet of Things) has gained fame with the introduction of Amazons Alexa, Google Echo, smart locks, smart lighting, etc. A report has revealed that the Indian IoT market is expected to expand at a compound annual growth rate of 13.2% from 2020 to 2025. The top four areas of IoT funding are lifestyle/wearables, embedded computing, industrial internet, and connected homes.

Well, talking about the evolving trends will be incomplete without mentioning cloud computing! According to a NASSCOM report, until 2022, investments in cloud management, storage networks, security, and backup services are predicted to increase by 31% YoY. And India alone recorded nearly 379,000 job openings for cloud roles in 2020 and this demand is likely to increase with time.

My leadership mantra is to be a leader whom people trust and give respect. I strongly believe that a leaders true asset is people.

The team at INT operates as a family and doesnt believe in hierarchy. Whenever I am in Kolkata, I dont sit in a separate cabin at the office. I sit with different teams every day so that my co-workers dont feel hesitant in approaching me. I try to know what motivates them. After all, a leader should know how to drive success and productivity.

Here are a few major challenges that the big data analytics industry is facing today:

Lack of data science professionals: There is a massive shortage of professionals like data scientists, data analysts, and data engineers. Many companies are upskilling their existing eligible employees so that they gain knowledge about big data analytics.

Security of data: Securing data is often neglected by companies as they remain mostly busy with understanding, storing, and analysing their data sets. By doing so, companies can lose a substantial amount of money if they fail to protect their data.

Poor visualization: Valuable data can be overlooked when it is combined with irrelevant data. It can create a faulty interpretation of the information to the audience. This misconception can lead to erroneous insights and bad business decisions, all while claiming to be backed by data.

The pandemic has accelerated the need for the digital transformation of businesses. Eventually, many industries like banking, financial services, ed-tech, logistics, etc. are increasingly looking for talents in the big data analytics field to stay competitive in the post-pandemic world.

These industries are offering lucrative packages and perks to hire and retain data science professionals. The World Economic Forum predicts that data scientists and analysts will become the No. 1 emerging role in the world by 2022.

The number of job openings in big data analytics is touching new heights every day. So, I dont see any immediate signs of plunging of the employment trends of the big data analytics industry. The US Bureau of Labour Statistics has reported that the rise of data science needs will create roughly 11.5 million job openings by 2026.

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An Exclusive Interview with Abhishek Rungta, Founder and CEO, INT - Analytics Insight

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