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商品編碼

1352146

全球增強分析市場 - 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: 約2個工作天內

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簡介目錄

概述

全球增強分析市場在 2022 年達到 85 億美元，預計到 2030 年將達到 463 億美元，2023-2030 年預測期間複合年成長率為 23.4%。

人工智慧和機器學習演算法技術的不斷成長和發展使得開發分析工具變得更加容易，這些工具可以自動執行許多任務，包括資料準備、分析和視覺化。為了從每天產生的來自物聯網設備、社交媒體和線上交易等各種來源的大量資料中獲得意義，目前需要更複雜的分析工具。

透過使資料民主化，增強分析使非技術使用者更容易進行資料分析，從而使業務使用者無需資料科學家或 IT 專家的幫助即可執行具有課題性的分析任務。由於增強的分析解決方案直覺的使用者介面、對自然語言處理和互動式儀表板的支持，數據分析變得更加容易理解。

到 2022 年，亞太地區預計將成為成長最快的地區，佔全球增強分析市場的佔有率不到 1/4。該地區擁有世界上人口最多的國家，並從電子商務、行動應用、物聯網設備和社交媒體等多種來源產生大量資料。增強分析可協助組織利用這些資料進行洞察和決策。

動力學

工業 5.0 的採用率不斷提高

一些成長因素將繼續促進工業 5.0 的採用，其中涉及人類智慧與擴增實境 (AR) 等尖端技術的結合。由於擴增實境（AR）技術，工業工人現在可以即時感知複雜的資料、設備和流程，它可以提供更身臨其境和互動的用戶體驗，這種改進的用戶體驗可能會鼓勵更多的接受和採用。

根據西門子2023年的文章，從工業4.0到工業5.0的轉變，非常強調人類智慧和人工智慧（AI）的結合，以實現卓越運作。透過向營運專業人員提供數據驅動決策的創新工具和知識，增強分析在這一轉變中發揮著至關重要的作用。

讓公民資料科學家和企業更輕鬆工作的需求不斷增加

為了實現資料和分析工具的民主化，正在利用擴增實境。它使業務用戶和公民資料科學家能夠以可視化且可訪問的方式與複雜的資料集和分析進行交互，從而最大限度地減少對深入技術技能的需求。基於擴增實境的資料視覺化和分析的開發和互動變得更加容易，因為擴增實境應用程式是透過使用者友好的介面開發的，不需要編碼或技術能力。

事實上，資料科學家 80% 以上的時間都花在執行常規、簡單的任務上，例如對資料進行分類和清理。增強分析可用於縮短這段時間。業務用戶可以直接使用它，而無需業務分析師或資料科學家的幫助，因為它旨在自動進行分析並創建業務洞察，而幾乎不需要監督。透過自動化，它減少了公司對資料科學家的依賴。

技術進步

人工智慧和機器學習在資料分析、模式識別和預測建模的自動化方面發揮著重要作用。增強分析利用這些技術來幫助使用者準備資料、產生見解和異常檢測。 NLP 使用戶能夠使用自然語言查詢和命令與資料和分析平台進行互動。它簡化了提出問題和接收見解的過程，使非技術用戶更容易進行分析。

例如，2023 年9 月5 日，領先的人工智慧和擴增實境美容和時尚技術解決方案提供商Perfect Corp. 宣布更新其人工智慧驅動的即時皮膚分析解決方案，這項符合HIPAA 要求且經過皮膚科醫生驗證的技術為使用者提供了深入的了解了解他們的皮膚狀況和個人化的護膚建議。 AI 皮膚分析創新現在可以透過即時攝影機模式分析多達 14 個皮膚問題，並包括擴增實境疊加效果，以即時突出顯示特定的皮膚問題。

隱私風險和解釋困難

增強分析的應用很大程度上取決於可靠、準確和整合的資料。資料品質差可能最終促使錯誤的發現和行動。整合來自多個來源的資料可能很困難且耗時，特別是在使用過時的技術和各種資料格式時。分析敏感資訊或個人識別資訊 (PII) 時會出現隱私風險，因此遵守 GDPR 等資料保護法至關重要。

增強分析中使用的人工智慧演算法可以繼承訓練資料的偏差，可能促使有偏見的見解和建議。確保人工智慧驅動分析的公平性和公平性是一項課題，因為它需要仔細考慮人口偏見等因素。增強分析中使用的一些人工智慧模型（例如深度學習模型）可能難以解釋，因此很難理解洞察是如何產生的。

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