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Killexams : Business Intelligence and Analytics Software Market 2022-2028 is Recovering from Covid-19 Outbreak- More Details About Key Players and Future Analysis

Business Intelligence and Analytics Software market is anticipated to grow in the forecast period owing to driving factors such as increasing awareness towards healthcare of livestock and companion animals and government policies to immunize the animals. Moreover, the inflated R&D investment in this sector, new technology with better therapeutic application and raised quality standards presents the opportunity for the market.

Software application developed to acquire, convert, analyze and report data for BI (business intelligence) are known as business intelligence and analytics software. Furthermore, these tools are used to read information that have already been stored, not necessarily in data mart. Besides this, the tool helps front line users to design reports as well as perform analytics, ensuring less dependency on information technology department. Rapid deployment, power data mining, performance management, ultimate optimization and curbing implementation challenges are some of benefits offered by business intelligence and analytics software.

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Some of the Prominent/Emerging Players in Business Intelligence and Analytics Software Market:

  1. Adobe Systems
  2. IBM Corporation
  3. Microsoft Corporation
  4. Oracle Corporation
  5. Salesforce.com, Inc.
  6. SAP AG
  7. SAS Institute, Inc.
  8. Tableau Software Inc.
  9. Teradata Corporation
  10. Tibco Software Inc.

The Business Intelligence and Analytics Software market is expected to grow significantly in the coming years, rising in special focus on consumers. Moreover, increase in demand for medical samples on a global level, creating demand for swift services of Business Intelligence and Analytics Software.

Global Business Intelligence and Analytics Software market is segmented Based on type, the global business intelligence and analytics software market is segmented into business intelligence platform, content analytics, advance and predictive analytics, analytic data management, others. On the basis of deployment type, the market is segmented into cloud deployment, on-premise deployment, hybrid deployment Based on vertical, the market is bifurcated into IT and telecom, BFSI, media and entertainment, manufacturing, healthcare and lifesciences, others.

COVID-19 first began in Wuhan (China) during December 2019 and since then it has spread at a fast pace across the globe. The US, India, Brazil, Russia, France, the UK, Turkey, Italy, and Spain are some of the worst affected countries in terms confirmed cases and reported deaths. The COVID-19 has been affecting economies and industries in various countries due to lockdowns, travel bans, and business shutdowns. Shutdown of various plants and factories has affected the global supply chains and negatively impacted the manufacturing, delivery schedules, and sales of products in global market. Few companies have already announced possible delays in product deliveries and slump in future sales of their products. According to the current market situation, the report further assesses the present and future effects of the COVID-19 pandemic on the overall market, giving more reliable and authentic projections In addition to this, the global travel bans imposed by countries in Europe, Asia-Pacific, and North America are affecting the business collaborations and partnerships opportunities.

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The report specifically highlights the Business Intelligence and Analytics Software market share, company profiles, regional outlook, product portfolio, a record of the recent developments, strategic analysis, key players in the market, sales, distribution chain, manufacturing, production, new market entrants as well as existing market players, advertising, brand value, popular products, demand and supply, and other important factors related to the market to help the new entrants understand the market scenario better.

The “Global Business Intelligence and Analytics Software Market Analysis To 2028” is a specialized and in-depth study of the technology, media, and telecommunications industry with a special focus on the global market trend analysis. The Business Intelligence and Analytics Software market report aims to provide an overview of the Business Intelligence and Analytics Software market with detailed market segmentation by component, data type, deployment model, industry vertical, and geography. The global Business Intelligence and Analytics Software market is expected to witness high growth during the forecast period. The report provides key statistics on the market status of the leading Business Intelligence and Analytics Software market players and offers key trends and opportunities in the market.

The report provides a detailed overview of the industry including both qualitative and quantitative information. It provides an overview and forecast of the global market based on various segments. It also provides market size and forecast estimates from the year 2019 to 2028 with respect to five major regions, namely; North America, Europe, Asia-Pacific (APAC), the Middle East and Africa (MEA), and South America. The Business Intelligence and Analytics Software market by each region is later sub-segmented by respective countries and segments. The report covers the analysis and forecast of 18 countries globally along with the current trend and opportunities prevailing in the region.

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Killexams : Embedded analytics emerges to offer new level of business intelligence

Business analytics is an increasingly powerful tool for organisations, but one that is associated with steep learning curves and significant investments in infrastructure.

The idea of using data to drive better decision-making is well established. But the conventional approach – centred around reporting and analysis tools – relies on specialist applications and highly trained staff. Often, firms find they have to build teams of data scientists to gather the data and manage the tools, and to build queries.

This creates bottlenecks in the flow of information, as business units rely on specialist teams to interrogate the data, and to report back. Even though reporting tools have improved dramatically over the past decade, with a move from spreadsheets to visual dashboards, there is still too much distance between the data and the decision-makers.

Companies and organistions also face dealing with myriad data sources. A study from IDC found that close to four in five firms used more than 100 data sources and just under one-third had more than 1,000. Often, this data exists in silos.

As a result, suppliers have developed embedded analytics to bring users closer to the data and, hopefully, lead to faster and more accurate decision-making. Suppliers in the space include ThoughtSpot, Qlik and Tableau, but business intelligence (BI) and data stalwarts such as Informatica, SAS, IBM and Microsoft also have relevant capabilities.

Embedded analytics adds functionality into existing enterprise software and web applications. That way, users no longer need to swap into another application – typically a dashboard or even a BI tool itself – to look at data. Instead, analytics suppliers provide application programming interfaces (APIs) to link their tools to the host application.

Embedded analytics can be used to give mobile and remote workers access to decision support information, and even potentially data, on the move. This goes beyond simple alerting tools: systems with embedded analytics built in allow users to see visualisations and to drill down into live data.

And the technology is even being used to provide context-aware information to consumers. Google, for example, uses analytics to present information about how busy a location or service will be, based on variables such as the time of day.

Indeed, some suppliers describe embedded analytics as a “Google for business” because it allows users to access data without technical know-how or an understanding of analytical queries.

“My definition generally is having analytics available in the system,” says Adam Mayer, technical product director at Qlik. “That’s not your dedicated kind of BI tool, but more to the point, I think it’s when you don’t realise that you’re analysing data. It’s just there.”

The trend towards embedding analytics into other applications or web services reflects the reality that there are many more people in enterprises who could benefit from the insights offered by BI than there are users of conventional BI systems.

Firms also want to Improve their return on investment in data collection and storage by giving more of the business access to the information they hold. And with the growth of machine learning and artificial intelligence (AI), some of the heavy lifting associated with querying data is being automated.

“What we are trying to do is give non-technical users the ability to engage with data,” says Damien Brophy, VP for Europe, the Middle East and Africa (EMEA) at ThoughtSpot. “We’re bringing that consumer-like, Google-like experience to enterprise data. It is giving thousands of people access to data, as opposed to five or 10 analysts in the business who then produce content for the rest of the business.”

At one level, embedded analytics stands to replace static reports and potentially dashboards too, without the need to switch applications. That way, an HR or supply chain specialist can view and – to a degree – query data from within their HR or enterprise resource planning (ERP) system, for example.

A field service engineer could use an embedded analysis module within a maintenance application to run basic “what if” queries, to check whether it is better to replace a part now or carry out a minor repair and do a full replacement later.

Embedded analytics to help decision-making

Also, customer service agents are using embedded analytics to help with decision-making and to tailor offers to customers.

Embedded systems are designed to work with live data and even data streams, even where users do not need to drill down into the data. Enterprises are likely to use the same data to drive multiple analysis tools: the analytics, business development or finance teams will use their own tools to carry out complex queries, and a field service or customer service agent might need little more than a red or green traffic light on their screen.

“The basic idea is that every time your traditional reporting process finds the root cause of a business problem, you train your software, either by formal if-then-else rules or via machine learning, to alert you the next time a similar situation is about to arise,” says Duncan Jones, VP and principal analyst at Forrester.

“For instance, suppose you need to investigate suppliers that are late delivering important items. In the old approach, you would create reports about supplier performance, with on-time-delivery KPI and trends and you’d pore through it looking for poor performers.

“The new approach is to create that as a view within your home screen or dashboard, continually alerting you to the worst performers or rapidly deteriorating ones, and triggering a formal workflow for you to record the actions you’ve taken – such as to contact that supplier to find out what it is doing to fix its problems.”

This type of alerting helps businesses, because it speeds up the decision-making process by providing better access to data that the organisation already holds.

“It’s partly businesses’ need to move faster, to react more quickly to issues,” says Jones. “It’s also evolution of the technology to make embedded alert-up analytics easier to deliver.”

Embedded analytics suppliers are also taking advantage of the trend for businesses to store more of their data in the cloud, making it easier to link to multiple applications via APIs. Some are going a step further and offering analytical services too: a firm might no longer need expertise in BI, as the supplier can offer its own analytical capabilities.

Again, this could be via the cloud, but serving the results back to the users in their own application. And it could even go further by allowing different users to analyse data in their own workflow-native applications.

A “smart” medical device, such as an asthma inhaler, could provide an individual’s clinical data to their doctor, but anonymised and aggregated data to the manufacturer to allow them to plan drug manufacturing capacity better.

“Data now is changing so quickly, you really need intraday reporting,” says Lee Howells, an analytics specialist at PA Consulting. “If we can put that in on a portal and allow people to see it as it happened, or interact with it, they are then able to drill down on it.

“It’s putting that data where employees can use it and those employees can be anyone from the CEO to people on operations.”

But if the advantage of embedded analytics lies in its ability to tailor data to the users’ roles and day-to-day applications, it still relies on the fundamentals of robust BI systems.

Firms considering embedded analytics need to look at data quality, data protection and data governance.

They also need to pay attention to security and privacy: the central data warehouse or data lake might have robust security controls, but does the application connecting via an API? Client software embedding the data should have equal security levels.

Cleaner data is critical

And, although cleaning data is always important for effective analytics and business intelligence, it becomes all the more critical when the users are not data scientists. They need to know that they can trust the data, and if the data is imperfect or incomplete, this needs to be flagged.

A data scientist working on an analytics team will have an instinctive feel for data quality and reliability, and will understand that data need not be 100% complete to Improve decision-making. But a user in the field, or a senior manager, might not.

“Embedded analytics continues the democratisation of data, bringing data and insight directly to the business user within their natural workflow,” says Greg Hanson, VP for EMEA at Informatica.

“This fosters a culture of data-driven decision-making and can speed time to value. However, for CDOs [chief data officers] and CIOs, the crucial question must be: ‘is it accurate, is it trustworthy and can I rely on it?’ For embedded analytics programmes to be a success, organisations need confidence that the data fuelling them is from the right sources, is high quality and the lineage is understood.”

CDOs should also consider starting small and scaling up. The usefulness of real-time data will vary from workflow to workflow. Some suppliers’ APIs will integrate better with the host application than others. And users will need time to become comfortable making decisions based on the data they see, but also to develop a feel for when questions are better passed on to the analytics or data science team.

“Organisations, as part of their next step forward, have come to us with their cloud infrastructure or data lakes already in place, and they started to transform their data engineering into something that can be used,” says PA’s Howell. “Sometimes they put several small use cases in place as proof of concept and the proof of value. Some data isn’t as well used as it could be. I think that’s going to be a continually evolving capability.”

Mon, 11 Jul 2022 09:43:00 -0500 en text/html https://www.computerweekly.com/feature/Embedded-analytics-emerges-to-offer-new-level-of-business-intelligence
Killexams : The Future of the Data Lakehouse – Open

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake. In a rush to own this term, many vendors have lost sight of the fact that the openness of a data architecture is what guarantees its durability and longevity.

On data warehouses and data lakes

Data lakes and data warehouses unify large volumes and varieties of data into a central location.  But with vastly different architectural worldviews.  Warehouses are vertically integrated for SQL Analytics, whereas Lakes prioritize flexibility of analytic methods beyond SQL.

Quest

In order to realize the benefits of both worlds—flexibility of analytics in data lakes, and simple and fast SQL in data warehouses—companies often deployed data lakes to complement their data warehouses, with the data lake feeding a data warehouse system as the last step of an extract, transform, load (ETL) or ELT pipeline. In doing so, they’ve accepted the resulting lock-in of their data in warehouses. 

But there was a better way: enter the Hive Metastore, one of the sleeper hits of the data platform of the last decade. As use cases matured, we saw the need for both efficient, interactive BI analytics and transactional semantics to modify data.

Iterations of the lakehouse

The first generation of the Hive Metastore attempted to address the performance considerations to run SQL efficiently on a data lake. It provided the concept of a database, schemas, and tables for describing the structure of a data lake in a way that let BI tools traverse the data efficiently. It added metadata that described the logical and physical layout of the data, enabling cost-based optimizers, dynamic partition pruning, and a number of key performance improvements targeted at SQL analytics.

The second generation of the Hive Metastore added support for transactional updates with Hive ACID. The lakehouse, while not yet named, was very much thriving. Transactions enabled the use cases of continuous ingest and inserts/updates/deletes (or MERGE), which opened up data warehouse style querying, capabilities, and migrations from other warehousing systems to data lakes. This was enormously valuable for many of our customers.

Projects like Delta Lake took a different approach at solving this problem. Delta Lake added transaction support to the data in a lake. This allowed data curation and brought the possibility to run data warehouse-style analytics to the data lake.

Somewhere along this timeline, the name “data lakehouse” was coined for this architecture pattern. We believe lakehouses are a great way to succinctly define this pattern and have gained mindshare very quickly among customers and the industry. 

What have customers been telling us?

In the last few years, as new data types are born and newer data processing engines have emerged to simplify analytics, companies have come to expect that the best of both worlds truly does require analytic engine flexibility. If large and valuable data for the enterprise is managed, then there has to be openness for the business to choose different analytic engines, or even vendors.

The lakehouse pattern, as implemented, had a critical contradiction at heart: while lakes were open, lakehouses were not.

The Hive metastore followed a Hive-first evolution, before adding engines like Impala, Spark, among others. Delta lake had a Spark-heavy evolution; customer options dwindle rapidly if they need freedom to choose a different engine than what is primary to the table format. 

Customers demanded more from the start. More formats, more engines, more interoperability. Today, the Hive metastore is used from multiple engines and with multiple storage options. Hive and Spark of course, but also Presto, Impala, and many more. The Hive metastore evolved organically to support these use cases, so integration was often complex and error prone.  

An open data lakehouse designed with this need for interoperability addresses this architectural problem at its core. It will make those who are “all in” on one platform uncomfortable, but community-driven innovation is about solving real-world problems in pragmatic ways with best-of-breed tools, and overcoming vendor lock-in whether they approve or not.

An open lakehouse, and the birth of Apache Iceberg

Apache Iceberg was built from inception with the goal to be easily interoperable across multiple analytic engines and at a cloud-native scale. Netflix, where this innovation was born, is perhaps the best example of a 100 PB scale S3 data lake that needed to be built into a data warehouse. The cloud native table format was open sourced into Apache Iceberg by its creators.

Apache Iceberg’s real superpower is its community. Organically, over the last three years, Apache Iceberg has added an impressive roster of first-class integrations with a thriving community:

  • Data processing and SQL engines Hive, Impala, Spark, PrestoDB, Trino, Flink
  • Multiple file formats: Parquet, AVRO, ORC
  • Large adopters in the community: Apple, LinkedIn, Adobe, Netflix, Expedia and others
  • Managed services with AWS Athena, Cloudera, EMR, Snowflake, Tencent, Alibaba, Dremio, Starburst

What makes this varied community thrive is the collective need of thousands of companies to ensure that data lakes can evolve to subsume data warehouses, while preserving analytic flexibility and openness across engines. This enables an open lakehouse: one that offers unlimited analytic flexibility for the future.

Quest

How are we embracing Iceberg?

At Cloudera, we are proud of our open-source roots and committed to enriching the community.  Since 2021, we have contributed to the growing Iceberg community with hundreds of contributions across Impala, Hive, Spark, and Iceberg. We extended the Hive Metastore and added integrations to our many open-source engines to leverage Iceberg tables. In early 2022, we enabled a Technical Preview of Apache Iceberg in Cloudera Data Platform allowing Cloudera customers to realize the value of Iceberg’s schema evolution and time travel capabilities in our Data Warehousing, Data Engineering and Machine Learning services.

Our customers have consistently told us that analytic needs evolve rapidly, whether it is modern BI, AI/ML, data science, or more.  Choosing an open data lakehouse powered by Apache Iceberg gives companies the freedom of choice for analytics.

If you want to learn more, join us on June 21 on our webinar with Ryan Blue, co-creator of Apache Iceberg and Anjali Norwood, Big Data Compute Lead at Netflix.

Thu, 23 Jun 2022 13:06:00 -0500 en-US text/html https://www.cio.com/article/350348/managing-cost-and-risks-with-cloud-data-management.html
Killexams : Best Ecommerce Platform : Start your online ecommerce platform today 2022

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Killexams : Adobe releases new analytics services for metaverse

Adobe has released new services in Adobe Analytics delivering a single workspace for brands to unify data and insights across all media types, now including the metaverse.

Adobe is previewing support for metaverse analytics, as more brands begin to embrace immersive and 3D experiences.

With a rearchitected platform, Adobe said it can now seamlessly extend the reach of Adobe Analytics to new and emerging channels. In the metaverse, brands will be able to measure and analyse specific events, such as the volume of engagement with 3D objects and immersive experiences, as well as collecting interactions across multiple metaverses.

This data can then be combined with insights across other channels like the website or mobile app, to understand changing consumer preferences. With Adobe Creative Cloud and immersive design tools like Adobe Substance 3D, Adobe claims it is uniquely positioned to help brands design, deliver, measure and monetise experiences in the metaverse.

Amit Ahuja, senior vice president, Adobe Experience Cloud platform and products at Adobe said, “Delivering personalised customer experiences is a top priority for every business in every industry, and the key to making it happen is connecting real-time insights across all aspects of the customer journey.

“With support for metaverse and streaming media channels, Adobe Analytics continues to lead the industry as the only true omnichannel analytics solution for customer engagement.”

With global brands adopting Adobe Analytics, Adobe also introduced a new service to seamlessly transition data from other analytics products while preserving historical compliance with regulations such as Global Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). Brands can drive true omnichannel analysis through Customer Journey Analytics, for deeper insights on new consumer behaviours.

Other new innovations include streaming media and the partner ecosystem.

Through Customer Journey Analytics (CJA), teams can tie digital media consumption to engagement on other channels like social media, websites and offline channels. A retailer, for instance, can see the types of content that drive social engagement and/or in-store activity to deliver personalisation and drive retention efforts.

Tue, 05 Jul 2022 17:30:00 -0500 text/html https://www.itnews.com.au/digitalnation/news/adobe-releases-new-analytics-services-for-metaverse-582221
Killexams : Telecom Analytics Industry | Up-To-Date Market Insights And Trends, Forecast From 2026

(MENAFN- EIN Presswire)

Telecom analytics Market Trends

The need to streamline business operations impacting the revenue is primarily driving the growth of the telecom analytics market.

PORTLAND , PORTLAND, OR, UNITED STATE, July 14, 2022 /EINPresswire.com / -- Surge in need for streamlining revenue management, rise in demand for fraud detection due to network attacks, and the need for churn reduction drive the growth of the global telecom analytics industry .

However, lack of awareness of telecom analytics among telecom operators hampers the market growth. On the contrary, integration of new technologies such as machine learning and AI in telecom analytics is expected to create lucrative opportunities in the near future.

According to the report published by Allied Market Research, the global telecom analytics market was pegged at $3.52 billion in 2018 and is anticipated to hit $9.89 billion by 2026, registering a CAGR of 13.9% from 2019 to 2026.

Download trial Report (Get Full Insights in PDF - 270 Pages) at:

The solution segment held the largest share in 2018, contributing to more than two-thirds of the global telecom analytics market. The adoption of this solution and multiple benefits offered by telecom analytics such as developing customized customer care strategies, improving customer experience, reducing the rate of customer churn, and analyzing the potential of new offerings drive the growth of the segment.

However, the service segment is projected to register the fastest CAGR of 15.40% during the forecast period. This is due to rise in adoption of software and platforms, and increase in demand for cloud-based telecom analytics services.

The on-premise segment dominated the market in 2018, accounting for nearly three-fifths of the global telecom analytics market. The investments by large enterprises for in house software deployment, and the need to secure the critical data and usage of legacy system are driving the growth of this segment. However, the cloud segment is expected to manifest the fastest CAGR of 15.50% during the forecast period.

The low investment cost agility, and scalability offered by telecom analytics as a service are driving the growth of the segment. Furthermore, the rise in need for mobility, and increase in adoption of cloud technology by small scale enterprises are expected to provide lucrative growth in the near future.

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The global telecom analytics market across North America held the largest share in 2018, accounting for nearly two-fifths of the market. This is due to the presence of a large number of market vendors in this region. Moreover, the proliferation of smart phones, IoT, and great presence of internet connectivity are anticipated to boost the growth of the market.

On the other hand, the Asia Pacific region is expected to register the fastest CAGR of 16.0% during the forecast period. This is attributed to the growing tele communication industry in this region. Furthermore, rise in use of smartphones, and increase in adoption of fast internet is expected to increase the growth in this region.

The key market players analyzed in the global telecom analytics market report include, Tableau, Sisense, Oracle, Cisco, SAS Institute, Teradata, SAP SE, Tibco, Adobe, and IBM. These market players have incorporated several strategies including partnership, expansion, collaboration, joint ventures, and others to corroborate their stand in the industry.

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Key Benefits for Stakeholders:

•This study includes the analytical depiction of the global telecom analytics market forecast and trends to determine the imminent investment pockets.

•The report presents information related to key drivers, restraints, and opportunities.

•The current market size is quantitatively analyzed from 2018 to 2026 to highlight the financial competency of the industry.

•Porter's five forces analysis illustrates the potency of buyers & suppliers in the telecom analytics industry.

Thanks for practicing this article; you can also get an individual chapter-wise section or region-wise report versions like North America, Europe, or Asia.

If you have any special requirements, please let us know and we will offer you the report as per your requirements.

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Lastly, this report provides market intelligence most comprehensively. The report structure has been kept such that it offers maximum business value. It provides critical insights into the market dynamics and will enable strategic decision-making for the existing market players as well as those willing to enter the market.

Similar Reports:

1. Telecom Application Program Interface(APIs) Market

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Killexams : Global Cloud Computing Market Report to 2028 - Featuring Accenture, Adobe, IBM and Intel Among Others - ResearchAndMarkets.com

Press release content from Business Wire. The AP news staff was not involved in its creation.

DUBLIN--(BUSINESS WIRE)--Jul 14, 2022--

The “Global Cloud Computing Market, By Deployment Type, By Service Model, Platform as a Service, Software as a Service, By Industry Vertical & By Region - Forecast and Analysis 2022 - 2028” report has been added to ResearchAndMarkets.com’s offering.

The Global Cloud Computing Market was valued at USD 442.89 Billion in 2021, and it is expected to reach a value of USD 1369.50 Billion by 2028, at a CAGR of more than 17.50% over the forecast period (2022 - 2028).

Cloud computing is the delivery of hosted services over the internet, including software, servers, storage, analytics, intelligence, and networking. Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS), and Platform-as-a-Service (PaaS) are three types of cloud computing services (PaaS).

The expanding usage of cloud-based services and the growing number of small and medium businesses around the world are the important drivers driving the market growth. Enterprises all over the world are embracing cloud-based platforms as a cost-effective way to store and manage data. Commercial data demands a lot of storage space. With the growing volume of data generated, many businesses have moved their data to cloud storage, using services like Amazon Web Services, Microsoft Azure, and Google Cloud Platform.

The growing need to regulate and reduce Capital Expenditure (CAPEX) and Operational Expenditure (OPEX), as well as the increasing volume of data generated in websites and mobile apps, are a few drivers driving the growth of emerging technologies. Emerging technologies like big data, artificial intelligence (AI), and machine learning (ML) are gaining traction, resulting in the global cloud computing industry growth. The cloud computing market is also driven by major factors such as data security, Faster Disaster Recovery (DR), and meeting compliance standards.

Aspects covered in this report

The global cloud computing market is segmented on the basis of deployment type, service model, and industry vertical. Based on the deployment type, the market is segmented as: private cloud, public cloud, and hybrid cloud. Based on the service model, the market is segmented as: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Based on industry vertical, the market is segmented as: Government, Military & Defense, Telecom & IT, Healthcare, Retail, and Others. Based on region it is categorized into: North America, Europe, Asia-Pacific, Latin America, and MEA.

Driver

  • The key aspect driving the market growth includes the increased RoI with lower storage and infrastructure costs. Enterprises are concerned about the expenses of data hosting on-premises, both in terms of implementation and maintenance. Electricity prices, employee expenses, and downtime issues are a few other concerns for companies. The current competitive environment and global economic conditions have hastened the use of cost-effective business model restructuring measures. This has significantly encouraged the use of cloud-based services, in turn, contributing to the growth of the market.

Restraint

  • The major factor that negatively impacts the growth of the market includes the need to manage regulatory and compliance policy needs. Due to changing business needs, increasing data breaches and cyber-attacks, and expanding data security concerns, the need to meet regulatory and compliance requirements is continually increasing; which is challenging the market growth.

Key Market Trends

  • The key trend driving cloud computing services is the increasing shift of businesses toward digital transformation and the enhancement of customer experience. These are just a few more factors that are driving the adoption of cloud computing services and, as a result, lowering enterprise costs.
  • The cloud offers the pay-as-you-go approach, which allows businesses to pay for cloud services based on how often they use them, resulting in lower prices. Due to the sheer benefits of cloud services, such as on-demand availability and low cost, companies are increasingly adopting them. This thus contributes to the growth of cloud computing market growth.
  • The outbreak of COVID-19 has also been a growth driver for the market. In May 2020, to address COVID-19 problems, Microsoft announced Microsoft Cloud for Healthcare. For healthcare enterprises, Microsoft developed industry-specific cloud products, enhanced workflow efficiency, and streamlined interactions. Data analytics for both structured and unstructured data are included in Microsoft Cloud for Healthcare. Hence, such growth strategies adopted by the key player in the market are likely to aid the industry growth during the forecast period.

Companies Mentioned

  • Accenture plc
  • Adobe, Inc.
  • Alibaba Cloud International
  • Amazon Web Services, Inc.
  • Apple, Inc.
  • At&T, Inc.
  • Cisco Systems, Inc.
  • Citrix Systems, Inc.
  • Dell, Inc.
  • Google LLC
  • Hewlett Packard Enterprise Development Lp
  • IBM Corp.
  • Intel Corp.
  • Microsoft Corp.
  • Netsuite, Inc.
  • Oracle Corp.
  • Salesforce.Com, Inc.
  • Sap Se
  • Verizon Enterprise Solutions
  • Yahoo, Inc.

For more information about this report visit https://www.researchandmarkets.com/r/m9wewu

View source version on businesswire.com:https://www.businesswire.com/news/home/20220714005444/en/

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