IBM Technical Exchange: India AI and Data Science Hub

IBM Technical Exchange: India AI and Data Science Hub

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IDC (International Data Corporation,) PDF Report

By Suman Suhag posted Tue May 06, 2025 08:29 AM

  

๐Ÿ“Š Market Overview

(Page 2, Lines 1โ€“40)

  • The data intelligence platform market ๐Ÿ“ˆ has evolved rapidly since 2018, driven by regulations like GDPR and the need for AI-ready data ๐Ÿค–.

  • IDC tracks this market by aggregating submarkets: data quality, master data intelligence, database lifecycle management, and metadata management.

  • 83% of organizations have changed their data strategy since GenAI emerged (IDC CDO Survey, Summer 2024).

  • Over half now state data management is a top organizational focus.

๐Ÿš€ Key Trends and Drivers

(Page 2, Lines 20โ€“40; Page 3, Lines 1โ€“25)

  • AI-Readiness: GenAI is fueling demand for high-quality, governed data for AI initiatives ๐Ÿค–โœจ.

  • Consolidation: Platforms now bundle cataloging, lineage, quality, and marketplaces into unified solutions ๐Ÿงฉ.

  • Compliance: Data governance for privacy and security is essential, especially in regulated sectors ๐Ÿ›ก๏ธ.

  • Unstructured Data: Managing โ€œdarkโ€ and unstructured data is increasingly vital for analytics and AI ๐ŸŒ‘๐Ÿ“‚.

  • Hybrid/Multicloud: Flexible deployment (SaaS, private, hybrid cloud) is a must for modern enterprises โ˜๏ธ๐Ÿ”„.

๐ŸŒŽ Regional and Sector Analysis

(Page 4, Lines 1โ€“20; Page 5, Lines 10โ€“20)

  • Vendors must operate in North America and at least one other global region ๐ŸŒ.

  • Key sectors: Financial services, retail, and telecom are leading adopters ๐Ÿ’ณ๐Ÿ›’๐Ÿ“ž.

  • Regulatory compliance is a major driver in these industries.

๐Ÿ’ก Technology Adoption Insights

(Page 4, Lines 20โ€“50; Page 5, Lines 1โ€“30)

  • AI & ML Integration: Platforms like IBM Knowledge Catalog (IKC) use LLM-based AI for metadata enrichment and plan to add interactive assistants soon ๐Ÿค๐Ÿค–.

  • Automation: Smart automation reduces manual work and boosts data quality โšก.

  • Data Observability: Real-time monitoring of data drift and shift is now standard for continuous AI/analytics operations โฑ๏ธ.

  • Partner Ecosystem: Strong partner programs and customer success initiatives are key for deployment and value realization ๐Ÿค.

๐Ÿงญ Strategic Recommendations

(Page 4, Lines 20โ€“50)

  • Define Requirements: Clearly understand and document your needs before procurement ๐Ÿ“.

  • Prioritize Integration: Choose platforms with cataloging, governance, lineage, quality, and product hubs for AI-ready data ๐Ÿงฉ.

  • Support for All Data: Ensure support for both structured and unstructured data for future-proofing ๐Ÿ“Š๐Ÿ“‚.

  • Flexible Deployment: Look for multicloud and hybrid support for maximum scalability and adaptability โ˜๏ธ.

  • Vendor Strength: Select vendors with strong AI/ML features and robust partner ecosystems ๐Ÿ’ช.

๐Ÿ“ˆ Future Projections and Growth Areas

(Page 4, Lines 30โ€“50; Page 5, Lines 1โ€“10)

  • AI-Driven Automation: Expect deeper AI/ML and LLM integration in data intelligence platforms ๐Ÿค–๐Ÿ”ฎ.

  • Data Marketplaces: Internal data marketplaces and product hubs will become central to data sharing and monetization strategies ๐Ÿช.

  • Advanced Lineage: Ongoing improvements in automated data lineage, especially through acquisitions and integration ๐Ÿ”—.

  • Data Literacy: Solutions will focus more on boosting data literacy across organizations ๐Ÿ“š.

๐Ÿ“Š Structured Data Table

๐Ÿ“Œ Metric/Insight ๐Ÿ“ˆ Value/Forecast ๐Ÿ“’ Notes/Comparison ๐Ÿ“„ Page/Line Reference
Orgs changing data strategy post-GenAI 83% IDC CDO Survey, Summer 2024 p2, l35โ€“40
Data management now top focus >50% IDC CDO Survey, Summer 2024 p2, l36โ€“40
Minimum vendor revenue (2023) $25 million IDC MarketScape inclusion criteria p4, l10โ€“15
IBM Knowledge Catalog profiling throughput 80โ€“90 columns/sec For auto enrichment, profiling, analysis p5, l25โ€“30
IBM Employees 288,000+ IBM company profile p5, l15
IBM Key Sectors Financial, Retail, Telecom Industry focus p5, l16โ€“17
Supported Deployment Models SaaS, Private Cloud, Hybrid IBM Knowledge Catalog p5, l18โ€“19
Pricing Model Subscription, Perpetual (core) IBM Knowledge Catalog p5, l20โ€“21
Vendor global revenue requirement 5%+ from Americas + 1 global region IDC MarketScape inclusion criteria p4, l16โ€“18

๐Ÿ”„ Recurring Themes & Significant Changes

  • Consolidation: Standalone data catalogs are now part of integrated data intelligence platforms ๐Ÿงฉ (p2, l20โ€“30).

  • AI/ML Differentiation: Machine learning and LLM-based features are now standard for leading vendors ๐Ÿค– (p5, l25โ€“30).

  • Governance for AI: Data governance is increasingly focused on supporting AI, not just compliance ๐Ÿ›ก๏ธ (p3, l10โ€“20).

  • Unstructured Data: Managing dark and unstructured data is a growing priority ๐ŸŒ‘ (p4, l30โ€“40).

๐Ÿ”ฌ Future Analysis Techniques

  • NLP for Sentiment/Keyword Trends: Extract sentiment and keyword trends from future IDC reports using NLP ๐Ÿง .

  • Time-Series Forecasting: Apply forecasting models (ARIMA, Prophet) to IDCโ€™s market data ๐Ÿ“ˆ.

  • Comparative Analytics: Compare vendor positioning and capability scores across 2020, 2022, and 2024 reports ๐Ÿ“Š.

  • Visualization-Ready Formats: Export insights in JSON/CSV for dashboard integration ๐Ÿ“ฆ.

๐Ÿ† Actionable Insights for Business Strategy

  • Invest in integrated, AI-enabled data intelligence platforms to future-proof data management and analytics ๐Ÿค๐Ÿค–.

  • Prioritize vendors with proven industry experience and strong regulatory compliance support ๐Ÿ›ก๏ธ.

  • Ensure solutions handle both structured and unstructured data for maximum AI/GenAI potential ๐Ÿ“Š๐Ÿ“‚.

  • Leverage platforms with robust partner ecosystems and customer success programs for smooth implementation and ongoing value ๐Ÿš€.

  • Monitor evolving trends in data governance, AI integration, and marketplace functionality to stay ahead of the competition ๐Ÿ”.

Worldwide Data Intelligence Platform Software 2024 Vendor Assessment, hereโ€™s a graphical representation of the key market data and trends, especially focusing on the impact of GenAI on data strategy and the market evolution. Since I cannot create images directly in this chat, Iโ€™ll provide a ready-to-use graph code (for Python/Matplotlib) and a visual mockup for you to use.

๐Ÿ“ˆ Graph: Impact of GenAI on Data Strategy

Graph Title:
Organizational Shift in Data Strategy Post-GenAI (2024)

Data Source:
IDC Office of the CDO Survey, Summer 2024 (Page 2, Lines 35โ€“40)

Key Data Points:

  • 83% of organizations changed their data strategy after GenAI emerged.

  • Over 50% now state data management is a top organizational focus.

1. Python/Matplotlib Code Example








import matplotlib.pyplot as plt



# Data
categories = ['Changed Data Strategy', 'Data Management Top Focus']
percentages = [83, 55]  # 55% as a representative value for "over half"



# Create bar chart
plt.figure(figsize=(8,5))
bars = plt.bar(categories, percentages, color=['#4F81BD', '#C0504D'])
plt.ylim(0, 100)



# Add value labels
for bar in bars:
    yval = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2, yval + 2, f'{yval}%', ha='center', fontsize=12)



plt.title('๐Ÿ“Š Impact of GenAI on Data Strategy (2024)', fontsize=14)
plt.ylabel('Percentage of Organizations (%)')
plt.xlabel('Strategic Change')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

2. Visual Mockup

3. Visualization-Ready Data (CSV/JSON)

CSV:








|                   ๐Ÿ“Š Impact of GenAI on Data Strategy (2024)                   |
|------------------------------------------------------------------------------|
|  100% +--------------------------------------------------------------------+  |
|      |                                                                    |  |
|   90%|    โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     |  |
|      |    โ–ˆ      Changed Data Strategy (83%)                      โ–ˆ        |  |
|   80%|    โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     |  |
|      |                                                                    |  |
|   70%|                                                                    |  |
|   60%|    โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ                            |  |
|      |    โ–ˆ      Data Management Top Focus (>50%)                โ–ˆ        |  |
|   50%|    โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ                            |  |
|      |                                                                    |  |
|    0%+--------------------------------------------------------------------+  |
|          Changed Data Strategy     Data Management Top Focus                |

JSON:








Category,Percentage
Changed Data Strategy,83
Data Management Top Focus,55

๐Ÿ“Œ How to Use

  • Python code: Paste into a Jupyter notebook or Python script to generate the bar chart.

  • CSV/JSON: Import into Excel, Power BI, Tableau, or any dashboard tool for instant visualization.

  • Visual mockup: Use as a reference for creating presentation slides.

๐Ÿ“„ Source Reference

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