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表紙：拡張アナリティクスの世界市場-2023年～2030年
市場調査レポート
商品コード
1352146

拡張アナリティクスの世界市場-2023年～2030年

Global Augmented Analytics Market - 2023-2030
出版日: | 発行: DataM Intelligence | ページ情報: 英文 186 Pages | 納期: 約2営業日

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拡張アナリティクスの世界市場-2023年～2030年
出版日: 2023年09月27日
発行: DataM Intelligence
ページ情報: 英文 186 Pages
納期: 約2営業日
ご注意事項 :
本レポートは最新情報反映のため適宜更新し、内容構成変更を行う場合があります。ご検討の際はお問い合わせください。
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  • 目次
概要

概要

世界の拡張アナリティクス市場は、2022年に85億米ドルに達し、2023年から2030年の予測期間中にCAGR 23.4％で成長し、2030年には463億米ドルに達すると予測されています。

AIとMLアルゴリズムにおける技術の継続的な成長と発展により、データの準備、分析、視覚化を含む多くのタスクを自動化する分析ツールの開発が容易になっています。IoTデバイス、ソーシャルメディア、オンライン取引など、さまざまなソースから日々生成される大量のデータから意味を見出すために、現在、より高度な分析ツールが期待されています。

拡張アナリティクスはデータを民主化することで、非技術系ユーザーにとってデータ分析がより身近なものになり、ビジネスユーザーはデータサイエンティストやITスペシャリストの支援を受けずに、困難な分析課題を遂行できるようになります。拡張アナリティクス・ソリューションの直感的なユーザーインターフェース、自然言語処理のサポート、インタラクティブなダッシュボードにより、データ分析はより理解しやすくなります。

2022年には、アジア太平洋地域が世界の拡張アナリティクス市場の1/4弱を占める急成長地域になると予想されています。この地域には世界で最も人口の多い国があり、eコマース、モバイルアプリ、IoTデバイス、ソーシャルメディアなど、多くのソースから膨大なデータが生成されます。拡張アナリティクスは、組織が洞察と意思決定のためにこのデータを活用するのを支援します。

ダイナミクス

インダストリー5.0の採用拡大

インダストリー5.0は、拡張現実（AR）のような最先端技術と人間拡張知能の融合を伴います。拡張現実（AR）技術は、より没入的でインタラクティブなユーザー体験を提供することができ、このユーザー体験の向上は、より大きな受け入れと採用を促進する可能性があります。

2023年のシーメンスの記事によると、インダストリー4.0からインダストリー5.0へのシフトは、オペレーショナル・エクセレンスを達成するために、人間の知能と人工知能（AI）の組み合わせに重点を置いています。データ主導の意思決定のための革新的なツールと知識をオペレーションの専門家に提供することで、拡張アナリティクスはこのシフトにおいて重要な役割を果たします。

市民データサイエンティストとビジネスにおける作業の容易化ニーズの増加

データと分析ツールへのアクセスを民主化するために、拡張現実が活用されています。拡張現実は、ビジネスユーザーや市民データサイエンティストが、複雑なデータセットや分析を視覚的かつアクセスしやすい方法で操作できるようにすることで、深い技術的スキルの必要性を最小限に抑えます。拡張現実アプリケーションは、コーディングや技術的能力を必要としないユーザーフレンドリーなインターフェースで開発されているため、拡張現実ベースのデータ可視化や分析の開発や対話が容易になっています。

現実には、データサイエンティストは、データの分類やクリーニングのような日常的で単純な作業に80%以上の時間を費やしています。拡張アナリティクスを使えば、この時間を短縮することができます。これは、ビジネスアナリストやデータサイエンティストの助けを借りることなく、ビジネスユーザーが直接活用できるもので、ほとんど監視することなく自動的に分析を行い、ビジネスインサイトを作成することを目的としているからです。自動化により、企業のデータサイエンティストへの依存度を下げることができます。

技術進歩

AIとMLは、データ分析、パターン認識、予測モデリングの自動化に役立っています。拡張アナリティクスはこれらの技術を活用し、データの準備、洞察の生成、異常検知においてユーザーを支援します。NLPは、自然言語のクエリーやコマンドを使用して、ユーザーがデータや分析プラットフォームと対話することを可能にします。これにより、質問して洞察を得るプロセスが簡素化され、非技術系ユーザーにとってアナリティクスがより身近なものになります。

例えば、2023年9月5日、人工知能と拡張知能を駆使した美容とファッションの技術ソリューションのリーディング・プロバイダーであるパーフェクト・コーポレーションは、AIを活用したライブ肌分析ソリューションのアップデートを発表しました。AI肌分析イノベーションは、ライブカメラモードを通じて最大14の肌悩みを分析できるようになり、特定の肌悩みをリアルタイムで強調する拡張現実オーバーレイ効果も含まれています。

プライバシーリスクと解釈の難しさ

拡張アナリティクスの応用は、信頼性が高く、正確で、統合されたデータに大きく依存します。データの品質が低いと、誤った所見や行動につながる可能性があります。特に、時代遅れの技術や様々なデータ形式を使いこなす場合、多数のソースからのデータを統合するのは困難で時間がかかります。機密情報や個人を特定できる情報（PII）を分析する場合にはプライバシーリスクが発生し、GDPRのようなデータ保護法への準拠が不可欠となります。

拡張アナリティクスで使用されるAIアルゴリズムは、学習データからバイアスを受け継ぐ可能性があり、偏った洞察や推奨につながる可能性があります。人口統計学的バイアスなどの要因を慎重に考慮する必要があるため、AI主導のアナリティクスにおける公平性と公正性の確保は課題です。ディープラーニングモデルなど、拡張アナリティクスで使用されるAIモデルの中には解釈が難しいものもあり、インサイトがどのように生成されるかを理解するのは困難です。

目次

第1章 調査手法と調査範囲

第2章 定義と概要

第3章 エグゼクティブサマリー

第4章 市場力学

  • 影響要因
    • 促進要因
      • インダストリー5.0の採用拡大
      • 市民データサイエンティストとビジネスにおける作業の容易化ニーズの増加
      • 技術進歩
    • 抑制要因
      • プライバシーリスクと解釈の難しさ
    • 機会
    • 影響分析

第5章 産業分析

  • ポーターのファイブフォース分析
  • サプライチェーン分析
  • 価格分析
  • 規制分析
  • ロシア・ウクライナ戦争の影響分析
  • DMIの見解

第6章 COVID-19分析

第7章 コンポーネント別

  • ソフトウェア
  • サービス

第8章 デプロイメント別

  • クラウド
  • オンプレミス

第9章 組織規模別

  • 中小企業
  • 大企業

第10章 ビジネス機能別

  • 営業・マーケティング
  • 財務
  • IT
  • 経営
  • その他

第11章 エンドユーザー別

  • 小売業
  • 医療・ライフサイエンス
  • BFSI
  • 通信・IT
  • 製造業
  • 政府機関
  • その他

第12章 地域別

  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • ロシア
    • その他欧州
  • 南米
    • ブラジル
    • アルゼンチン
    • その他南米
  • アジア太平洋
    • 中国
    • インド
    • 日本
    • オーストラリア
    • その他アジア太平洋
  • 中東・アフリカ

第13章 競合情勢

  • 競合シナリオ
  • 市況/シェア分析
  • M&A分析

第14章 企業プロファイル

  • SAP SE
    • 企業概要
    • 製品ポートフォリオと説明
    • 財務概要
    • 主な動向
  • International Business Machines Corporation（IBM）
  • Salesforce.com, Inc.
  • Sisense Inc.
  • Tableau Software
  • THOUGHTSPOT
  • Tibco Software Inc.
  • QLIK
  • Microsoft
  • SAS Institute Inc.

第15章 付録

目次
Product Code: ICT6905

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

Table of Contents

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

15. Appendix

  • 15.1. About Us and Services
  • 15.2. Contact Us