データレイク市場：コンポーネント別、展開モード別、組織規模別、最終用途産業別、地域別、2024年～2031年

データレイク市場の評価、2024年～2031年

さまざまな産業で生成されるデータ量の増加、高度な分析の必要性、企業がさまざまなデータ形式から意味のある情報を抽出できるようにする手頃な価格のデータ管理ソリューションへの需要が、データレイク市場を推進する主な要因となっています。Verified Market Researchのアナリストによると、データレイク市場は、2024年には約172億1,000万米ドルの評価額を下回り、予測期間中に790億9,000万米ドルの評価額に達すると推定されています。

ヘルスケア業界は、電子健康記録（EHR）、医療画像、ゲノムシーケンスによって生成される大量の患者データを管理・分析する必要があるため、データレイク市場の成長に大きく貢献すると予想されます。これにより、同市場は2024年から2031年にかけて約21.00%のCAGRで成長することになります。

データレイク市場定義/概要

データレイクとは、多くのソースから構造化、半構造化、非構造化データを含む大量の生データを、事前に整理することなく自然な形式で保存できる集中型リポジトリのことです。この柔軟性により、企業はビジネスアプリ、IoTデバイス、ソーシャルメディアなど、さまざまなソースからデータを取得し、維持することができ、必要に応じて高度な分析や機械学習を実行することができます。データレイクは、ビッグデータ分析、リアルタイムデータ処理、予測モデリングなど、さまざまな用途で利用されており、膨大なデータセットから洞察を得て意思決定プロセスを改善したい企業にとって不可欠な存在となっています。

データレイク市場を促進する主な要因とは？

業界全体でデータの生産量が大幅に増加していることが、データレイクの需要を促進しています。International Data Corporation（IDC）によると、世界のデータスフィアは2018年の33ゼタバイトから、2025年には175ゼタバイトに増加すると予想されています。このデータ量の431%という驚異的な増加には、この爆発的なデータを管理し、そこから価値を引き出すためのデータレイクのようなスケーラブルで柔軟なストレージ・ソリューションが必要です。

ビッグデータ分析と人工知能/機械学習（AI/ML）技術の利用の増加が、データレイク市場を牽引しています。NewVantage Partnersの調査によると、著名企業の91.9%が2021年までにビッグデータとAIへの投資を拡大する予定です。データレイクは、高度なアナリティクスやAI/MLアプリケーションに必要な膨大な量の異種データを保存・処理するために必要なインフラを提供します。

さらに、クラウドコンピューティングへの移行が、クラウドベースのデータレイクの普及を加速させています。ガートナーは、2021年の30％から、2025年までに新しいデジタルワークロードの95％以上がクラウドネイティブプラットフォームに実装されると予測しています。この動向は、拡張性、コスト効率、分散データ処理とアナリティクスをサポートする能力を理由に、企業にクラウドベースのデータレイクの利用を促しています。

データガバナンスの課題はデータレイク市場をどのように阻害しているか？

データガバナンスの複雑さは、データレイク市場の成長を阻む大きな障壁となっています。組織がさまざまなソースから大量の生データを収集するにつれ、データの品質、セキュリティ、コンプライアンスの確保がより複雑になっています。強力なガバナンスフレームワークがなければ、企業はデータの整合性と規制コンプライアンスに課題が発生し、不正確な分析と不十分な意思決定につながるリスクがあります。このような複雑さは、ガバナンス・プロセスとテクノロジーに多大な投資を必要とするため、データレイクの利用を断念する企業もあります。

さらに、データレイク内でのデータ品質維持の難しさも重要な制約となっています。データはクレンジングやバリデーションを経ずに生のまま吸収されることが多いため、エラーや不正確さが発生する可能性があります。このような品質管理の欠如は、下流のアナリティクスや意思決定プロセスに好ましくない影響を及ぼし、誤った洞察をもたらします。このようなリスクを防止するために、組織は強力なデータ品質基準を採用する必要があります。

目次

第1章 世界のデータレイク市場のイントロダクション

• 市場概要
• 調査範囲
• 前提条件

第2章 エグゼクティブサマリー

第3章 VERIFIED MARKET RESEARCHの調査手法

• データマイニング
• バリデーション
• 一次資料
• データソース一覧

第4章 世界のデータレイク市場展望

• 概要
• 市場力学
  • 促進要因
  • 抑制要因
  • 機会
• ポーターのファイブフォースモデル
• バリューチェーン分析

第5章 データレイクの世界市場：コンポーネント別

• 概要
• ソリューション
• サービス

第6章 データレイクの世界市場：展開モード別

• 概要
• オンプレミス
• クラウドベース

第7章 データレイクの世界市場：組織規模別

• 概要
• 大企業
• 中小企業（SMEs）

第8章 データレイクの世界市場：エンドユーザー別

• 概要
• 通信・IT
• 銀行、金融サービス、保険（BFSI）
• 小売
• ヘルスケア
• メディア
• 政府機関
• ホスピタリティ
• 教育
• その他

第9章 データレイクの世界市場：地域別

• 概要
• 北米
  • 米国
  • カナダ
  • メキシコ
• 欧州
  • ドイツ
  • 英国
  • フランス
  • その他欧州
• アジア太平洋
  • 中国
  • 日本
  • インド
  • その他アジア太平洋地域
• 世界のその他の地域
  • ラテンアメリカ
  • 中東・アフリカ

第10章 世界のデータレイク市場の競合情勢

• 概要
• 各社の市場ランキング
• 主な発展戦略

第11章 企業プロファイル

• Microsoft
• IBM
• Oracle
• Cloudera
• Informatica
• Teradata
• Zaloni
• Snowflake
• Dremio
• HPE

第12章 付録

• 関連調査

Data Lakes Market Valuation - 2024-2031

The growing amount of data produced by various industries, the need for sophisticated analytics, and the demand for affordable data management solutions that let businesses extract meaningful information from various data formats are the main factors propelling the data lake market. According to the analyst from Verified Market Research, the data lakes market is estimated to reach a valuation of USD 79.09 Billion over the forecast subjugating around USD 17.21 Billion valued in 2024.

The healthcare industry is expected to contribute substantially to the growth of the data lake market, owing to the requirement to manage and analyze massive amounts of patient data generated by electronic health records (EHRs), medical imaging, and genomic sequencing. It enables the market to grow at a CAGR of about 21.00% from 2024 to 2031.

Data Lakes Market: Definition/ Overview

A data lake is a centralized repository that can store large amounts of raw data in its natural format, including structured, semi-structured, and unstructured data from many sources without the need for prior organizing. This flexibility enables businesses to consume and maintain data from a variety of sources, including business apps, IoT devices, and social media, allowing them to execute advanced analytics and machine learning as needed. Data lakes are used in a variety of applications, including big data analytics, real-time data processing, and predictive modeling, making them critical for companies looking to get insights from massive datasets and improve decision-making processes.

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What are the Primary Factors Propelling the Data Lakes Market?

The substantial rise in the production of data across industries has fueled the demand for data lakes. According to the International Data Corporation (IDC), the global datasphere is expected to increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This staggering 431% rise in data volume needs scalable and flexible storage solutions such as data lakes to manage and extract value from this data explosion.

The increased use of big data analytics and artificial intelligence/machine learning (AI/ML) technologies is driving the data lake market. According to NewVantage Partners' survey, 91.9% of prominent organizations plan to increase their investments in big data and AI initiatives by 2021. Data lakes provide the necessary infrastructure to store and handle enormous volumes of heterogeneous data needed for advanced analytics and AI/ML applications.

Furthermore, the shift to cloud computing is accelerating the popularity of cloud-based data lakes. Gartner anticipates that by 2025, more than 95% of new digital workloads will be implemented on cloud-native platforms, up from 30% in 2021. This trend is encouraging enterprises to use cloud-based data lakes because of their scalability, cost-effectiveness, and capacity to support distributed data processing and analytics.

How do the Data Governance Challenges Hamper the Data Lakes Market?

The complexity of data governance is a major barrier to growth in the data lakes market. As organizations collect massive amounts of raw data from a variety of sources, ensuring data quality, security, and compliance becomes more complex. Without a strong governance framework, firms risk experiencing challenges with data integrity and regulatory compliance, resulting in incorrect analytics and poor decision-making. This complexity needs significant investment in governance processes and technologies, discouraging some companies from using data lakes.

Furthermore, the difficulty of maintaining data quality within data lakes is another important constraint. Because data is frequently absorbed in its raw form without previous cleansing or validation, errors and inaccuracies may occur. This absence of quality control has an unfavorable effect on downstream analytics and decision-making processes, resulting in incorrect insights. To prevent these risks, organizations must employ strong data quality standards that involve significant resources and expertise.

Category-Wise Acumens

How does the Growing Demand for Advanced Analytics Drive Data Lakes Solutions in the Market?

The solution segment is estimated to dominate the data lakes market during the forecast period. Organizations are increasingly looking for advanced analytics skills to extract useful insights from large amounts of data. The solutions segment, which includes data discovery, integration, and analytics tools, allows businesses to easily process and analyze raw data. The demand for sophisticated analytical tools is accelerating the expansion of the solutions segment significantly.

The requirement for efficient data integration and management solutions grows as organizations amass heterogeneous datasets from several sources. The solutions segment meets this need by offering tools that assist enterprises in streamlining data ingestion, storage, and processing. This capability not only improves operational efficiency but also allows for superior decision-making processes, boosting the solutions segment's market dominance.

Furthermore, data lakes provide exceptional scalability and flexibility, enabling businesses to store and manage massive amounts of organized and unstructured data. The solutions segment capitalizes on this advantage by offering scalable infrastructures that can adapt to an organization's changing data requirements. This adaptability is particularly appealing to businesses trying to future-proof their data initiatives, reinforcing the solutions segment's market leadership.

How does the Data-Driven Decision Making in BFSI Sector Drive the Market?

The banking, financial services, & insurance (BFSI) segment is estimated to dominate the market during the forecast period. The BFSI industry relies extensively on data for decision-making processes such as risk assessment, fraud detection, and consumer insights. Data lakes enable financial institutions to store massive amounts of structured and unstructured data, allowing for advanced analytics and machine learning applications that boost operational efficiency and service delivery.

The BFSI industry is subject to severe regulations governing data management and reporting. Data lakes provide a consolidated repository that makes compliance easier by allowing firms to keep detailed records of transactions and consumer interactions. This feature promotes good data governance and enables financial institutions to respond quickly to regulatory audits and inquiries.

Furthermore, in an increasingly competitive landscape, BFSI firms are focused on individualized customer experiences to retain customers and attract new ones. Data lakes enable these firms to gather and analyze a variety of customer data sources, allowing them to personalize products, services, and marketing campaigns to individual tastes. This focused strategy improves consumer satisfaction and loyalty, hence driving segment growth.

Country/Region-wise Acumens

What are the Key Drivers Bolstering the Demand for Data Lakes in North America?

North America is estimated to dominate the data lakes market during the forecast period. North America leads in technological adoption and digital transformation activities, which fuels the demand for data lakes. According to IDC, US businesses are estimated to invest USD 1.8 Trillion in digital transformation activities by 2025. This large investment demonstrates the region's commitment to using advanced data management technologies, such as data lakes, to support digital objectives and preserve a competitive advantage.

Furthermore, the rapid proliferation of Internet of Things (IoT) devices in North America is generating large volumes of data, increasing the demand for data lakes. IoT Analytics predicts that North America will have 5.4 billion IoT connections by 2025, indicating a 14% compound annual growth rate (CAGR). This boom of connected devices generates massive volumes of heterogeneous data, necessitating scalable storage and processing solutions, establishing data lakes as a critical component of the region's IoT ecosystem.

How Does the Explosive Growth in Mobile & Internet Users Driving the Growth of the Market in Asia Pacific?

The Asia Pacific region is estimated to exhibit the highest growth within the market during the forecast period. The Asia Pacific region is experiencing a spike in mobile and internet adoption, resulting in massive amounts of data that must be efficiently stored and analyzed. According to GSMA Intelligence, the Asia Pacific region's mobile internet user base will grow from 2.7 billion in 2021 to 3.1 billion by 2025. This rapid increase in connected people generates massive amounts of heterogeneous data, making data lakes critical for organizations to acquire, store, and derive insights from this wealth of information.

Furthermore, many Asian countries are implementing national initiatives to encourage big data and artificial intelligence, resulting in increased demand for data lakes. China's New Generation Artificial Intelligence Development Plan intends to make the country a world leader in AI by 2030, with an estimated core AI industry gross output of over 1 trillion yuan (~ USD 150 Billion). Similarly, India's National Strategy for Artificial Intelligence predicts that AI will bring $957 billion to the Indian economy by 2035. These government-supported initiatives are hastening the adoption of data lakes as the basic infrastructure for big data and AI projects throughout the region.

Competitive Landscape

The competitive landscape of the data lakes market is fragmented, with multiple competitors fighting for market share in various regions and sectors. Organizations in a variety of industries, including retail, healthcare, and manufacturing, are increasingly using data lake solutions to leverage massive amounts of structured and unstructured data for better decision-making and operational efficiencies.

Some of the prominent players operating in the data lakes market include:

Microsoft

IBM

Oracle

Cloudera

Informatica

Teradata

Zaloni

Snowflake

Dremio

HPE

SAS Institute

Google

Alibaba Cloud

Tencent Cloud

Baidu

VMware

SAP

Dell Technologies

Huawei

Latest Developments

In December 2022, Atos announced the development of a new solution in collaboration with AWS that allows clients to expedite and properly monitor company key performance indicators (KPIs) by offering simple access to non-SAP and SAP data silos. 'Atos' AWS Data Lake Accelerator for SAP" is an innovative solution that delivers enterprise-wide and self-service reporting for significant insights into daily changes that rapidly impact decisions to drive the bottom line.

In November 2022, Amazon Web Services (AWS) announced the launch of Amazon Security Lake. This new cybersecurity solution automatically centralizes safety data from on-premises and cloud sources into a purpose-built data lake in a user's AWS account.

In April 2022, Google introduced the preview launch of Big Lake. This new data lake storage system allows organizations to analyze data in their data lakes and warehouses at its Cloud Data Summit.

TABLE OF CONTENTS

1 INTRODUCTION OF GLOBAL DATA LAKES MARKET

• 1.1 Overview of the Market
• 1.2 Scope of Report
• 1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

• 3.1 Data Mining
• 3.2 Validation
• 3.3 Primary Interviews
• 3.4 List of Data Sources

4 GLOBAL DATA LAKES MARKET OUTLOOK

• 4.1 Overview
• 4.2 Market Dynamics
  • 4.2.1 Drivers
  • 4.2.2 Restraints
  • 4.2.3 Opportunities
• 4.3 Porters Five Force Model
• 4.4 Value Chain Analysis

5 GLOBAL DATA LAKES MARKET, BY COMPONENT

• 5.1 Overview
• 5.2 Solution
• 5.3 Services

6 GLOBAL DATA LAKES MARKET, BY DEPLOYMENT MODE

• 6.1 Overview
• 6.2 On-Premise
• 6.3 Cloud-based

7 GLOBAL DATA LAKES MARKET, BY ORGANIZATION SIZE

• 7.1 Overview
• 7.2 Large Enterprises
• 7.3 Small & Medium Enterprises (SMEs)

8 GLOBAL DATA LAKES MARKET, BY END-USER

• 8.1 Overview
• 8.2 Telecommunication & IT
• 8.3 Banking, Financial Services, and Insurance (BFSI)
• 8.4 Retail
• 8.5 Healthcare
• 8.6 Media
• 8.7 Government
• 8.8 Hospitality
• 8.9 Education
• 8.10 Others

9 GLOBAL DATA LAKES MARKET, BY GEOGRAPHY

• 9.1 Overview
• 9.2 North America
  • 9.2.1 U.S.
  • 9.2.2 Canada
  • 9.2.3 Mexico
• 9.3 Europe
  • 9.3.1 Germany
  • 9.3.2 U.K.
  • 9.3.3 France
  • 9.3.4 Rest of Europe
• 9.4 Asia Pacific
  • 9.4.1 China
  • 9.4.2 Japan
  • 9.4.3 India
  • 9.4.4 Rest of Asia Pacific
• 9.5 Rest of the World
  • 9.5.1 Latin America
  • 9.5.2 Middle East & Africa

10 GLOBAL DATA LAKES MARKET COMPETITIVE LANDSCAPE

• 10.1 Overview
• 10.2 Company Market Ranking
• 10.3 Key Development Strategies

11 COMPANY PROFILES

• 11.1 Microsoft
  • 11.1.1 Overview
  • 11.1.2 Financial Performance
  • 11.1.3 Product Outlook
  • 11.1.4 Key Developments
• 11.2 IBM
  • 11.2.1 Overview
  • 11.2.2 Financial Performance
  • 11.2.3 Product Outlook
  • 11.2.4 Key Developments
• 11.3 Oracle
  • 11.3.1 Overview
  • 11.3.2 Financial Performance
  • 11.3.3 Product Outlook
  • 11.3.4 Key Developments
• 11.4 Cloudera
  • 11.4.1 Overview
  • 11.4.2 Financial Performance
  • 11.4.3 Product Outlook
  • 11.4.4 Key Developments
• 11.5 Informatica
  • 11.5.1 Overview
  • 11.5.2 Financial Performance
  • 11.5.3 Product Outlook
  • 11.5.4 Key Developments
• 11.6 Teradata
  • 11.6.1 Overview
  • 11.6.2 Financial Performance
  • 11.6.3 Product Outlook
  • 11.6.4 Key Developments
• 11.7 Zaloni
  • 11.7.1 Overview
  • 11.7.2 Financial Performance
  • 11.7.3 Product Outlook
  • 11.7.4 Key Developments
• 11.8 Snowflake
  • 11.8.1 Overview
  • 11.8.2 Financial Performance
  • 11.8.3 Product Outlook
  • 11.8.4 Key Developments
• 11.9 Dremio
  • 11.9.1 Overview
  • 11.9.2 Financial Performance
  • 11.9.3 Product Outlook
  • 11.9.4 Key Developments
• 11.10 HPE
  • 11.10.1 Overview
  • 11.10.2 Financial Performance
  • 11.10.3 Product Outlook
  • 11.10.4 Key Developments

12 Appendix

• 12.1 Related Research