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Tag: Cloud Storage – The Think Curiouser

Market Overview of Cloud Storage Market

The Cloud Storage market report provides a detailed analysis of global market size, regional and country-level market size, segmentation market growth, market share, competitive Landscape, sales analysis, impact of domestic and global market players, value chain optimization, trade regulations, recent developments, opportunities analysis, strategic market growth analysis, product launches, area marketplace expanding, and technological innovations.

The global Cloud Storage market size is expected to gain market growth in the forecast period of 2020 to 2025, with a CAGR of xx%% in the forecast period of 2020 to 2025 and will expected to reach USD xx million by 2025, from USD xx million in 2019.

Get PDF Sample Copy of this Report to understand the structure of the complete report: (Including Full TOC, List of Tables & Figures, Chart) @ https://www.researchmoz.com/enquiry.php?type=S&repid=2823413&source=atm

Market segmentation

Cloud Storage market is split by Type and by Application. For the period 2015-2025, the growth among segments provide accurate calculations and forecasts for sales by Type and by Application in terms of volume and value. This analysis can help you expand your business by targeting qualified niche markets.

segment by Type, the product can be split intoPersonal Cloud StoragePublic Cloud StoragePrivate Cloud StorageHybrid Cloud Storage

Market segment by Application, split intoEnterpriseGovernmentPersonalOther

Based on regional and country-level analysis, the Cloud Storage market has been segmented as follows:North AmericaUnited StatesCanadaEuropeGermanyFranceU.K.ItalyRussiaNordicRest of EuropeAsia-PacificChinaJapanSouth KoreaSoutheast AsiaIndiaAustraliaRest of Asia-PacificLatin AmericaMexicoBrazilMiddle East & AfricaTurkeySaudi ArabiaUAERest of Middle East & Africa

Regional analysis is another highly comprehensive part of the research and analysis study of the global Cloud Storage market presented in the report. This section sheds light on the sales growth of different regional and country-level Cloud Storage markets. For the historical and forecast period 2015 to 2025, it provides detailed and accurate country-wise volume analysis and region-wise market size analysis of the global Cloud Storage market.

Do You Have Any Query Or Specific Requirement? Ask to Our Industry [emailprotected] https://www.researchmoz.com/enquiry.php?type=E&repid=2823413&source=atm

The report offers in-depth assessment of the growth and other aspects of the Cloud Storage market in important countries (regions), including:

North America (United States, Canada and Mexico)

Europe (Germany, France, UK, Russia and Italy)

Asia-Pacific (China, Japan, Korea, India and Southeast Asia)

South America (Brazil, Argentina, etc.)

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

Cloud Storage competitive landscape provides details by vendors, including company overview, company total revenue (financials), market potential, global presence, Cloud Storage sales and revenue generated, market share, price, production sites and facilities, SWOT analysis, product launch. For the period 2015-2020, this study provides the Cloud Storage sales, revenue and market share for each player covered in this report.

The key players covered in this studyOneDriveDropboxGoogle DriveBoxpCloudMegaAmazon DriveSpiderOakBaiduAlibabaTencentMicrosoft

You can Buy This Report from Here @ https://www.researchmoz.com/checkout?rep_id=2823413&licType=S&source=atm

The content of the study subjects, includes a total of 15 chapters:

Chapter 1, to describe Cloud Storage product scope, market overview, market opportunities, market driving force and market risks.

Chapter 2, to profile the top manufacturers of Cloud Storage, with price, sales, revenue and global market share of Cloud Storage in 2018 and 2019.

Chapter 3, the Cloud Storage competitive situation, sales, revenue and global market share of top manufacturers are analyzed emphatically by landscape contrast.

Chapter 4, the Cloud Storage breakdown data are shown at the regional level, to show the sales, revenue and growth by regions, from 2015 to 2020.

Chapter 5, 6, 7, 8 and 9, to break the sales data at the country level, with sales, revenue and market share for key countries in the world, from 2015 to 2020.

Chapter 10 and 11, to segment the sales by type and application, with sales market share and growth rate by type, application, from 2015 to 2020.

Chapter 12, Cloud Storage market forecast, by regions, type and application, with sales and revenue, from 2020 to 2025.

Chapter 13, 14 and 15, to describe Cloud Storage sales channel, distributors, customers, research findings and conclusion, appendix and data source.

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ResearchMoz

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USA-Canada Toll Free: 866-997-4948

Email: [emailprotected]

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Tag: Cloud Storage - The Think Curiouser

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Global Cloud Storage Software Market Growth, Demand And Threats Analysis 2020 By Regional Overview Of Leading Players, Types, Application and Forecast…

The effect of the Covid-19 outbreak on the Cloud Storage Software industry, involving possible opportunities and challenges, drivers and risks, is also investigated and evaluated in this study. Based on various scenarios (optimistic, pessimistic, very optimistic, most likely, etc.), we present the impact assessment of the Covid-19 effects on Cloud Storage Software maker and market growth forecast 2020-2026.

A detailed analysis of global Cloud Storage Software market size, regional and country market size, market segmentation growth , market share, competitive landscape, sales analysis, impact of domestic and global market players, value chain analysis, trade regulations, recent developments, opportunities analysis, strategic market growth analysis, product launches, area marketplace analysis is provided in the Cloud Storage Software market report.

Global Cloud Storage Software Market research analyzes vital geographical regions, provides an in-depth evaluation including key market trends according to their growth, upcoming technologies, industry drivers, challenges, regulatory policies, key players profiles.

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HPEAmazon Web ServicesHuawei TechnologiesMicrosoftOracleRackspace HostingRed HatIBMHitachi Data SystemsCA TechnologiesNetappDell EMCGoogleVMware

Key Questioned answered in Market report:

Cloud Storage Software Market Segmentation

Cloud Storage Software Market Segment by Application, covers:

BFSIGovernment & EducationHealthcareTelecom & ITRetailManufacturingMedia & EntertainmentOthers

Cloud Storage Software Market Fragment by Types can be classified into:

Private CloudPublic CloudHybrid Cloud

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In this report, the years considered to estimate the global market size of Cloud Storage Software are following: Historical Year: 2015-2018 Base Year: 2019 Estimated Year: 2020 Forecast Year 2020 to 2026

The research process involved the study of various factors affecting the Cloud Storage Software industry, including the government policy, market environment, competitive landscape, historical data, key tecnological advancement and current trends shaping the market, present development trends in the market, and the technical progress in related industry, and market risks, opportunities, market barriers and challenges.

Global Cloud Storage Software Market Details Based On Regions

Cloud Storage Software Market in North America (USA, Canada and Mexico).

Europe Cloud Storage Software Market(Germany, France, UK, Russia and Italy).

Cloud Storage Software Market in Asia-Pacific (China, Japan, Korea, India and South-east Asia).

Latin America Cloud Storage Software Market, Middle and Africa.

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Global Cloud Storage Software Market Growth, Demand And Threats Analysis 2020 By Regional Overview Of Leading Players, Types, Application and Forecast...

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Cloud Object Storage Market Analysis 2020-2027: by Key Manufacturers with Countries, Types, Application and Forecast Till 2027 – The Think Curiouser

Cloud object storage is the secure cloud storage service that is used to store the unstructured data. It can store, process, and access massive amounts of data and features imperceptible bandwidth and capacity expansion, making it a perfect data pool for big data computation and analytics. Growing digitization across the globe coupled with a rise in the adoption of technologies such as big data analytics, Internet of things (IoT), and cloud computing is anticipated to fuel the growth of the cloud object storage market over the forecast period.

The rising demand for data security and the protection of data by various enterprises is a major factor booming the growth of the cloud object storage market. Further, a rise in demand for technologically upgraded services and high demand for the fast transfer of data is likely to influence the demand for the cloud object storage market. However, piracy concerns might hinder the growth of the cloud object storage market. The increasing adoption of cloud object storage as it minimizes the IT infrastructure cost and increasing demand for a cost-effective solution from enterprises are expected to drive the growth of the cloud object storage market.

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The Major Market Player Included in This Report are:

Alibaba Cloud Amazon Web Services, Inc. Cisco Systems, Inc. Google LLC Hewlett Packard Enterprise Development LP Hitachi Vantara Corporation IBM Corporation Microsoft Corporation Oracle Corporation Tencent Cloud

Scope of the Report:

The research on the Cloud Object Storage market concentrates on extracting valuable data on swelling investment pockets, significant growth opportunities, and major market vendors to help understand business owners what their competitors are doing best to stay ahead in the competition. The research also segments the Cloud Object Storage market on the basis of end-user, product type, application, and demography for the forecast period 20202027. Detailed analysis of critical aspects such as impacting factors and competitive landscape are showcased with the help of vital resources, which include charts, tables, and info graphics.

For more clarity on the real potential of the Cloud Object Storage market for the forecast period 20202027, the study provides vital intelligence on major opportunities, threats, and challenges posed by the industry. Additionally, a strong emphasis is laid on the weaknesses and strengths of a few prominent players operating in the same market. Quantitative assessment of the recent momentum brought about by events such as collaborations, acquisition and mergers, product launches and technology innovation empower product owners, as well as marketing professionals and business analysts make a profitable decision to reduce cost and increase their customer base.

Geographically, this report focuses on product sales, value, market share, and growth opportunity in key regions such as United States, Europe, China, Japan, Southeast Asia, and India.

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Cloud Object Storage Market Analysis 2020-2027: by Key Manufacturers with Countries, Types, Application and Forecast Till 2027 - The Think Curiouser

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Cloud Storage Software Market (2020 to 2025) | Growing Application in Industry, Presents Opportunities and Demand Analysis, Growth, Size and Share -…

This report besides representing detailed synopsis of the current Cloud Storage Software Market scenario, this section of the report also includes versatile details on the overall ecosystem, key trends, Market catalysts as well as threats and challenges that seem to significantly impact revenue generation in the Cloud Storage Software Market.

Post persistent observation and research initiatives, this new research presentation on Global Cloud Storage Software Market has been recently released to ensure optimum scavenging of the Global Cloud Storage Software Market to make vital conclusions.

Access the PDF sample of the Cloud Storage Software Market report @ https://www.orbisresearch.com/contacts/request-sample/2496024?utm_source=Atish

The key players covered in this studyAmazon Web ServicesMicrosoftIBMHPEOracleDell EMCNetappGoogleVMwareCA TechnologiesRackspace HostingRed HatHitachi Data SystemsHuawei Technologies

The report shows discernable light on pertinent Market elements such as segment specific performance. The report meticulously gauges into past and current performance status of various segments to understand past growth outlook as well as current milestones that result in accurate forecast predictions about Global Cloud Storage Software Market.

Make an enquiry of Cloud Storage Software Market report @ https://www.orbisresearch.com/contacts/enquiry-before-buying/2496024?utm_source=Atish

Additional report components entail real-time status of segment categorization. For superlative reader comprehension, this versatile report segregates key Market components into product and application based compartments. Further, the report aptly explains regional segmentation highlighting major growth hotspots along with relevant developments in the regions.

Market segment by Type, the product can be split intoPrivate CloudPublic CloudHybrid Cloud

Market segment by Application, split intoBFSIGovernment & EducationHealthcareTelecom & ITRetailManufacturingMedia & EntertainmentOthers

Browse the complete Cloud Storage Software Market report @ https://www.orbisresearch.com/reports/index/global-cloud-storage-software-market-size-status-and-forecast-2019-2025?utm_source=Atish

A distinctive DROT analysis section is also included in the report to closely scout for teeming Market opportunities, major threats and challenges that tend to shun growth through the forecast span.

A close review of opportunity mapping as well as barrier analysis across specific growth pockets allow Market participants to augment future-ready investment decisions.

A dedicated section on pandemic crisis and effective management guide have also been included in the report to comply with reader discretion.

About Us:Orbis Research (orbisresearch.com) is a single point aid for all your Market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required Market research study for our clients.

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Cloud Storage Software Market (2020 to 2025) | Growing Application in Industry, Presents Opportunities and Demand Analysis, Growth, Size and Share -...

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Cloud Storage Market: Qualitative Analysis of the Leading Players and Competitive Industry Scenario, 2023 – The Think Curiouser

Market Overview of Cloud Storage Market

The Cloud Storage market report provides a detailed analysis of global market size, regional and country-level market size, segmentation market growth, market share, competitive Landscape, sales analysis, impact of domestic and global market players, value chain optimization, trade regulations, recent developments, opportunities analysis, strategic market growth analysis, product launches, area marketplace expanding, and technological innovations.

The global Cloud Storage market size is expected to gain market growth in the forecast period of 2020 to 2025, with a CAGR of xx%% in the forecast period of 2020 to 2025 and will expected to reach USD xx million by 2025, from USD xx million in 2019.

Get PDF Sample Copy of this Report to understand the structure of the complete report: (Including Full TOC, List of Tables & Figures, Chart) @ https://www.researchmoz.com/enquiry.php?type=S&repid=2823413&source=atm

Market segmentation

Cloud Storage market is split by Type and by Application. For the period 2015-2025, the growth among segments provide accurate calculations and forecasts for sales by Type and by Application in terms of volume and value. This analysis can help you expand your business by targeting qualified niche markets.

segment by Type, the product can be split intoPersonal Cloud StoragePublic Cloud StoragePrivate Cloud StorageHybrid Cloud Storage

Market segment by Application, split intoEnterpriseGovernmentPersonalOther

Based on regional and country-level analysis, the Cloud Storage market has been segmented as follows:North AmericaUnited StatesCanadaEuropeGermanyFranceU.K.ItalyRussiaNordicRest of EuropeAsia-PacificChinaJapanSouth KoreaSoutheast AsiaIndiaAustraliaRest of Asia-PacificLatin AmericaMexicoBrazilMiddle East & AfricaTurkeySaudi ArabiaUAERest of Middle East & Africa

Regional analysis is another highly comprehensive part of the research and analysis study of the global Cloud Storage market presented in the report. This section sheds light on the sales growth of different regional and country-level Cloud Storage markets. For the historical and forecast period 2015 to 2025, it provides detailed and accurate country-wise volume analysis and region-wise market size analysis of the global Cloud Storage market.

Do You Have Any Query Or Specific Requirement? Ask to Our Industry [emailprotected] https://www.researchmoz.com/enquiry.php?type=E&repid=2823413&source=atm

The report offers in-depth assessment of the growth and other aspects of the Cloud Storage market in important countries (regions), including:

North America (United States, Canada and Mexico)

Europe (Germany, France, UK, Russia and Italy)

Asia-Pacific (China, Japan, Korea, India and Southeast Asia)

South America (Brazil, Argentina, etc.)

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

Cloud Storage competitive landscape provides details by vendors, including company overview, company total revenue (financials), market potential, global presence, Cloud Storage sales and revenue generated, market share, price, production sites and facilities, SWOT analysis, product launch. For the period 2015-2020, this study provides the Cloud Storage sales, revenue and market share for each player covered in this report.

The key players covered in this studyOneDriveDropboxGoogle DriveBoxpCloudMegaAmazon DriveSpiderOakBaiduAlibabaTencentMicrosoft

You can Buy This Report from Here @ https://www.researchmoz.com/checkout?rep_id=2823413&licType=S&source=atm

The content of the study subjects, includes a total of 15 chapters:

Chapter 1, to describe Cloud Storage product scope, market overview, market opportunities, market driving force and market risks.

Chapter 2, to profile the top manufacturers of Cloud Storage, with price, sales, revenue and global market share of Cloud Storage in 2018 and 2019.

Chapter 3, the Cloud Storage competitive situation, sales, revenue and global market share of top manufacturers are analyzed emphatically by landscape contrast.

Chapter 4, the Cloud Storage breakdown data are shown at the regional level, to show the sales, revenue and growth by regions, from 2015 to 2020.

Chapter 5, 6, 7, 8 and 9, to break the sales data at the country level, with sales, revenue and market share for key countries in the world, from 2015 to 2020.

Chapter 10 and 11, to segment the sales by type and application, with sales market share and growth rate by type, application, from 2015 to 2020.

Chapter 12, Cloud Storage market forecast, by regions, type and application, with sales and revenue, from 2020 to 2025.

Chapter 13, 14 and 15, to describe Cloud Storage sales channel, distributors, customers, research findings and conclusion, appendix and data source.

Contact Us:

ResearchMoz

Tel: +1-518-621-2074

USA-Canada Toll Free: 866-997-4948

Email: [emailprotected]

About ResearchMoz

ResearchMoz is the one stop online destination to find and buy market research reports & Industry Analysis. We fulfil all your research needs spanning across industry verticals with our huge collection of market research reports. We provide our services to all sizes of organisations and across all industry verticals and markets. Our Research Coordinators have in-depth knowledge of reports as well as publishers and will assist you in making an informed decision by giving you unbiased and deep insights on which reports will satisfy your needs at the best price.

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Cloud Storage Market: Qualitative Analysis of the Leading Players and Competitive Industry Scenario, 2023 - The Think Curiouser

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3 ways Artificial Intelligence Will Help IT MSPs Do Better in 2021 – Channel Futures

Artificial intelligence and machine learning can help make ITSM processes more efficient.

CIOs are now using artificial intelligence (AI) and machine learning (ML) technologies to make IT service management processes more efficient.

A typical use case for artificial intelligence in ITSM involves natural language processing (NLP). User requests for IT services are automated using NLP. IT practitioners get a deeper understanding of their processes by applying machine learning (ML) to ITSM data. The natural language processing technology that powers virtual agents is very often integrated with channels that the employees are familiar with. Many organizations integrate virtual agents with chat services like Slack, where employees can directly communicate with the IT service desks.

ITSM systems generate large volumes of data, so applying machine learning to these systems makes sense. The data collected by these systems is large not only in volume but also in detail. All of this data helps us understand existing IT assets and processes, along with information about their ownership.

These insights help IT understand the real priorities of ITSM issues, work proactively instead of reactively, accelerate time to resolution and enhance employee productivity. In the current age of remote work, enhancing employee experience to ensure business continuity is at the top of every CIOs mind, and artificial intelligence will prove to be just the right technology to use to face this new challenge.

Lets look at the three ways artificial intelligence will help IT MSPs to do better in 2021.

Chatbots integrated with an ITSM environment can easily be used to categorize the problem in employee requests. For example, if an organization has integrated Freshservices Virtual Agent with MS Teams, it creates a channel for employees to raise a service request or resolve their issues. The chat interface is a familiar UI for the employees, and the chatbot will identify whether the employee has a service request or an incident to raise using machine learning.

Another important and time-consuming task normally performed by an agent or complex workflows is routing a ticket to the correct support groups. Chatbots will triage the incoming requests or incidents to the right support group, making the process a lot efficient.

The historical ticket data and an extensive ITSM knowledge base will help agents resolve various requests faster. However, this requires the admins/agents to create an extensive knowledge base covering a wide range of requests and incidents. The ability to directly convert a resolution email to a knowledge base article will help build a rich knowledge base repository. When a similar problem arises, AI and machine learning can be used to dig through this repository and present the closest match to resolve the issue faster.

A well-managed repository will also help with incident resolution throughout the solution. AI can provide advice that is as simple as a related or similar incident along with its history, or a solution article with words that match the current incident/request, thereby shortening the time taken to think through the issue from scratch.

Like employee onboarding, many requests to IT demand human staff hours to perform a series of complex tasks to fulfill the requests. Machine learning models watch and learn how humans carry on and execute them to automate them in the future. By recognizing patterns in the request types and execution methods, machine learning-based models make intelligent suggestions for even the most complex IT processes.

Hemalakshmi is a Product Expert with Freshworks. Her responsibility includes educating and helping industry peers and customers on best practices, tips and tricks, quick guides, and solutions around IT Service Management and its various use cases. In her 6+ years of experience in the core SaaS business applications serving as a product expert, Hema has worked with multiple businesses in helping them with their business needs and setting up their service desk solution Freshservice.Follow her onLinkedIn.

This guest blog is part of a Channel Futures sponsorship.

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3 ways Artificial Intelligence Will Help IT MSPs Do Better in 2021 - Channel Futures

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Leap And Learn: The Common Thread Of Artificial Intelligence Success Stories – Forbes

AI success is built on learning

Enterprises seeing real success with artificial intelligence have something in common: they are capable of learning quickly from their successes or failures and re-applying those lessons into the mainstream of their businesses.

Of course, theres nothing new about the ability to rinse, learn and repeat, which has been a fundamental tenet of business success for ages. But because AI is all about real-time, nanosecond responsiveness to a range of things, from machines to markets, the ability to leap and learn at a blinding pace has taken on a new urgency.

At this moment, only 10% of companies are seeing financial benefits from their AI initiatives, a survey of 3,000 executives conducted by Boston Consulting Group and MIT Sloan Management Review finds. There is a lot of AI going around: more than half, 57%, piloting or deploying AI up from 46% in 2017. In addition, at least 70% understand the business value proposition of AI. But financial results have been elusive.

So, what are the enlightened 10% doing to finally realize actual, tangible gains from AI? They do all the right things, of course, but theres an extra piece of the magic thrown in. For instance, scaling AI seen as the path to enterprise adoption has only limited value by itself. Adding the ability to embed AI into processes and solutions improves the likelihood of significant benefits dramatically, but only to 39%, the survey shows.

Successful AI adopters have figured out how to learn from their AI experiences and apply them in forward-looking ways to their businesses, the survey reports authors, led by Sam Ransbotham, conclude. Our survey analysis demonstrates that leaders share one outstanding feature they intend to become more adept learners with AI. This ability to learn and understand the potential and pitfalls of AI enable them to sense and respond quickly and appropriately to changing conditions, such as a new competitor or a worldwide pandemic, are more likely to take advantage of those disruptions.

In other words, they give executives and employees the space they need to better understand, adjust and adapt to AI-driven processes and figure out their roles in making it all work. Automation is not thrust upon them with no preparation or training. Realizing significant financial benefits with AI requires far more than a foundation in data, infrastructure, and talent, the researchers state. Even embedding AI in business processes is not enough.

Those organizations that lead the way with AI success pursue the following strategies:

They facilitate systematic and continuous learning between humans and machines. Organizational learning with AI isnt just machines learning autonomously. Or humans teaching machines. Or machines teaching humans, Ransbotham and his co-authors state. Its all three. Organizations that enable humans and machines to continuously learn from each other with all three methods are five times more likely to realize significant financial benefits than organizations that learn with a single method.

They develop multiple ways for humans and machines to interact. Deploying the appropriate interaction modes in the appropriate context is critical, the co-authors state. For example, some situations may require an AI system to make a recommendation and humans to decide whether to implement it. Some context-rich environments may require humans to generate solutions and AI to evaluate the quality of those solutions.

They change to learn, and learn to change. Successful initiatives dont just change processes to use AI; they change processes in response to what they learn with AI.

AI has great potential to expand our visions of where and how businesses can deliver greater service in the months and years ahead. But it requires more than simply installing new systems and processes and waiting to see the results. Its a continuous process of improvement and innovation,

Originally posted here:
Leap And Learn: The Common Thread Of Artificial Intelligence Success Stories - Forbes

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Re-Humanizing Fundraising With Artificial Intelligence – Stanford Social Innovation Review

(Photo by iStock/xijian)

Conventional wisdom about nonprofit fundraising considers these two statements equally true: 1) Acquiring new donors loses money, and 2) Future gifts from new donors make up for the money lost on acquisition.

Alas, only one of them is true. Acquiring new donors does, indeed, lose money, often estimated at 50 percent of the initial gift. However, according to Blackbaud, fewer than a quarter of those initial donors will renew their gift. The math gets even worse in out-years, as 60 percent of donors lapse year after year.

The reality is that most organizations spend an enormous amount of time frantically trying to refill their leaky bucket of donors. The result is a transactional approach to fundraising that requires constantly asking for donations rather than spending time getting to know donors, particularly donors who arent writing huge checks. Just because its the norm, however, doesnt make it good or effective, particularly during a pandemic when everyone is distracted, scared, and stretched.

We recently released a report funded by the Bill and Melinda Gates Foundation on using artificial intelligence (AI) for fundraising and philanthropy. The report outlines ways that nonprofits are beginning to use AI to increase giving, and while the fact that the most powerful technology in history can help nonprofits raise more money didnt surprise us, we were surprised by how much opportunity nonprofits have to use AI to re-imagine and re-humanize fundraising.

AI automates tasks that previously only humans could do. The field isnt newits been around for decadesbut its recently become much less expensive, making it available for everyday use and by smaller organizations.

AI tools for increasing fundraising currently include:

One example of a nonprofit putting AI tools into action is the 24-hour fundraising marathon Extra Life, a fundraising effort of Childrens Miracle Network Hospitals. Staff members were getting overwhelmed answering the same question from Canadian supporters, who wanted to know what currency Extra Life would use to process their donations. To ameliorate this, Extra Life added a chatbot to its donation page specifically to answer this question, and even used an algorithm to personalize the landing page so that the chatbot appeared only for Canadian donors.

The chatbot on the Extra Life website provides instant answers to common donor questions about things like conversion rates.

Another example is the Cure Alzheimer's Fund, which raised $1.2 million in donations using Gravytys AI-powered fundraising software. Gravyty drafts emails to existing donors based on their preferences and previous actions, and highlights donors who are on the cusp of lapsing. Staff members review the emails and cultivation plan, then send them out the door. Gravyty isnt just automating renewal letters; by helping fundraisers continuously improve the specific content and timing of messages to individual donors, its adding more intelligence into the fundraising system.

Similarly, Rainforest Action Network piloted software from Accessible Intelligence Limited in May 2020. This software recommends the right content to include in fundraising appeals (including writing style, specific ask, and even subject lines to test), as well as the right number and interval of communication touch points. As a result, open rates and signed online petitions increased significantly. More importantly, conversion of one-time donors to monthly donors increased 866 percent. (That is not a typo!)

Since the publication of our report, weve been thinking about the time development staff could save by using AI. What could change? What could staff do differently or better with this precious gift of time? We see a great opportunity for development teams to patch the holes in the leaky bucket of fundraising, and enable their organizations to move from transactional to relational fundraising, starting with these three activities:

1.Add retention rates to dashboards and budgetary calculations. We have served on many boards and cant recall one discussion focused on donor retention. Organizations need to measure and monitor donor retention rates over time. They also need to calculate the net cost of fundraising, as well as the cost of money raised through acquisition and lost through lapsed donors over time.

2.Put time for conversations with donors, clients, and volunteers on the calendar. Activities that arent on the calendar dont get done. Staff and leading volunteers (such as board members) need to spend time listening to donors and stop treating them like ATMs. Instead, they need to find out why the cause is important to each donornot just major donors, but donors at every leveland what makes them feel good or bad when they give.

3.Establish ethical-use guidelines around the use of AI. Its critically important that organizations use the incredible power of AI with great care. We recommend establishing an outside committee of advisors to discuss issues such as the use and storage of data, the need to inform people when they are talking to a robot and not a person, and careful monitoring of AI-powered efforts for racial and other biases.

One person we interviewed for our report said, AI cant fix bad fundraising practices. Our greatest fear is that nonprofit leaders will use the incredible speed and power of AI to supersize existing transactional fundraising practices. We implore them to take the care and time needed to create a new chapter in fundraising, where every person can be heard and where most donors stay with causes for years, not months.

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Re-Humanizing Fundraising With Artificial Intelligence - Stanford Social Innovation Review

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Artificial Intelligence Is Used To Understand The Geospatial World To Improve Business And Governmental Performance – Forbes

Artificial Intelligence

Recently, there was brief news about Microsoft Flight Simulator and a tower more than 200 stories tall created by a typo. As funny as that was, it missed the larger picture. Google Earth started a trend that has continued, and the virtualization of the world has proceeded at a rapid pace. It is now to the point where real business benefit is being gained by such work, supporting the application of artificial intelligence (AI) to even more problems.

There have been smaller discussions about virtualization and augmented reality, to analysis and improve performance in stores and other smaller spaces. However, similar to how a drive for better gaming led to NVIDIAs GPUs, which helped advance AI, the business of capturing a global image base to improve gaming can now help AI lend its skills to new areas.

Whats interesting is that the volume of geographic imaging is beginning to provide analytics to a wide range of businesses. Both businesses and governments are beginning to use the images to estimate forest conditions, crop yields, and other large scale issues. In another interesting application, analysis of buildings and other large structures is beginning to yield ROI on inspections.

One example is inspection of a type of structure called a floating oil tank. It is, as the name implies, an oil storage tank. Whats interesting is the roof floats on top of the oil, raising and lowering based on oil level. The Blackshark.ai system, which includes 200 GPUs, works with satellite imagery, the time stamp of the image, and the shadows provided by the facilities. It is then simple trigonometry to provide an estimate of oil volume. Note, this is something that is good for government oil reserve estimates and for insurance, but companies would want more detailed information.

In that example, the AI is in the computer vision component, it isnt required for the estimate creation. However, there are examples where AI can be used for additional analysis. Imagine a government trying to estimate energy usage or tax base depending on building type. A satellite image can be analyzed by an AI system which can identify building types by what is on the roof. The size of HVAC systems, for instance, can help to identify a buildings size and use type.

The image is the starting point for the analysis, said Michael Putz, CEO and Co-founder, Blackshark.ai. Semantic reconstruction is the process of adding semantic information needed for critical decisions by companies, governments, and individuals. Past computer vision systems have only enhanced images, leaving it to people to clarify items. Artificial intelligence can do the work of identifying objects, adding the semantics necessary to speed analysis and enhance the accuracy of decision making.

In the aftermath of events such as earthquakes, floods, and other natural disaster, comparisons to previous images can quickly prepare both governments, NGOs, and insurance companies in taking both faster and more effective action.

Rendering 2D images into 3D simulations also provide other business benefits. Consider wireless signal propagation. 3G and 5G have different broadcast features. Simulating geospatial features can aid coverage range and engineering cost analysis for optimal ROI for tower placement.

Notice the individuals mentioned Michael Putz. Think about semantic analysis and someones back yard. As AI is able to identify objects and even render 2D satellite images into 3D representations, that enhances the ability for homeowners and small businesses to work together to combine AI and VR to plan for changes. The example Mr. Putz provided was adding a pool to a yard. Being able to visualize that in 3D could help owners check line of site and see if other work, such as higher fences for privacy, might be needed.

At this point, I see the technology being focused on the higher end solutions, such as those for large companies and for government agencies. As with all new product arenas, advances will drive price down and the Cloud model will mean consumer applications will become profitable just not yet.

Geospatial image capture started off small, but has now grown to a massive scale. The addition of AI both improves computer vision and downstream analysis. This is another are where the world around us is being enhanced by artificial intelligence.

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Artificial Intelligence Is Used To Understand The Geospatial World To Improve Business And Governmental Performance - Forbes

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Python and artificial intelligence are the future so learn it all here for less than $5 a course – The Next Web

TLDR: The Ultimate Python and Artificial Intelligence Certification Bundle explore training in data science and how to build machines that think for themselves.

After 20 years as one of the undisputed kings of programming languages, Java may be about to relinquish its crown. For two decades, Java and C have held the top two spots on Tiobes programming language rankings.

After experiencing what Tiobe called an all-time low in popularity, falling over 4 percentage points in year-over-year usage rates, Java is now poised to see its no. 2 rankings usurped by the hard-charging Python.

And yes, C programming should be looking over its shoulder as well. Python and its monumental role in advanced programming technologies like machine learning and artificial intelligence have made it the fastest-growing coding discipline of the past decade.

You can learn Python from the ground up as well as some of its most important applications in The Ultimate Python and Artificial Intelligence Certification Bundle. Its now available for $39.96, over 90 percent off, from TNW Deals.

This package includes nine courses featuring almost 40 hours of training covering all things Python, from basic fundamentals through to how its used in some of the most in-demand tech fields working today.

Three courses Python: Introduction to Data Science and Machine Learning A-Z, Python for Beginners: Learn All the Basics of Python and Python For Beginners: The Basics For Python Development get the training underway with basic math concepts, data science introductions, programming dos and donts, as well as everything a new user needs to understand how and why Python works so well.

After a brief segue into a pair of courses centered around data organization and visualization using fellow data science stalwart R programming, the training then steps up to more advanced Python-related subjects: deep learning and the creation of artificial intelligence.

Keras Bootcamp for Deep Learning and AI in Python gives learners a grounding in using Keras, Googles powerful deep learning framework, to create artificial neural networks and the foundations of how machines are being constructed to think and act on their own. That learning expands in Image Processing and Analysis Bootcamp with OpenCV and Deep Learning in Python, where Python Tensorflow and Keras are used to help machines actually interpret images and extract meaning.

Deep learning models get deeper exploration in Master PyTorch for Artificial Neural Networks (ANN) and Deep Learning before learning how to speed up those processes by using H2O in Artificial Intelligence (AI) in Python: A H2O Approach.

The entire package is a nearly $1,800 collection of training, but by getting in on this bundle now, you can get each course at less than $5 each, only $39.99.

Prices are subject to change.

Read next: This highly rated Google Play Store language learning app is now on sale

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Python and artificial intelligence are the future so learn it all here for less than $5 a course - The Next Web

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