首页 > 市场调查报告书 > 通讯

巨量资料人工智能

市场调查报告书

商品编码

1352146

全球增强分析市场 - 2023-2030

Global Augmented Analytics Market - 2023-2030

出版日期: 2023年09月27日 | 出版商:

DataM Intelligence | 英文 186 Pages | 商品交期: 最快1-2个工作天内

价格

※ 本网页内容可能与最新版本有所差异。详细情况请与我们联繫。

简介目录

概述

全球增强分析市场在 2022 年达到 85 亿美元，预计到 2030 年将达到 463 亿美元，2023-2030 年预测期间复合年增长率为 23.4%。

人工智慧和机器学习演算法技术的不断增长和发展使得开发分析工具变得更加容易，这些工具可以自动执行许多任务，包括资料准备、分析和视觉化。为了从每天产生的来自物联网设备、社交媒体和线上交易等各种来源的大量资料中获得意义，目前需要更复杂的分析工具。

透过使资料民主化，增强分析使非技术使用者更容易进行资料分析，从而使业务使用者无需资料科学家或 IT 专家的帮助即可执行具有挑战性的分析任务。由于增强的分析解决方案直观的使用者介面、对自然语言处理和互动式仪表板的支持，数据分析变得更加容易理解。

到 2022 年，亚太地区预计将成为成长最快的地区，占全球增强分析市场的份额不到 1/4。该地区拥有世界上人口最多的国家，并从电子商务、行动应用、物联网设备和社交媒体等多种来源产生大量资料。增强分析可协助组织利用这些资料进行洞察和决策。

动力学

工业 5.0 的采用率不断提高

一些成长因素将继续促进工业 5.0 的采用，其中涉及人类智慧与扩增实境 (AR) 等尖端技术的结合。由于扩增实境（AR）技术，工业工人现在可以即时感知复杂的资料、设备和流程，它可以提供更身临其境和互动的用户体验，这种改进的用户体验可能会鼓励更多的接受和采用。

根据西门子2023年的文章，从工业4.0到工业5.0的转变，非常强调人类智慧和人工智慧（AI）的结合，以实现卓越运作。透过向营运专业人员提供数据驱动决策的创新工具和知识，增强分析在这一转变中发挥着至关重要的作用。

让公民资料科学家和企业更轻鬆工作的需求不断增加

为了实现资料和分析工具的民主化，正在利用扩增实境。它使业务用户和公民资料科学家能够以可视化且可访问的方式与复杂的资料集和分析进行交互，从而最大限度地减少对深入技术技能的需求。基于扩增实境的资料视觉化和分析的开发和互动变得更加容易，因为扩增实境应用程式是透过使用者友好的介面开发的，不需要编码或技术能力。

事实上，资料科学家 80% 以上的时间都花在执行常规、简单的任务上，例如对资料进行分类和清理。增强分析可用于缩短这段时间。业务用户可以直接使用它，而无需业务分析师或资料科学家的帮助，因为它旨在自动进行分析并创建业务洞察，而几乎不需要监督。透过自动化，它减少了公司对资料科学家的依赖。

技术进步

人工智慧和机器学习在资料分析、模式识别和预测建模的自动化方面发挥着重要作用。增强分析利用这些技术来帮助使用者准备资料、产生见解和异常检测。 NLP 使用户能够使用自然语言查询和命令与资料和分析平台进行互动。它简化了提出问题和接收见解的过程，使非技术用户更容易进行分析。

例如，2023 年9 月5 日，领先的人工智慧和扩增实境美容和时尚技术解决方案提供商Perfect Corp. 宣布更新其人工智慧驱动的即时皮肤分析解决方案，这项符合HIPAA 要求且经过皮肤科医生验证的技术为使用者提供了深入的了解了解他们的皮肤状况和个人化的护肤建议。 AI 皮肤分析创新现在可以透过即时摄影机模式分析多达 14 个皮肤问题，并包括扩增实境迭加效果，以即时突出显示特定的皮肤问题。

隐私风险和解释困难

增强分析的应用很大程度上取决于可靠、准确和整合的资料。资料品质差可能最终导致错误的发现和行动。整合来自多个来源的资料可能很困难且耗时，特别是在使用过时的技术和各种资料格式时。分析敏感资讯或个人识别资讯 (PII) 时会出现隐私风险，因此遵守 GDPR 等资料保护法至关重要。

增强分析中使用的人工智慧演算法可以继承训练资料的偏差，可能导致有偏见的见解和建议。确保人工智慧驱动分析的公平性和公平性是一项挑战，因为它需要仔细考虑人口偏见等因素。增强分析中使用的一些人工智慧模型（例如深度学习模型）可能难以解释，因此很难理解洞察是如何产生的。

Overview

Global Augmented Analytics Market reached US$ 8.5 billion in 2022 and is expected to reach US$ 46.3 billion by 2030, growing with a CAGR of 23.4% during the forecast period 2023-2030.

Continuous growth and development in technologies in AI and ML algorithms have made it easier for developing analytics tools that automate many tasks including data preparation, analysis and visualization. In order to make meaning from the massive volumes of data generated each day, which come from a variety of sources such as IoT devices, social media and online transactions, more sophisticated analytics tools are currently being expected.

By democratizing data, augmented analytics renders data analysis more approachable for non-technical users, allowing business users to carry out challenging analytics tasks without the assistance of data scientists or IT specialists. Data analysis becomes more understandable because of augmented analytics solutions intuitive user interfaces, support for natural language processing and interactive dashboards.

In 2022, Asia-Pacific is expected to be the fastest growing region having less than 1/4th of the global augmented analytics market. The region has world's most populous countries and generates vast data from many sources, such as e-commerce, mobile apps, IoT devices and social media. Augmented analytics assists organizations harness this data for insights and decision-making.

Dynamics

Growing Adoption of Industry 5.0

Several growth factors will continue to contribute to the adoption of Industry 5.0, which involves the combination of human intelligence with cutting-edge technology like augmented reality (AR). Industrial workers may now perceive complex data, equipment and processes in real-time because of augmented reality (AR) technology, which can offer a more immersive and interactive user experience and this improved user experience may encourage greater acceptance and adoption.

According to the article by Siemens in 2023, the shift from Industry 4.0 to Industry 5.0, puts a strong emphasis on the combination of human intelligence and artificial intelligence (AI) to achieve operational excellence. Through the supply of innovative tools and knowledge for data-driven decision-making to operational professionals, augmented analytics plays a crucial role in this shift.

Increase in Need to Make the Work Easier for Citizen Data Scientists and Business

Towards democratizing access to data and analytical tools, augmented reality is being utilized. It minimizes the need for in-depth technical skills by enabling business users and citizen data scientists to interact with complex data sets and analytics in a visual and accessible way. It has become easier to develop and interact with augmented reality-based data visualizations and analytics because augmented reality applications are being developed with user-friendly interfaces that require no coding or technical abilities.

In reality, data scientists spend more than 80% of their time performing routine, straightforward tasks like categorizing and cleaning the data. Augmented analytics can be used to shorten this period of time. It can be utilized directly by business users without the help of a business analyst or data scientist because it is meant to conduct analysis and create business insights automatically with little to no oversight. Through automation, it reduces the company's reliance on data scientists.

Technology Advancement

AI and ML are instrumental in the automation of data analysis, pattern recognition and predictive modeling. Augmented analytics leverages these technologies to assist users in data preparation, insights generation and anomaly detection. NLP enables users to interact with data and analytics platforms using natural language queries and commands. It leads to simplifies the process of asking questions and receiving insights, making analytics more accessible to non-technical users.

For instance, on 5 September 2023, Perfect Corp., a leading artificial intelligence and augmented reality beauty and fashion tech solutions provider, announced updates to its AI-powered Live Skin Analysis Solution and this HIPAA-compliant and dermatologist-verified technology offers users deep insights into their skin condition and personalized skincare recommendations. The AI Skin Analysis innovation can now analyze up to 14 skin concerns through live camera mode and it includes augmented reality overlay effects to highlight specific skin concerns in real-time.

Privacy Risk and Difficult in Interpretation

The application of augmented analytics largely depends on reliable, accurate and integrated data. Poor data quality could end up in incorrect findings and actions. It can be difficult and time-consuming to integrate data from numerous sources, especially when navigating outdated technologies and various data formats. Privacy risks arise when sensitive or personally identifiable information (PII) is analyzed, making compliance to data protection laws like the GDPR essential.

AI algorithms used in augmented analytics can inherit biases from training data, potentially leading to biased insights and recommendations. Ensuring fairness and equity in AI-driven analytics is a challenge, as it requires careful consideration of factors like demographic bias. Some AI models used in augmented analytics, such as deep learning models, can be difficult to interpret, making it challenging to understand how insights are generated.

Segment Analysis

The global augmented analytics market is segmented based on component, deployment, organization size, business function, end-user and region.

Rising Adoption of Cloud Platform

Cloud deployment is expected to be the dominant segment with about 1/3rd of the market during the forecast period 2023-2030. Cloud systems have virtually infinite scalability, allowing businesses to handle massive data volumes and conduct sophisticated analytical activities without requiring to invest in significant upfront equipment investments.

As it is more cost-effective than on-premises infrastructure, cloud-based augmented analytics solutions frequently employ a pay-as-you-go business model and this enables enterprises to avoid the high capital costs of on-premises infrastructure and makes augmented analytics available to a wider spectrum of companies.

For instance, on 6 September 2023, ZINFI Technologies, Inc., a leader in partner relationship management and through-channel marketing automation introduced advanced generative artificial intelligence capabilities into its SaaS platform for unified partner management. ZINFI's analytics capabilities, powered by Microsoft's Power BI, are further strengthened with the integration of Microsoft's Copilot technology and this enables the generation of insights based on partner performance analytics across various activities to improve return on investment.

Geographical Penetration

Technology Innovation in North America

North America is among the growing regions in the global augmented analytics market covering more than 1/3rd of the market. The region is a hub for technological innovation, with many AI and machine learning research centers and startups and this has led to the development of advanced analytics tools and algorithms that power augmented analytics solutions. According to a report by BCG, Australian Airlines saves US$ 40 million in annual costs by using cloud analytics.

In May 2022, Pyramid Analytics, a decision intelligence platform provider, achieved significant recognition in Gartner's Critical Capabilities for Analytics and Business Intelligence Platforms report. Pyramid Analytics secured the top ranking in the augmented analytics Use Case among 20 companies evaluated by Gartner. Augmented analytics involves using technologies like machine learning and AI to aid in data preparation, insight generation and explanation, enhancing data exploration and analysis in analytics and business intelligence platforms.

Competitive Landscape

The major global players in the market include: SAP SE, International Business Machines Corporation (IBM), Salesforce.com, Inc., Sisense Inc., Tableau Software, THOUGHTSPOT, Tibco Software Inc., QLIK, Microsoft and SAS Institute Inc.

COVID-19 Impact Analysis

The pandemic generated an unprecedented amount of data related to infection rates, healthcare resources, economic changes and remote work patterns. Analyzing this complex data presented challenges. Augmented analytics helped organizations make sense of this vast data by automating data preparation, pattern recognition and insights generation. Many businesses faced disruptions, changes in customer behavior and shifts in demand due to lockdowns and restrictions. Traditional data analytics models needed adaptation.

Augmented analytics allowed businesses to quickly adapt by automating the analysis of changing market conditions and customer preferences, helping them make data-driven decisions. With remote work becoming widespread, businesses needed to monitor and support employees' productivity and well-being. Augmented analytics tools provided insights into employee engagement, productivity and remote work challenges, helping organizations make data-driven adjustments to their policies and practices.

Augmented analytics played a crucial role in tracking and analyzing COVID-19 data, including infection rates, vaccination progress and healthcare resource allocation and the pandemic disrupted global supply chains, leading to challenges in logistics and inventory management and these analytics tools helped public health authorities and healthcare organizations make informed decisions about resource allocation and public health interventions.

AI Impact

AI streamlines data preparation tasks by automatically cleaning, transforming and integrating data from various sources and this reduces the time and effort required for data preparation. NLP capabilities in AI enable users to interact with data and analytics platforms using natural language queries and commands, this makes it easier for non-technical users to explore data and receive insights.

AI-powered data visualization tools automatically generate meaningful charts, graphs and dashboards based on the data, making it easier for users to visualize trends and patterns. AI algorithms can analyze data and automatically generate insights and recommendations and this helps users uncover hidden patterns and make data-driven decisions more quickly. The model can predict future trends and outcomes based on historical data.

For instance, on 29 August 2023, Wizeline, an AI-focused digital services provider, introduced its "AI-Native Offerings" at Disney's Data & Analytics Conference and these offerings emphasize the fusion of AI technology with a human-centric approach, highlighting Wizeline's belief in enhancing human capabilities with AI rather than replacing them.

The company showcased its capabilities through demonstrations centered on Generative AI and engaged with conference attendees to illustrate the real-world applications of their solutions. Wizeline's commitment to AI innovation is embodied in its AI-Native Framework, which aims to seamlessly integrate AI technologies into corporate infrastructures.

Russia- Ukraine War Impact

The war can disrupt supply chains, leading to fluctuations in the availability and cost of hardware components and data storage and this could affect the implementation and maintenance of augmented analytics solutions. In regions directly affected by the conflict, data collection and reporting may be disrupted. Augmented analytics relies on high-quality data, so any disruptions can hinder insights generation.

During times of geopolitical instability, there is often an uptick in cyberattacks and espionage. Augmented analytics platforms may need to strengthen their security measures to protect sensitive data. Organizations and governments may prioritize resources for immediate humanitarian and security needs, potentially diverting investments away from AI and analytics initiatives, including augmented analytics.

By Component

Software
Services

By Deployment

Cloud
On-Premise

By Organization Size

Small & Medium Sized Enterprises
Large Enterprises

By Business Function

Sales & Marketing
Finance
IT
Operations
Others

By End-User

Retail
Healthcare and Life Sciences
BFSI
Telecom and IT
Manufacturing
Government
Others

By Region

North America
- U.S.
- Canada
- Mexico

Europe
- Germany
- UK
- France
- Italy
- Russia
- Rest of Europe

South America
- Brazil
- Argentina
- Rest of South America

Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific

Middle East and Africa

Key Developments

On 9 November 2021, Narrative BI launched the Public Beta version of its platform, featuring a set of powerful features designed to provide valuable insights from Google Analytics. The platform includes an Insight Generation Engine that aims to simplify trend identification and anomaly detection in Google Analytics, making it easier for growth teams to stay ahead of emerging trends and identify blind spots and this launch offers growth teams a lightweight yet powerful marketing analytics solution.
On 23 April 2021, Subex launched HyperSense, an end-to-end augmented analytics platform designed to leverage artificial intelligence (AI) across the data value chain. HyperSense offers a range of augmented analytics capabilities in a flexible and modular platform, with no-code features that allow users without coding knowledge to aggregate data from various sources, create, interpret and fine-tune AI models and share their findings within the organization.
On 20 May 2022, Alteryx, a data and analytics vendor, introduced new integrations with cloud data platforms such as Databricks, Snowflake and Google BigQuery to allow users to work with data directly in their storage platform of choice and these integrations aim to enhance connectivity and streamline data preparation for analytics, reducing the time to gain insights.

Why Purchase the Report?

To visualize the global augmented analytics market segmentation based on component, deployment, organization size, business function, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of augmented analytics market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global augmented analytics market report would provide approximately 61 tables, 58 figures and 186 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Co.mpanies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Component
3.2. Snippet by Deployment
3.3. Snippet by Organization Size
3.4. Snippet by Business Function
3.5. Snippet by End-User
3.6. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Growing Adoption of Industry 5.0
  - 4.1.1.2. Increase in Need to Make the Work Easier for Citizen Data Scientists and Business
  - 4.1.1.3. Technology Advancement
- 4.1.2. Restraints
  - 4.1.2.1. Privacy Risk and Difficult in Interpretation
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Component

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 7.1.2. Market Attractiveness Index, By Component
7.2. Software*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Services

8. By Deployment

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 8.1.2. Market Attractiveness Index, By Deployment
8.2. Cloud *
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. On-Premise

9. By Organization Size

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 9.1.2. Market Attractiveness Index, By Organization Size
9.2. Small & Medium Sized Enterprises*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Large Enterprises

10. By Business Function

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Business Function
- 10.1.2. Market Attractiveness Index, By Business Function
10.2. Sales & Marketing*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Finance
10.4. IT
10.5. Operations
10.6. Others

11. By End-User

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.1.2. Market Attractiveness Index, By End-User
11.2. Retail*
- 11.2.1. Introduction
- 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
11.3. Healthcare and Life Sciences
11.4. BFSI
11.5. Telecom and IT
11.6. Manufacturing
11.7. Government
11.8. Others

12. By Region

12.1. Introduction
- 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 12.1.2. Market Attractiveness Index, By Region
12.2. North America
- 12.2.1. Introduction
- 12.2.2. Key Region-Specific Dynamics
- 12.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 12.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 12.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Business Function
- 12.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.2.8.1. U.S.
  - 12.2.8.2. Canada
  - 12.2.8.3. Mexico
12.3. Europe
- 12.3.1. Introduction
- 12.3.2. Key Region-Specific Dynamics
- 12.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 12.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 12.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Business Function
- 12.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.3.8.1. Germany
  - 12.3.8.2. UK
  - 12.3.8.3. France
  - 12.3.8.4. Italy
  - 12.3.8.5. Russia
  - 12.3.8.6. Rest of Europe
12.4. South America
- 12.4.1. Introduction
- 12.4.2. Key Region-Specific Dynamics
- 12.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 12.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 12.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Business Function
- 12.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.4.8.1. Brazil
  - 12.4.8.2. Argentina
  - 12.4.8.3. Rest of South America
12.5. Asia-Pacific
- 12.5.1. Introduction
- 12.5.2. Key Region-Specific Dynamics
- 12.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 12.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 12.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Business Function
- 12.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.5.8.1. China
  - 12.5.8.2. India
  - 12.5.8.3. Japan
  - 12.5.8.4. Australia
  - 12.5.8.5. Rest of Asia-Pacific
12.6. Middle East and Africa
- 12.6.1. Introduction
- 12.6.2. Key Region-Specific Dynamics
- 12.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 12.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 12.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 12.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Business Function
- 12.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

13. Competitive Landscape

13.1. Competitive Scenario
13.2. Market Positioning/Share Analysis
13.3. Mergers and Acquisitions Analysis

14. Company Profiles

14.1. SAP SE*
- 14.1.1. Company Overview
- 14.1.2. Product Portfolio and Description
- 14.1.3. Financial Overview
- 14.1.4. Key Developments
14.2. International Business Machines Corporation (IBM)
14.3. Salesforce.com, Inc.
14.4. Sisense Inc.
14.5. Tableau Software
14.6. THOUGHTSPOT
14.7. Tibco Software Inc.
14.8. QLIK
14.9. Microsoft
14.10. SAS Institute Inc.

LIST NOT EXHAUSTIVE