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Data Management and Analytics Market Report 2024–2030

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A 246-page report detailing the market for data management and analytics solutions.
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Document type: PDF, PPTX
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Published: May 2024
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Main author: Mohammad Hasan
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About the report

The Data Management and Analytics Market Report 2024–2030 is part of IoT Analytics’ ongoing coverage of software/analytics topics. The content presented in this report is based on a compilation of primary research, including surveys and interviews with 30+ industry experts from data management and analytics vendors and end users conducted between July 2023 and February 2024. The report encompasses a holistic overview of the current state of the data management and analytics market and its intersection with adjacent markets, such as Generative AI (Gen AI) and IoT, including market projections, factors driving adoption, the competitive landscape, a technology and process implementation overview, notable trends and developments, and insightful case studies.

The primary objective of this document is to provide our readers with a comprehensive understanding of the current data management and analytics landscape, offering in-depth analysis, market sizing, and valuable insights to facilitate informed decision-making and strategic planning.

Data Management Definition

How IoT Analytics defines Data Management

Data management is the systematic approach to handling data, which includes the collection, storage, processing, utilization, and safeguarding of information.
This process is integral to facilitating informed decision-making and supporting an organization’s strategic goals. By doing so, data management becomes a cornerstone in driving operational improvements, enhancing customer satisfaction, and achieving a competitive advantage in the marketplace.

Challenges faced by industry leaders

  1. The ever-increasing growth of data presents a key data management challenge. To tackle this, companies are deploying data management platforms that enhance data ingestion and analytics capabilities, helping to maintain clarity and control over their sprawling data assets.
  2. The evolving regulatory landscape complicates compliance and increases risk. A streamlined data management process with embedded compliance checks as part of the data governance framework, ensuring that data are processed in line with current laws, reduces the risk of breaches and non-compliance penalties.
  3. The cost and complexity associated with data security and privacy are rising. Well-managed data pipelines, equipped with proper security measures such as access controls, can enhance data protection mechanisms, making privacy management more methodical and reducing the financial burden on organizations.

The challenges mentioned above are just the tip of the iceberg. The right data management tools are thus critical in addressing these issues, enabling efficient data access and analysis that supports proactive and informed decision-making in a rapidly evolving market.

About the data management market

According to the Data Management and Analytics Market Report 2024–2030 by IoT Analytics, the market is predicted to grow at a compound annual growth rate (CAGR) of 16%. By 2030, it’s expected to be worth $513.3 billion.

The rising relevance of database technologies, data architectures, analytics, and data governance tools in fulfilling business needs has been instrumental in the recent expansion of the data management market.

Over the next six years, data analytics is predicted to contribute significantly to the growth of the data management market. Notably, data science is seeing an uptick, surpassing the overall market growth due to the increased demand for AI and ML tools, such as predictive analytics and generative AI.

Table of Contents

Data Management and Analytics Market Report 2024–2030 (PDF)

  1. Executive summary
  2. Introduction
    1. 2.1. Data management – definition
    2. 2.2. Components of data management
    3. 2.3. Evolution of database models and data architecture
    4. 2.4. Understanding data
    5. 2.5. Data challenges faced by industry leaders
    6. 2.6. Data management case study 1—Southwest’s Christmas 2022 debacle
    7. 2.7. Data management case study 2—Netflix’s approach to global web traffic
  3. Technology overview
    1. 3.1. Modern data stack
    2. 3.2. Modern data stack case study: The four data evolution steps at Uber
    3. 3.3. Components of the data stack– technological deep dive with examples
    4. 3.3.1. Sources
    5. 3.3.2. Ingestion
    6. 3.3.3. Storage – Storage technologies, Data architecture
    7. 3.3.4. Transform
    8. 3.3.5. Analytics – Business intelligence, Data science
    9. 3.3.6. Data governance & security
    10. 3.3.7. Data orchestration
  4. IoT and data management
    1. 4.1. Exploring the characteristics of the IoT data
    2. 4.2. IoT data management and analytics – Example
    3. 4.3. Strategic framework for IoT data analytics
    4. 4.3. Five examples of data management aiding typical IoT use cases
  5. Interplay between AI and data management
    1. 5.1. Relationship between data management and AI
    2. 5.2. Gen AI as a global economic catalyst
    3. 5.3. Exponential revenue rise of the Gen AI market
    4. 5.4. Tracking the adoption of AI technologies in business
    5. 5.5. Gen AI‘s transformative impact on data management
    6. 5.6. In-depth coverage of the Gen AI-led disruption
  6. Market size and outlook
    1. 6.1. Global spending on data management and analytics by – stages, segment, region, and country
  7. Competitive Landscape
    1. 7.1. Modern data mgt. vendors Vs. Legacy data mgt. vendors
    2. 7.2. Data management—vendor comparison by component
    3. 7.3. Data management market share 2023
    4. 7.4. Data management and analytics vendor profiles
  8. Case Studies
    1. 8 real-world case studies focusing on the practical applications of vendor technologies
  9. Funding and M&A
    1. 9.1. List of top 15 investment rounds
    2. 9.2. List of top 15 mergers and acquisitions
  10. Trends and developments
    1. 8 trends related to technologies and methodologies, architectural evolution, and business strategy and economic consideration
  11. Market Sizing Definitions and Methodology

Authors

Knud Lasse Lueth, Mohammad Hasan, Oktay Demir, Philipp Wegner

Questions answered

  • What constitutes data management, and how does its evolution reflect the strategic needs of contemporary business operations and decision-making?
  • What is the market size for data management and analytics solutions? What is the projected growth?
  • What specific developments and synergies within the market are projected to impact market size and outlook?
  • Which companies lead the market in terms of the market share?
  • How is the competitive landscape within data management evolving, particularly between hyperscalers and niche vendors?
  • How are organizations redefining data management practices to adapt to Gen AI’s growing influence across technology and industry sectors?
  • How are different end users leveraging the tools and technologies from data management vendors to solve real-world challenges?
  • Which pockets of data management are receiving the most funding, and what does the M&A situation look like?
  • What are the notable trends shaping the data management and analytics landscape? How do the trends influence the direction of business strategy?

Companies mentioned

A selection of companies mentioned in the report.

AWS

Alibaba Cloud

Alteryx

Cloudera

Confluent

Databricks

Datadog

Google Cloud

IBM

Informatica

Mathworks

Microsoft

MongoDB

Oracle

Qlik

SAP

Salesforce

Snowflake

Splunk

Teradata

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