#Dataiku

[dluxtech]

Digital Makeover: From Laggard to Leader

So, you've heard of digital transformation, right? It's everywhere these days, promising to turn your business from a sluggish snail into a gazelle of innovation. But hold on, this transformation thing can be tricky. It's not just about throwing some fancy tech at your problems.

Think of it this way: Digitization is like putting your car on autopilot. Sure, it makes the ride smoother, but you're still stuck on the same old road. Digital transformation, on the other hand, is like taking a whole new road trip. You're using the best navigation (data!), exploring hidden gems (new opportunities), and maybe even discovering a shortcut or two (increased efficiency).

Data is the fuel in this journey. Just like a car needs gas to go anywhere, your transformation needs high-quality data to make smart decisions, personalize experiences for your customers, and keep things running smoothly. But watch out for the "data swamp"! This is where information gets all cluttered and unreliable, making it more like a junkyard than a treasure trove.

Here's the secret sauce for success: You need a powerful combination of people, processes, and technology. Technology is great, but it's your people who will really steer the ship. They need to be comfortable with new tools, ready to rethink how things are done, and focused on keeping your customers happy.

And remember, this isn't a one-time thing. Digital transformation is more like a delicious buffet – you keep going back for more! Embrace experimentation, learn from your mistakes, and be ready to adapt as new technologies emerge.

Want some inspiration? Look at Pfizer. They realized the power of data science and went all in, setting clear goals, working together across departments, and smashing through any roadblocks. Now they're data science rockstars! Or how about GE Aviation? They put their users first, making sure their data initiatives were all about solving real problems. No getting bogged down in tech jargon here!

By avoiding the pitfalls and embracing the good stuff, you can navigate your digital transformation with confidence. Focus on clean data, strong collaboration, and continuous improvement. Remember, it's your own unique journey, so tailor your approach to what works best for you. With the right strategy and the power of data, your business can transform into an innovation machine, ready to conquer the future!

And hey, if you need help with that data thing, we at DLUX are your best friends! We're Dataiku experts who can help you unlock the full potential of this amazing platform. We'll get you set up, train your team, and keep things running smoothly so you can focus on growing your business.

Ready to ditch the junkyard data and hit the road to transformation? Get in touch with DLUX today! Let's make it happen.

[trillionstech-ai]

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[jcmarchi]

French initiative for responsible AI leaders - AI News

New Post has been published on https://thedigitalinsider.com/french-initiative-for-responsible-ai-leaders-ai-news/

French initiative for responsible AI leaders - AI News

ESSEC Business School and Accenture have announced the launch of a new initiative, ‘AI for Responsible Leadership,’ which marks the 10th anniversary of the establishment of the role of Chair at ESSEC, titled the ESSEC Accenture Strategic Business Analytics Chair.

The initiative aims to encourage the use of artificial intelligence by leaders in ways that are responsible and ethical, and that lead to high levels of professional performance. It aims to provide current and future leaders with the skills they require when faced with challenges in the future; economic, environmental, or social.

Several organisations support the initiative, including institutions, businesses, and specialised groups, including ESSEC Metalab for Data, Technology & Society, and Accenture Research.

Executive Director of the ESSEC Metalab, Abdelmounaim Derraz, spoke of the collaboration, saying, “Technical subjects are continuing to shake up business schools, and AI has opened up opportunities for collaboration between partner companies, researchers, and other members of the ecosystem (students, think tanks, associations, [and] public service).”

ESSEC and Accenture aim to integrate perspectives from multiple fields of expertise, an approach that is a result of experimentation in the decade the Chair has existed.

The elements of the initiative include workshops and talks designed to promote the exchange of knowledge and methods. It will also include a ‘barometer’ to help track AI’s implementation and overall impact on responsible leadership.

The initiative will engage with a network of institutions and academic publications, and an annual Grand Prix will recognise projects that focus on and explore the subject of AI and leadership.

Fabrice Marque, founder of the initiative and the current ESSEC Accenture Strategics Business Analytics Chair, said, “For years, we have explored the potential of using data and artificial intelligence in organisations. The synergies we have developed with our partners (Accenture, Accor, Dataiku, Engie, Eurofins, MSD, Orange) allowed us to evaluate and test innovative solutions before deploying them.

“With this initiative, we’re taking a major step: bringing together an engaged ecosystem to sustainably transform how leaders think, decide, and act in the face of tomorrow’s challenges. Our ambition is clear: to make AI a lever for performance, innovation and responsibility for […] leaders.”

Managing Director at Accenture and sponsor of the ESSEC/Accenture Chair and initiative, Aurélien Bouriot, said, “The ecosystem will benefit from the resources that Accenture puts at its disposal, and will also benefit our employees who participate.”

Laetitia Cailleteau, Managing Director at Accenture and leader of Responsible AI & Generative AI for Europe, highlighted the importance of future leaders understanding all aspects of AI.

“AI is a pillar of the ongoing industrial transformation. Tomorrow’s leaders must understand the technical, ethical, and human aspects and risks – and know how to manage them. In this way, they will be able to maximise value creation and generate a positive impact for the organisation, its stakeholders and society as a whole.”

Image credit: Wikimedia Commons

See also: Microsoft and OpenAI probe alleged data theft by DeepSeek

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

[thoughtportal]

Canada’s explosive wildfires have damaged a forest carbon offset project

Scientists argue that it is a 'risky bet' to count on trees – temporary stores of carbon – to compensate for the carbon dioxide released by burning fossil fuels that stays in the atmosphere for centuries.

Canada’s explosive wildfire season has already pumped millions of tons of carbon dioxide into the atmosphere. Some of that carbon is coming from vegetation burned at a carbon offset project, highlighting the fragility of a tool the world is relying on to fight catastrophic climate change.

With Canada facing what’s on track to be its worst wildfire season on record – and climate change fueling ever more destructive blazes – climate experts and offset developers are concerned it could be a harbinger of what’s to come.

On June 3, British Columbia fire officials spotted a blaze that has impacted the BigCoast Forest Climate Initiative project, according to Domenico Iannidinardo, senior vice president for forests and climate at Mosaic Forest Management Corporation, which runs the project.

“About 100 hectares of our 40,000 hectare project was involved in this fire,” or about 0.25% of the project, Iannidinardo told Bloomberg Green. That’s an area equivalent to roughly 140 football pitches worth of forest.

So far, little is known about how the fire will impact BigCoast’s carbon removal capacity or how much carbon has been released. Werner Kurz, senior research scientist in the Canadian Forest Service, said its emissions could be up to 32,250 tons of carbon dioxide equivalent, depending on the fire’s severity. The impact is “clearly not trivial” for BigCoast or the local area, he said, but it’s a “rounding error” in terms of the climate impact of the wildfires that have ravaged the province.

As of June 23, crews had suppressed the fire so it was no longer spreading. Mosaic said that assessing the emissions from the area that was burned “will take some time.” They will be incorporated into future carbon accounting and be independently verified. Still, Iannidinardo described the “disturbance” as “negligible.”

Companies and countries are increasingly relying on carbon offsets to reach their emissions targets, a tool used in an attempt to compensate for their climate pollution by investing in projects that reduce or remove emissions elsewhere. But climate scientists and activists say the instruments, including those based on forests, aren’t generally effective at mitigating climate change, despite decades of experimentation and improvement. They point to forest fires – which are increasing in severity partly due to climate change – as a big reason. Grayson Badgley, research scientist at CarbonPlan, a U.S.-based nonprofit​, said it’s a “risky bet” to count on trees – temporary stores of carbon – to compensate for the carbon dioxide released by burning fossil fuels that stays in the atmosphere for centuries.

In 2018, Mosaic, a logging company, and its partners committed to stop cutting down trees in the project area and instead protect them for 30 years. The company is measuring the tons of additional CO2 stored and the forestry-related emissions avoided, and packaging each of those as a carbon credit for sale to companies or individuals looking to offset their carbon footprint. Each credit represents one ton of CO2 removed or not added to the atmosphere.

The project has already issued 1.4 million credits, an amount equivalent to the total emissions of Sierra Leone in 2021. They’ve been bought by U.K.-based AI company Dataiku, global insurance firm Aspen and the American Institute for Foreign Study, a travel and insurance company, among others, according to Bloomberg Green analysis of public data. There’s currently no information to indicate any of those companies’ credits have been impacted by this year’s fire.

Under the rules of the offset registry Verra, whose standard Mosaic uses, the company has 30 days to report any damage to its forests and up to two years to submit a “loss report” detailing its impact. As a type of insurance mechanism against wildfires and other risks, project developers must contribute a portion of their credits to what’s known as a buffer pool. If disaster strikes and impacts a project’s carbon inventory, the standard states that an equivalent number of credits are taken out of the pool.

BigCoast’s buffer pool is 15.5% of its issued credits, Mosaic said. But none of these are earmarked for natural risks like extreme weather, pest outbreaks and fire, according to project documentation. That’s because the company evaluates that risk – calculated according to a matrix of significance and likelihood – to be zero.

That assessment is “mind-boggling,” said William Anderegg, director of the Wilkes Center for Climate Science and Policy at the University of Utah. Fires are a “really dramatic risk” that forest offset projects face, he said, along with other risks such as drought stress and insect outbreaks.

Mosaic said its risk assessment is based on the fact that “the project is geographically and ecologically diverse and distributed,” meaning the likelihood of widespread damage due to fires or something else measures as “insignificant.”

Under Verra’s rules, credits allocated against other risks can backstop a fire incident, but in the long run this could have a serious impact on the insurance efficacy, Anderegg said. If wildfires eat up more than was budgeted, that has “very real impacts on whether these projects are likely to succeed over a century,” he said.

A team of researchers led by Barbara Haya at the University of California at Berkeley’s Goldman School of Public Policy recently identified a set of shortcomings in carbon offset registry methods, like those used by BigCoast, that could “critically undermine” their buffer pool policies. None take into account how climate change may increase fire risk, for example. In the U.S., CarbonPlan’s Badgley and a team of researchers found California’s buffer pool to be “severely undercapitalized.”

Verra relies on “the historical likelihood of an event occurring” to guide its buffer pool policies, said company spokesperson Joel Finkelstein. The company is set to update its insurance tool later in the summer to better account for changing risks due to climate change.

Canadian offset developers across the country are nervous about the rest of the fire season. “This is a wake-up call,” said André Gravel, chief executive of Société de gestion d’actifs forestiers (Solifor), which runs the Monet Forest Conservation Project. “The frequency of fires is increasing,” he said.

“Everyone is very concerned and on high alert,” said Adrian Leslie, manager of a Nature Conservancy of Canada forestry offsets project called Darkwoods in British Columbia. The group said approximately 4,485 hectares of the project burned in 2021, or less than 10% of the total area. That equates to about 36,700 tonnes of CO2 being released, according to a preliminary estimate shared by Leslie.

“The IPCC has made it very clear that every ton matters, every year matters, every degree matters,” Kurz said. Wildfire risk is increasing and project developers must recognize and address this: “We have to bend the curve.”

Bloomberg’s Demetrios Pogkas contributed to this report.

[marketgrowthreports]

Data Wrangling Market: Size, Shares, Regional Insights, and Forecasts Through 2033

"Data Wrangling Market" - Research Report, 2025-2033 delivers a comprehensive analysis of the industry's growth trajectory, encompassing historical trends, current market conditions, and essential metrics including production costs, market valuation, and growth rates. Data Wrangling Market Size, Share, Growth, and Industry Analysis, By Type (On-premises, Cloud Based), By Application (BFSI, Telecom and IT, Retail and eCommerce, Healthcare and Life Sciences, Travel and Hospitality, Government, Manufacturing, Energy and Utilities, Transportation and Logistics, Others), Regional Insights and Forecast to 2033 are driving major changes, setting new standards and influencing customer expectations. These advancements are expected to lead to significant market growth. Capitalize on the market's projected expansion at a CAGR of 11.2% from 2024 to 2033. Our comprehensive [109+ Pages] market research report offers Exclusive Insights, Vital Statistics, Trends, and Competitive Analysis to help you succeed in this Information & Technology sector.

Data Wrangling Market: Is it Worth Investing In? (2025-2033)

Global Data Wrangling Market size in 2024 is estimated to be USD 1819.31 million, with projections to grow to USD 4729.88 million by 2033 at a CAGR of 11.2%.

The Data Wrangling market is expected to demonstrate strong growth between 2025 and 2033, driven by 2024's positive performance and strategic advancements from key players.

The leading key players in the Data Wrangling market include:

IBM

Oracle

SAS

Trifacta

Datawatch

Talend

Alteryx

Dataiku

TIBCO Software

Paxata

Informatica

Hitachi Vantara

Teradata

IRI

Brillio

Onedot

TMMData

Datameer

Cooladata

Unifi Software

Rapid Insight

Infogix

Zaloni

Impetus

Ideata Analytics

Request a Free Sample Copy @ https://www.marketgrowthreports.com/enquiry/request-sample/103841

Report Scope

This report offers a comprehensive analysis of the global Data Wrangling market, providing insights into market size, estimations, and forecasts. Leveraging sales volume (K Units) and revenue (USD millions) data, the report covers the historical period from 2020 to 2025 and forecasts for the future, with 2024 as the base year.

For granular market understanding, the report segments the market by product type, application, and player. Additionally, regional market sizes are provided, offering a detailed picture of the global Data Wrangling landscape.

Gain valuable insights into the competitive landscape through detailed profiles of key players and their market ranks. The report also explores emerging technological trends and new product developments, keeping you at the forefront of industry advancements.

This research empowers Data Wrangling manufacturers, new entrants, and related industry chain companies by providing critical information. Access detailed data on revenues, sales volume, and average price across various segments, including company, type, application, and region.

Request a Free Sample Copy of the Data Wrangling Report 2025 - https://www.marketgrowthreports.com/enquiry/request-sample/103841

Understanding Data Wrangling Product Types & Applications: Key Trends and Innovations in 2025

By Product Types:

On-premises

Cloud Based

By Application:

BFSI

Telecom and IT

Retail and eCommerce

Healthcare and Life Sciences

Travel and Hospitality

Government

Manufacturing

Energy and Utilities

Transportation and Logistics

Others

Emerging Data Wrangling Market Leaders: Where's the Growth in 2025?

North America (United States, Canada and Mexico)

Europe (Germany, UK, France, Italy, Russia and Turkey etc.)

Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)

South America (Brazil, Argentina, Columbia etc.)

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

Inquire more and share questions if any before the purchase on this report at - https://www.marketgrowthreports.com/enquiry/request-sample/103841

This report offers a comprehensive analysis of the Data Wrangling market, considering both the direct and indirect effects from related industries. We examine the pandemic's influence on the global and regional Data Wrangling market landscape, including market size, trends, and growth projections. The analysis is further segmented by type, application, and consumer sector for a granular understanding.

Additionally, the report provides a pre and post pandemic assessment of key growth drivers and challenges within the Data Wrangling industry. A PESTEL analysis is also included, evaluating political, economic, social, technological, environmental, and legal factors influencing the market.

We understand that your specific needs might require tailored data. Our research analysts can customize the report to focus on a particular region, application, or specific statistics. Furthermore, we continuously update our research, triangulating your data with our findings to provide a comprehensive and customized market analysis.

COVID-19 Changed Us? An Impact and Recovery Analysis

This report delves into the specific repercussions on the Data Wrangling Market. We meticulously tracked both the direct and cascading effects of the pandemic, examining how it reshaped market size, trends, and growth across international and regional landscapes. Segmented by type, application, and consumer sector, this analysis provides a comprehensive view of the market's evolution, incorporating a PESTEL analysis to understand key influencers and barriers. Ultimately, this report aims to provide actionable insights into the market's recovery trajectory, reflecting the broader shifts. Final Report will add the analysis of the impact of Russia-Ukraine War and COVID-19 on this Data Wrangling Industry.

TO KNOW HOW COVID-19 PANDEMIC AND RUSSIA UKRAINE WAR WILL IMPACT THIS MARKET - REQUEST SAMPLE

Detailed TOC of Global Data Wrangling Market Research Report, 2025-2033

1 Report Overview

1.1 Study Scope 1.2 Global Data Wrangling Market Size Growth Rate by Type: 2020 VS 2024 VS 2033 1.3 Global Data Wrangling Market Growth by Application: 2020 VS 2024 VS 2033 1.4 Study Objectives 1.5 Years Considered

2 Global Growth Trends

2.1 Global Data Wrangling Market Perspective (2020-2033) 2.2 Data Wrangling Growth Trends by Region 2.2.1 Global Data Wrangling Market Size by Region: 2020 VS 2024 VS 2033 2.2.2 Data Wrangling Historic Market Size by Region (2020-2025) 2.2.3 Data Wrangling Forecasted Market Size by Region (2025-2033) 2.3 Data Wrangling Market Dynamics 2.3.1 Data Wrangling Industry Trends 2.3.2 Data Wrangling Market Drivers 2.3.3 Data Wrangling Market Challenges 2.3.4 Data Wrangling Market Restraints

3 Competition Landscape by Key Players

3.1 Global Top Data Wrangling Players by Revenue 3.1.1 Global Top Data Wrangling Players by Revenue (2020-2025) 3.1.2 Global Data Wrangling Revenue Market Share by Players (2020-2025) 3.2 Global Data Wrangling Market Share by Company Type (Tier 1, Tier 2, and Tier 3) 3.3 Players Covered: Ranking by Data Wrangling Revenue 3.4 Global Data Wrangling Market Concentration Ratio 3.4.1 Global Data Wrangling Market Concentration Ratio (CR5 and HHI) 3.4.2 Global Top 10 and Top 5 Companies by Data Wrangling Revenue in 2024 3.5 Data Wrangling Key Players Head office and Area Served 3.6 Key Players Data Wrangling Product Solution and Service 3.7 Date of Enter into Data Wrangling Market 3.8 Mergers & Acquisitions, Expansion Plans

4 Data Wrangling Breakdown Data by Type

4.1 Global Data Wrangling Historic Market Size by Type (2020-2025) 4.2 Global Data Wrangling Forecasted Market Size by Type (2025-2033)

5 Data Wrangling Breakdown Data by Application

5.1 Global Data Wrangling Historic Market Size by Application (2020-2025) 5.2 Global Data Wrangling Forecasted Market Size by Application (2025-2033)

6 North America

6.1 North America Data Wrangling Market Size (2020-2033) 6.2 North America Data Wrangling Market Growth Rate by Country: 2020 VS 2024 VS 2033 6.3 North America Data Wrangling Market Size by Country (2020-2025) 6.4 North America Data Wrangling Market Size by Country (2025-2033) 6.5 United States 6.6 Canada

7 Europe

7.1 Europe Data Wrangling Market Size (2020-2033) 7.2 Europe Data Wrangling Market Growth Rate by Country: 2020 VS 2024 VS 2033 7.3 Europe Data Wrangling Market Size by Country (2020-2025) 7.4 Europe Data Wrangling Market Size by Country (2025-2033) 7.5 Germany 7.6 France 7.7 U.K. 7.8 Italy 7.9 Russia 7.10 Nordic Countries

8 Asia-Pacific

8.1 Asia-Pacific Data Wrangling Market Size (2020-2033) 8.2 Asia-Pacific Data Wrangling Market Growth Rate by Region: 2020 VS 2024 VS 2033 8.3 Asia-Pacific Data Wrangling Market Size by Region (2020-2025) 8.4 Asia-Pacific Data Wrangling Market Size by Region (2025-2033) 8.5 China 8.6 Japan 8.7 South Korea 8.8 Southeast Asia 8.9 India 8.10 Australia

9 Latin America

9.1 Latin America Data Wrangling Market Size (2020-2033) 9.2 Latin America Data Wrangling Market Growth Rate by Country: 2020 VS 2024 VS 2033 9.3 Latin America Data Wrangling Market Size by Country (2020-2025) 9.4 Latin America Data Wrangling Market Size by Country (2025-2033) 9.5 Mexico 9.6 Brazil

10 Middle East & Africa

10.1 Middle East & Africa Data Wrangling Market Size (2020-2033) 10.2 Middle East & Africa Data Wrangling Market Growth Rate by Country: 2020 VS 2024 VS 2033 10.3 Middle East & Africa Data Wrangling Market Size by Country (2020-2025) 10.4 Middle East & Africa Data Wrangling Market Size by Country (2025-2033) 10.5 Turkey 10.6 Saudi Arabia 10.7 UAE

11 Key Players Profiles

12 Analyst's Viewpoints/Conclusions

13 Appendix

13.1 Research Methodology 13.1.1 Methodology/Research Approach 13.1.2 Data Source 13.2 Disclaimer 13.3 Author Details

Request a Free Sample Copy of the Data Wrangling Report 2025 @ https://www.marketgrowthreports.com/enquiry/request-sample/103841

About Us: Market Growth Reports is a unique organization that offers expert analysis and accurate data-based market intelligence, aiding companies of all shapes and sizes to make well-informed decisions. We tailor inventive solutions for our clients, helping them tackle any challenges that are likely to emerge from time to time and affect their businesses.

[rawstorys]

The survey reveals that the vast majority of CEOs are afraid of losing their functions in front of artificial intelligence

Ai Company Dataiku A survey said that most executives are concerned about losing their functions to artificial intelligence. Business leaders are also concerned about the transition very slow, and for fear that they will lose their jobs in front of the Chief Executive of the Rapid Executive. What is the executive managers really thinking about artificial intelligence? The question by Dataiku,…

[aitoolswhitehattoolbox]

Manager, Development - Tax, Finance ((Python /Pyspark))

Job Summary Work as a Senior Developer for a Strategic Tax Reporting application under Finance Technology. Individual will be responsible for end to end Development, Testing and Implementation of Data solutions using Dataiku tool, with Python, Spark, Hadoop and Hive as the core programming languages and frameworks to develop data products, API’s and integrate with other applications within the…

[techviewindustry]

Cloud AI Market Analysis: Regulatory Landscape and Implications for Industry

The recently released Fortune Business Insights research on the Global Cloud AI Market survey report provides facts and statistics regarding market structure and size. Global Cloud AI Market Size 2025 Research report presents an in-depth analysis of the Global Market size, growth, share, segments, manufacturers, and forecast, competition landscape and growth opportunity. The research’s goal is to provide market data and strategic insights to help decision-makers make educated investment decisions while also identifying potential gaps and development possibilities.

Get Sample PDF Brochure: https://www.fortunebusinessinsights.com/enquiry/request-sample-pdf/108878

Companies Profiled in the Global Cloud AI Market:

Microsoft Corporation (U.S.)

Amazon Web Services, Inc. (U.S.)

Google LLC (U.S.)

IBM Corporation (U.S.)

Oracle Corporation (U.S.)

Alibaba Cloud (China)

H2O.ai (U.S.)

Salesforce, Inc. (U.S.)

Tencent (China)

Dataiku (U.S.)

Factors Driving Demand in the Cloud AI Market:

Several factors contribute to the growing demand for Cloud AI solutions globally. One key driver is the need for scalable and cost-effective AI infrastructure. Cloud-based AI services allow organizations to scale their AI initiatives based on demand, avoiding the upfront costs and complexities associated with building and maintaining on-premises AI infrastructure. This scalability is particularly advantageous for businesses with fluctuating workloads and evolving AI requirements.

Moreover, the increasing awareness of the business benefits of AI, including improved decision-making, automation, and enhanced customer experiences, drives the adoption of Cloud AI solutions. Organizations recognize the transformative potential of AI technologies and turn to cloud providers to access AI capabilities seamlessly. Additionally, the trend towards digital transformation and data-driven decision-making fuels the demand for AI solutions that can be easily integrated into existing cloud environments.

What exactly is included in the Report?

– Industry Trends and Developments: In this section, the authors of the research discuss the significant trends and developments that are occurring in the Cloud AI Market place, as well as their expected impact on the overall growth.

– Analysis of the industry’s size and forecast: The industry analysts have provided information on the size of the industry from both a value and volume standpoint, including historical, present and projected figures.

– Future Prospects: In this portion of the study market participants are presented with information about the prospects that the Cloud AI Market is likely to supply them with.

– The Competitive Landscape: This section of the study sheds light on the competitive landscape of the Cloud AI Market by examining the important strategies implemented by vendors to strengthen their position in the global market.

– Study on Industry Segmentation: This section of the study contains a detailed overview of the important Cloud AI Market segments, which include product type, application, and vertical, among others.

– In-Depth Regional Analysis: Vendors are provided with in-depth information about high-growth regions and their particular countries, allowing them to place their money in more profitable areas.

This Report Answers the Following Questions:

What are the Cloud AI Market growth drivers, hindrances, and dynamics?

Which companies would lead the market by generating the largest revenue?

How will the companies surge the processes adoption amid the COVID-19 pandemic?

Which region and segment would dominate the Cloud AI Market in the coming years?

Cloud AI Market Segments:

By Component

Solution

Services

By Technology

Machine Learning (ML)

Deep Learning

Natural Language Processing (NLP)

Others (Computer Vision, Data Analytics, etc.)

By Function

Finance

Marketing & Sales

Supply Chain Management

Human Resources

Others (Operations, etc.)

By End-Users

BFSI

IT and Telecommunications

Healthcare

Retail and Consumer Goods

Media and Entertainment

Others (Automotive, Education, etc.)

Table Of Content:

1. Introduction 1.1. Research Scope 1.2. Market Segmentation 1.3. Research Methodology 1.4. Definitions and Assumptions

2. Executive Summary

3. Market Dynamics 3.1. Market Drivers 3.2. Market Restraints 3.3. Market Opportunities

4. Key Insights 4.1 Global Statistics — Key Countries 4.2 New Product Launches 4.3 Pipeline Analysis 4.4 Regulatory Scenario — Key Countries 4.5 Recent Industry Developments — Partnerships, Mergers & Acquisitions

5. Global Cloud AI Market Analysis, Insights and Forecast 5.1. Key Findings/ Summary 5.2. Market Analysis — By Product Type 5.3. Market Analysis — By Distribution Channel 5.4. Market Analysis — By Countries/Sub-regions

……………

11. Competitive Analysis 11.1. Key Industry Developments 11.2. Global Market Share Analysis 11.3. Competition Dashboard 11.4. Comparative Analysis — Major Players

12. Company Profiles

12.1 Overview 12.2 Products & Services 12.3 SWOT Analysis 12.4 Recent developments 12.5 Major Investments 12.6 Regional Market Size and Demand

13. Strategic Recommendations

TOC Continued……………….

About Us:

Fortune Business Insights™ Delivers Accurate Data And Innovative Corporate Analysis, Helping Organizations Of All Sizes Make Appropriate Decisions. We Tailor Novel Solutions For Our Clients, Assisting Them To Address Various Challenges Distinct To Their Businesses. Our Aim Is To Empower Them With Holistic Market Intelligence, Providing A Granular Overview Of The Market They Are Operating In.

Contact Us:

Fortune Business Insights™ Pvt. Ltd.

US:+1 424 253 0390

UK:+44 2071 939123

APAC:+91 744 740 1245

Email:Sales@Fortunebusinessinsights.Com

[ericvanderburg]

Dataiku Optimizer for Snowflake is Now Available to Help Customers Maximize the Efficiency of Their Analytics Projects

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[ai-network]

Alteryx Data & Analytics

Alteryx Data & Analytics Platform: A Comprehensive Report

Alteryx Data & Analytics provide a comprehensive self-service analytics solution that caters to various data blending, preparation, and advanced analytics tasks. It delivers powerful features, predictive modeling, and spatial analysis capabilities. Introduction Alteryx is a powerful and versatile data analytics and automation platform designed to empower businesses to transform raw data into actionable insights. By unifying data preparation, blending, analysis, and visualization capabilities in a user-friendly interface, Alteryx democratizes analytics, enabling both technical and non-technical users to participate in the data-driven decision-making process. Key Components and Features - Alteryx Designer: The core of the platform, Designer offers a drag-and-drop workflow environment with over 300 pre-built tools for data access, preparation, blending, analysis, and output. Its intuitive interface allows users to visually design and automate data workflows without extensive coding knowledge. - Alteryx Server: This component enables the sharing and scheduling of workflows built in Designer, facilitating collaboration and broader access to analytics across the organization. It also enhances performance by leveraging server resources for complex data processing tasks. - Alteryx Analytics Cloud: This cloud-based platform provides a unified environment for data preparation, blending, and advanced analytics. It integrates Designer Cloud powered by Trifacta, offering AI-driven data preparation capabilities and enhanced collaboration features. - Alteryx Machine Learning: This cloud-native solution democratizes machine learning by providing an intuitive interface for building, training, and deploying predictive models. It automates key tasks like feature engineering and model selection, making machine learning accessible to a wider audience. Key Capabilities and Benefits - Data Preparation and Blending: Alteryx excels at simplifying the often complex and time-consuming process of data preparation. It allows users to connect to and integrate data from various sources, cleanse and transform data, and prepare it for analysis. - Advanced Analytics: The platform supports a wide range of analytical techniques, including statistical analysis, predictive modeling, spatial analysis, and text mining. This enables users to uncover deeper insights and make more informed decisions. - Automation and Scalability: Alteryx enables the automation of repetitive data tasks, freeing up analysts to focus on higher-value activities. Its server-based architecture ensures scalability to handle growing data volumes and user demands. - Improved Decision-Making: By providing self-service access to data and analytics, Alteryx empowers users across the organization to make data-driven decisions, leading to improved business outcomes. Use Cases Alteryx finds applications across various industries and departments, including: - Marketing: Customer segmentation, campaign analysis, and marketing performance optimization. - Sales: Sales forecasting, lead scoring, and territory analysis. - Operations: Supply chain optimization, fraud detection, and risk management. - Finance: Financial reporting, budgeting, and forecasting. - Human Resources: Workforce analytics, talent acquisition, and employee retention. Competitive Landscape Alteryx competes with other data analytics and business intelligence platforms, including: - Tableau: Focuses on data visualization and exploration. - Power BI: Microsoft's business intelligence tool with strong integration with other Microsoft products. - Qlik Sense: Offers associative data exploration and visualization capabilities. - Dataiku: Provides a collaborative platform for data scientists and business analysts. Strengths and Weaknesses Strengths: - User-friendly interface with drag-and-drop functionality. - Wide range of pre-built tools for data preparation and analysis. - Strong automation capabilities for repetitive tasks. - Scalable architecture to handle large data volumes. - Extensive community and support resources. Weaknesses: - Can be expensive for smaller organizations or individual users. - Some advanced features require coding knowledge. - Integration with certain data sources can be challenging. Conclusion Alteryx is a comprehensive and powerful data analytics platform that simplifies the process of turning data into actionable insights. Its user-friendly interface, extensive capabilities, and automation features make it a valuable tool for businesses of all sizes. By empowering users across the organization to participate in the data-driven decision-making process, Alteryx helps drive business growth and improve outcomes. Additional Resources: - Alteryx Website: https://www.alteryx.com/ - Alteryx Community: https://community.alteryx.com/ - Alteryx Academy: https://community.alteryx.com/t5/Alteryx-Academy/ct-p/alteryx-academy Read the full article

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AI Machine Learning (ML) vs. Deep Learning (DL)

You may have heard of these buzz words machine learning (ML) and deep learning (DL), but what does it really mean?

ML is a subset of artificial intelligence that focuses on developing algorithms that allow machines to learn from and make decisions based on that data. It can be supervised, unsupervised and reinforced learning. You can find this in applications such as spam detection, predictive analytics and fraud detection.

DL is a subset of ML that uses neural networks with many layers to model complex patterns in large datasets. This has been inspired by the structure and function of the human brain. This is useful for handling large amounts of unstructured data and excels in tasks like image and speech recognition. You can find this in applications such as autonomous vehicles, natural language processing, image and voice recognition.

Some leading businesses that top the charts in ML and DL include:

IBM

Microsoft

Amazon Web Services

Dataiku

Databricks

H20.ai

SAS

DataRobot, Inc.

Nvidia

OpenAI

Apple

Google

I would suggest learning as much as you can about it!

R. J. Davies

A Riveting Jacked-In Dreamy Mind-Bender

RJ Davies - Science Fiction Author, Maddox Files, Novels

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The benefits of AI in the banking sector are already well recognised in areas such as improving customer experience, boosting efficiency and automating processes. However, the applications in AI are still evolving and have yet to reach the full potential. How else can AI transform banking?   ITNews Asia gets the lowdown from Sophie Dionnet, Global Vice President, Product and Solutions, Dataiku, and discusses how significant the impact of AI will be.  iTNews Asia: How is AI changing the financial industry in APAC?    AI is revolutionising how banks and financial institutions across Asia Pacific operate. Take customer service, for example. The days of waiting on hold to resolve a simple query are fading, thanks to AI-powered chatbots and virtual assistants like DBS Bank’s Digibank. These tools have dramatically reduced customer friction by offering instant, 24/7 support, whether it’s checking account balances or facilitating transactions.   AI's impact on financial services, however, extends far beyond marketing and customer service and plays a vital role in risk management. For banks and other financial institutions, doing risk modelling is a core activity. With machine learning, they have an opportunity to augment risk modelling by incorporating newer risk factors in their approaches, uncovering new patterns, and be in a position to make better risk-adjusted business decisions on critical activities such as credit allocation or Know Your Customer (KYC).  These advanced capabilities are crucial in a digital finance environment to safeguard both institutions and their customers.   The financial industry’s perception of AI is also evolving. In Singapore, research indicates that in 2023 alone, AI and machine learning initiatives at DBS Bank contributed approximately US$ 275.9 million (S$ 370 million) in incremental economic value through revenue growth and cost savings. A recent study by  Accenture supports this, predicting that banks can boost their productivity by as much as 30 percent using generative AI over the next three years.   Materialising these efficiency gains takes different shapes and forms: from tedious analytics production acceleration down to financial or compliance reporting Gen AI-assisted production, opportunities for banks to streamline their day-to-day activities are massive, with the potential for a significantly reduced cost base down the line.   iTNews Asia: Banks very much started using AI in areas such as automation and data analysis in areas such as customer support in retail banking, sales and marketing. Chatbots, for example, are now a staple in customer service. With the increasing use of generative AI, what further financial and banking services are being transformed? How is the customer experience being reshaped?  While the full potential of Generative AI in banking is still unfolding, its immediate applications are already making a significant impact, particularly in areas where banks have already begun leveraging AI, such as automation and data analysis in customer support, sales, and marketing. In fact, Deloitte predicts that the top 14 global investment banks can boost their front-office productivity by as much as 27 to 35 percent using Generative AI.  Investment banks have long looked to streamline front-office tasks. While the concept of a fully autonomous "robo-banker" is not yet feasible, current AI applications can significantly enhance workflows. For instance, Generative AI can make financial documents more accessible by providing queryable formats, allowing junior bankers to retrieve relevant information quickly.    Additionally, Generative AI can assist in scanning internal deal documents and pitch decks, reducing research time and improving overall productivity. These improvements not only streamline workflows but enhance the quality of life for junior staff, who often bear the brunt of time-intensive tasks.  Generative AI also presents opportunities for improving CRM systems. Banks currently use predictive modelling to identify clients likely to undergo significant corporate events. By incorporating Natural Language Processing (NLP) features derived from news and management commentary, AI can improve the accuracy of predictive models, enabling bankers to identify high-value clients and tailor their outreach strategies more effectively.    Overall, the technology's adoption must be accompanied by rigorous risk management and regulatory frameworks to ensure its safe, accurate, and effective use.    iTNews Asia: Banks have traditionally prioritised security, process organization and risk management. Can the use of AI deliver real value, for example in risk assessment or fraud prevention? What are the future possibilities?  One of the most significant opportunities AI presents for banks is the ability to process vast amounts of data in real time. This capability allows banks to gain deep insights into customer behaviour, identify potential risks, and detect fraudulent activities. AI-driven algorithms can analyse transaction patterns, spot anomalies, and trigger alerts for suspicious activity, enabling banks to proactively safeguard their customers and assets.  For example, BGL BNP Paribas looked to strengthen key risk control processes through advanced analytics. Although the bank already had a machine learning model in place for advanced fraud detection, with limited visibility and data science resources, the model remained largely static.   When changing the model, the challenge was to harness a data-driven approach across all parts of the organisation. This initiative brought together data analysts and business users from the fraud department, along with data scientists from BGL BNP Paribas’ data lab and Dataiku. Through this collaboration, the bank successfully developed a new fraud detection prototype that delivered clear business value.   Ultimately, as AI technology continues to evolve, the possibility of its application to traditional banking will remain vast. By embracing AI, traditional banks can position themselves for long-term success in an increasingly competitive and dynamic landscape.    iTNews Asia: What do you see as the challenges of implementing AI in banking? Do you see issues in governance, transparency in the use of data, data privacy? How can we overcome them?  One of the primary concerns is the complexity of AI models, which can often make it difficult to understand how they arrive at their predictions. AI models often operate as "black boxes," making it difficult to explain their decision-making processes and leading to unintended biases. The usage of LLMs is only amplifying these concerns.   If built without the right explainability, AI use cases cannot be applied to critical banking activities, notably as they would be in no position to pass audits from regulators or simply would fail to be used by the business functions for lack of trust and maintainability.   On the other end of the spectrum, banking decisions are largely supported by large volumes of analysis usually done in outdated tools like spreadsheets, including for critical analytics. These legacy systems, while familiar, hinder growth and efficiency, ultimately creating a compliance burden that will need to be addressed down the line.   Overcoming these (legacy) challenges requires a multi-faceted approach. Banks must invest in developing AI expertise among their staff, including risk managers and compliance professionals. Additionally, implementing comprehensive governance structures that cover the entire AI lifecycle—from development to deployment and monitoring—is essential to embed rigorous risk management practices in AI system building.  - Sophie Dionnet, Global Vice President, Product and Solutions, Dataiku Advanced platforms provide a centralised, governed environment for data processing and model development, reducing barriers to governance enforcement and augmenting compliance readiness. Ensuring there is no gap between data pipelining and modelling also plays a critical role in improving system accuracy and auditability.    iTNews Asia: Not all banks in APAC are embracing AI at the same speed – a lot depends on their data maturity and AI readiness. How can they prepare? What should banks do to get themselves AI-ready?  To put it simply, a tailored approach is essential. The crucial first step is to assess the bank’s current capabilities and challenges. This involves evaluating existing data infrastructure, identifying the level of AI understanding and adoption, and pinpointing specific business problems that AI could solve. This assessment will provide a foundation for developing a tailored roadmap for AI adoption.  Improving data foundations is an important part of this process - this is essentially the fuel that powers AI models and enables accurate predictions and insights. Banks must invest in improving data quality, centralising data management, and upgrading data infrastructure to ensure that data is accessible, reliable, and suitable for AI use.   Concurrently, developing AI skills and expertise to develop, implement, and maintain AI solutions is essential. This can be achieved through hiring or training skilled individuals, partnering with external providers, and providing ongoing training opportunities.   Ultimately, choosing the right AI platform is a critical decision and will be essential to maximising the benefits of adoption. Banks should seek a balance between high power and low risk. Platforms like Dataiku allow users to break away from spreadsheets, build intelligent and potent (often machine learning) models, visualise their data, and build value-adding insights swiftly and without friction. These features allow stakeholders across the firm to work collaboratively on the same projects with clear, governable, and auditable oversight every step of the way, minimising risk while retaining power. 

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#uxjobs | UX Designer https://remotive.com/remote-jobs/design/ux-designer-solutions-1922904?utm_source=dlvr.it&utm_medium=tumblr