½ÃÀ庸°í¼­
»óǰÄÚµå
1540917

¼¼°èÀÇ Àθ޸𸮠ºÐ¼® ½ÃÀå º¸°í¼­ : ¿ëµµ, Á¶Á÷ ±Ô¸ð, »ê¾÷º°, Áö¿ªº°(2024-2032³â)

In-Memory Analytics Market Report by Application, Organization Size, Vertical, and Region 2024-2032

¹ßÇàÀÏ: | ¸®¼­Ä¡»ç: IMARC | ÆäÀÌÁö Á¤º¸: ¿µ¹® 144 Pages | ¹è¼Û¾È³» : 2-3ÀÏ (¿µ¾÷ÀÏ ±âÁØ)

    
    
    




¡Ø º» »óǰÀº ¿µ¹® ÀÚ·á·Î Çѱ۰ú ¿µ¹® ¸ñÂ÷¿¡ ºÒÀÏÄ¡ÇÏ´Â ³»¿ëÀÌ ÀÖÀ» °æ¿ì ¿µ¹®À» ¿ì¼±ÇÕ´Ï´Ù. Á¤È®ÇÑ °ËÅ並 À§ÇØ ¿µ¹® ¸ñÂ÷¸¦ Âü°íÇØÁֽñ⠹ٶø´Ï´Ù.

¼¼°è Àθ޸𸮠ºÐ¼® ½ÃÀå ±Ô¸ð´Â 2023³â 57¾ï ´Þ·¯¿¡ ´ÞÇß½À´Ï´Ù. ÇâÈÄ IMARC GroupÀº 2024-2032³â±îÁö 22.3%ÀÇ ¿¬Æò±Õ ¼ºÀå·ü(CAGR)À» º¸À̸ç 2032³â±îÁö 367¾ï ´Þ·¯¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹»óÇϰí ÀÖ½À´Ï´Ù.

Àθ޸𸮠ºÐ¼®Àº ·£´ý ¾×¼¼½º ¸Þ¸ð¸®(RAM)¿¡ ÀúÀåµÈ Äõ¸® µ¥ÀÌÅ͸¦ ¿©·¯ »ç¿ëÀÚ°¡ ¼­·Î ´Ù¸¥ ¿ëµµ¿¡¼­ µ¿½Ã¿¡ ºü¸£°í ¾ÈÀüÇÏ°Ô »ç¿ëÇÒ ¼ö ÀÖµµ·Ï Áö¿øÇÕ´Ï´Ù. À̸¦ ÅëÇØ ½ÉÃþÀûÀÎ ÅëÂû·ÂÀ» ½Å¼ÓÇϰí Á¤È®ÇÏ°Ô ¾òÀ» ¼ö ÀÖ¾î Á¤º¸¿¡ ÀÔ°¢ÇÑ ´Éµ¿ÀûÀÎ ÀÇ»ç°áÁ¤À» ³»¸± ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ, ¼öÀÍ Áõ´ë, ¸®½ºÅ© °ü¸®, ½ÅÁ¦Ç° ¹× ¼­ºñ½º Çõ½ÅÀ» µ½½À´Ï´Ù. ±× °á°ú, Äõ¸® ºÐ¼®, Å¥ºê ±¸Ãà, Áý°è Å×ÀÌºí ¼³°è µî ½Ã°£ÀÌ ¸¹ÀÌ ¼Ò¿äµÇ´Â ÀÛ¾÷¿¡ ¼Ò¿äµÇ´Â ½Ã°£À» ÃÖ¼ÒÈ­ÇÒ ¼ö Àֱ⠶§¹®¿¡ Àü ¼¼°è ¸¹Àº Á¶Á÷ÀÌ Àθ޸𸮠ºÐ¼®À» äÅÃÇϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ, µ¥ÀÌÅÍ ¼Ò½º¿¡ ´ëÇÑ ¾×¼¼½º¸¦ °£¼ÒÈ­Çϰí Áï°¢ÀûÀÎ Á¶Ä¡¿Í ÀÀ´äÀ» Á¦°øÇÏ¿© ÁøÈ­ÇÏ´Â ¼ÒºñÀÚ ¼ö¿ä¿¡ ´ëÀÀÇÒ ¼ö ÀÖµµ·Ï µ½½À´Ï´Ù.

Àθ޸𸮠ºÐ¼® ½ÃÀå µ¿Çâ :

¼­ºñ½º ¹× ºñÁî´Ï½º Çõ½ÅÀ» À§ÇÑ µðÁöÅÐ ±â¼ú µµÀÔÀÌ Å©°Ô Áõ°¡ÇÔ¿¡ µû¶ó µ¥ÀÌÅͺ£À̽º ³» µ¥ÀÌÅͰ¡ ´ë·®À¸·Î È®»êµÇ°í ÀÖ½À´Ï´Ù. ÀÌ´Â Á¤º¸¿¡ ´ëÇÑ ºü¸¥ Á¢±Ù°ú ¼Õ½¬¿î ºÐ¼®À» À§ÇÑ Àθ޸𸮠ºÐ¼®ÀÇ Çʿ伺À» ÃËÁøÇÏ´Â ÁÖ¿ä ¿äÀÎÀÌ µÇ°í ÀÖ½À´Ï´Ù. ¶ÇÇÑ, µ¥ÀÌÅÍ ¿þ¾îÇϿ콺¸¦ ±¸ÃàÇÒ Àü¹® Áö½Ä°ú ¸®¼Ò½º°¡ ¾ø´Â Áß¼Ò±â¾÷(SME)ÀÇ °æ¿ì, Àθ޸𸮠ºÐ¼®Àº µ¥ÀÌÅÍ ¿þ¾îÇϿ콺¸¦ ´ëüÇÒ ¼ö ÀÖ´Â ºñ¿ë È¿À²ÀûÀÎ ´ë¾ÈÀÌ µÉ ¼ö ÀÖ½À´Ï´Ù. Àθ޸𸮠ºÐ¼®Àº ´Ù¾çÇÑ ±Ô¸ð¿Í º¹À⼺À» °¡Áø µ¥ÀÌÅ͸¦ Àú·ÅÇÑ ºñ¿ëÀ¸·Î Àü·Ê ¾ø´Â ¼Óµµ·Î ºÐ¼®ÇÒ ¼ö ÀÖ´Â ´É·ÂÀ» ¼³¸íÇÕ´Ï´Ù. À̿ʹ º°µµ·Î, ¿Â¶óÀÎ ¹ðÅ· ¼­ºñ½ºÀÇ È®´ë´Â Àü ¼¼°è ÀºÇà, ±ÝÀ¶ ¼­ºñ½º ¹× º¸Çè(BFSI) ºÎ¹®ÀÇ À§Çè °Å·¡ °ü¸® ¹× ºÎÁ¤ °áÁ¦ ŽÁö¸¦ À§ÇÑ Àθ޸𸮠ºÐ¼®ÀÇ Àû¿ë¿¡ ±àÁ¤ÀûÀÎ ¿µÇâÀ» ¹ÌÄ¡°í ÀÖ½À´Ï´Ù. ¶ÇÇÑ, Áö¸®Á¤º¸½Ã½ºÅÛ(GIS) 󸮸¦ Æ÷ÇÔÇÑ ¿ëµµ¿¡µµ Ȱ¿ëµÇ°í ÀÖ½À´Ï´Ù. ¹°·ù ¹× ¿î¼Û »ê¾÷¿¡¼­ GIS 󸮴 ±³Åë È¥Àâ, Ãßõ °æ·Î, ±³Åë À§Çè¿¡ ´ëÇÑ ½Ç½Ã°£ ¾È³»¸¦ À§ÇØ ³Î¸® »ç¿ëµÇ°í ÀÖÀ¸¸ç, ½ÃÀåÀ» ÁÖµµÇÒ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù.

º» º¸°í¼­¿¡¼­ ´Ù·é ÁÖ¿ä Áú¹®µé

  • ¼¼°è Àθ޸𸮠ºÐ¼® ½ÃÀåÀº Áö±Ý±îÁö ¾î¶»°Ô ¼ºÀåÇØ¿Ô°í, ¾ÕÀ¸·Î ¾î¶»°Ô ¼ºÀåÇÒ °ÍÀΰ¡?
  • Äڷγª19´Â ¼¼°è Àθ޸𸮠ºÐ¼® ½ÃÀå¿¡ ¾î¶² ¿µÇâÀ» ¹ÌÃÆ´Â°¡?
  • ÁÖ¿ä Áö¿ª ½ÃÀåÀº?
  • ¿ëµµº° ½ÃÀå ºÐ¼®Àº?
  • Á¶Á÷ ±Ô¸ðº° ½ÃÀå ÇöȲÀº?
  • »ê¾÷º° ½ÃÀå ºÐ¼®Àº?
  • »ê¾÷ °¡Ä¡»ç½½ÀÇ ´Ù¾çÇÑ ´Ü°è´Â?
  • ¾÷°èÀÇ ÁÖ¿ä ÃËÁø¿äÀΰú °úÁ¦´Â?
  • ¼¼°è Àθ޸𸮠ºÐ¼® ½ÃÀå ±¸Á¶¿Í ÁÖ¿ä ÁøÃâ ±â¾÷Àº?
  • ¾÷°èÀÇ °æÀïÀº ¾î´À Á¤µµÀΰ¡?

¸ñÂ÷

Á¦1Àå ¼­¹®

Á¦2Àå Á¶»ç ¹üÀ§¿Í Á¶»ç ¹æ¹ý

  • Á¶»ç ¸ñÀû
  • ÀÌÇØ°ü°èÀÚ
  • µ¥ÀÌÅÍ ¼Ò½º
    • 1Â÷ Á¤º¸
    • 2Â÷ Á¤º¸
  • ½ÃÀå ÃßÁ¤
    • º¸ÅÒ¾÷ Á¢±Ù
    • Åé´Ù¿î Á¢±Ù
  • Á¶»ç ¹æ¹ý

Á¦3Àå ÁÖ¿ä ¿ä¾à

Á¦4Àå ¼­·Ð

  • °³¿ä
  • ÁÖ¿ä ¾÷°è µ¿Çâ

Á¦5Àå ¼¼°èÀÇ Àθ޸𸮠ºÐ¼® ½ÃÀå

  • ½ÃÀå °³¿ä
  • ½ÃÀå ºÐ¼®
  • COVID-19ÀÇ ¿µÇâ
  • ½ÃÀå ¿¹Ãø

Á¦6Àå ½ÃÀå ºÐ¼® : ¿ëµµº°

  • °í°´ °æÇè °ü¸®
  • µðÀÚÀÎ ¹× Çõ½Å
  • ¿î¿µ ÃÖÀûÈ­
  • ¸¶ÄÉÆÃ °ü¸®
  • ½Ç½Ã°£ ºÐ¼® ¹× ÀÇ»ç°áÁ¤
  • ±âŸ

Á¦7Àå ½ÃÀå ºÐ¼® : Á¶Á÷ ±Ô¸ðº°

  • Áß¼Ò±â¾÷
  • ´ë±â¾÷

Á¦8Àå ½ÃÀå ºÐ¼® : ¾÷°èº°

  • ÀºÇà/±ÝÀ¶¼­ºñ½º/º¸Çè(BFSI)
  • ¼Ò¸Å¾÷ ¹× E-Commerce
  • Á¤ºÎ ¹× ¹æÀ§
  • ÀÇ·á
  • Á¦Á¶
  • IT ¹× Åë½Å
  • ±âŸ

Á¦9Àå ½ÃÀå ºÐ¼® : Áö¿ªº°

  • ºÏ¹Ì
    • ¹Ì±¹
    • ij³ª´Ù
  • ¾Æ½Ã¾ÆÅÂÆò¾ç
    • Áß±¹
    • ÀϺ»
    • Àεµ
    • Çѱ¹
    • È£ÁÖ
    • Àεµ³×½Ã¾Æ
    • ±âŸ
  • À¯·´
    • µ¶ÀÏ
    • ÇÁ¶û½º
    • ¿µ±¹
    • ÀÌÅ»¸®¾Æ
    • ½ºÆäÀÎ
    • ·¯½Ã¾Æ
    • ±âŸ
  • ¶óƾ¾Æ¸Þ¸®Ä«
    • ºê¶óÁú
    • ¸ß½ÃÄÚ
    • ±âŸ
  • Áßµ¿ ¹× ¾ÆÇÁ¸®Ä«
    • ½ÃÀå µ¿Çâ
    • ½ÃÀå ºÐ¼® : ±¹°¡º°
    • ½ÃÀå ¿¹Ãø

Á¦10Àå SWOT ºÐ¼®

  • °³¿ä
  • °­Á¡
  • ¾àÁ¡
  • ±âȸ
  • À§Çù

Á¦11Àå ¹ë·ùüÀÎ ºÐ¼®

Á¦12Àå PorterÀÇ Five Forces ºÐ¼®

  • °³¿ä
  • ¹ÙÀ̾îÀÇ ±³¼··Â
  • °ø±Þ ±â¾÷ÀÇ ±³¼··Â
  • °æÀï Á¤µµ
  • ½Å±Ô ÁøÃâ¾÷üÀÇ À§Çù
  • ´ëüǰÀÇ À§Çù

Á¦13Àå °¡°Ý ºÐ¼®

Á¦14Àå °æÀï ±¸µµ

  • ½ÃÀå ±¸Á¶
  • ÁÖ¿ä ±â¾÷
  • ÁÖ¿ä ±â¾÷ °³¿ä
    • ActiveViam
    • Amazon Web Services Inc.
    • Hitachi Ltd.
    • Information Builders Inc.(Tibco Software Inc.)
    • International Business Machines Corporation
    • Kognitio Ltd
    • Microstrategy Incorporated
    • Oracle Corporation
    • Qlik Technologies
    • SAP SE
    • SAS Institute Inc.
    • Software AG
LSH 24.09.03

The global in-memory analytics market size reached US$ 5.7 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 36.7 Billion by 2032, exhibiting a growth rate (CAGR) of 22.3% during 2024-2032.

In-memory analytics query data in random access memory (RAM) can be used by multiple users across different applications rapidly, securely, and concurrently. It provides deep insights with speed and precision, resulting in informed and proactive decisions. It also increases revenue, manages risks, and assists in new product or service innovation. Consequently, organizations worldwide are adopting in-memory analytics as it helps them minimize the time spent on query analysis, cube building, aggregate table designing, and other time-consuming tasks. It further enables them to simplify access to data sources, deliver immediate actions and responses, and meet evolving consumer demands.

In-Memory Analytics Market Trends:

A considerable rise in the adoption of digital technology to transform services or businesses is resulting in a massive proliferation of data in databases. This acts as a primary factor promoting the need for in-memory analytics for fast access to information and easy analysis. Moreover, it is a cost-effective alternative to data warehouses for small and medium-sized enterprises (SMEs) that lack the expertise and resources to construct a data warehouse. In-memory analytics provides the ability to analyze data of varied sizes and complexities with unprecedented speed at an affordable cost. Apart from this, the growing utilization of online banking services is positively influencing the application of in-memory analytics in the banking, financial services, and insurance (BFSI) sector worldwide for risk and transaction management and detection of fraud payments. Furthermore, it is utilized in applications involving geographic information system (GIS) processing. The widespread use of GIS processing for real-time directions on traffic congestion, recommended routes, and traffic hazards in the logistics and transportation industry is anticipated to drive the market.

Key Market Segmentation:

IMARC Group provides an analysis of the key trends in each sub-segment of the global in-memory analytics market report, along with forecasts at the global, regional and country level from 2024-2032. Our report has categorized the market based on application, organization size and vertical.

Breakup by Application:

Customer Experience Management

Design and Innovation

Operation Optimization

Marketing Management

Real-Time Analysis and Decision-making

Others

Breakup by Organization Size:

Small and Medium Enterprises

Large Enterprises

Breakup by Vertical:

BFSI

Retail and E-commerce

Government and Defense

Healthcare

Manufacturing

IT and Telecommunication

Others

Breakup by Region:

North America

United States

Canada

Asia-Pacific

China

Japan

India

South Korea

Australia

Indonesia

Others

Europe

Germany

France

United Kingdom

Italy

Spain

Russia

Others

Latin America

Brazil

Mexico

Others

Middle East and Africa

Competitive Landscape:

The competitive landscape of the industry has also been examined along with the profiles of the key players being ActiveViam, Amazon Web Services Inc., Hitachi Ltd., Information Builders Inc. (Tibco Software Inc.), International Business Machines Corporation, Kognitio Ltd, Microstrategy Incorporated, Oracle Corporation, Qlik Technologies, SAP SE, SAS Institute Inc. and Software AG.

Key Questions Answered in This Report:

  • How has the global in-memory analytics market performed so far and how will it perform in the coming years?
  • What has been the impact of COVID-19 on the global in-memory analytics market?
  • What are the key regional markets?
  • What is the breakup of the market based on the application?
  • What is the breakup of the market based on the organization size?
  • What is the breakup of the market based on the vertical?
  • What are the various stages in the value chain of the industry?
  • What are the key driving factors and challenges in the industry?
  • What is the structure of the global in-memory analytics market and who are the key players?
  • What is the degree of competition in the industry?

Table of Contents

1 Preface

2 Scope and Methodology

  • 2.1 Objectives of the Study
  • 2.2 Stakeholders
  • 2.3 Data Sources
    • 2.3.1 Primary Sources
    • 2.3.2 Secondary Sources
  • 2.4 Market Estimation
    • 2.4.1 Bottom-Up Approach
    • 2.4.2 Top-Down Approach
  • 2.5 Forecasting Methodology

3 Executive Summary

4 Introduction

  • 4.1 Overview
  • 4.2 Key Industry Trends

5 Global In-Memory Analytics Market

  • 5.1 Market Overview
  • 5.2 Market Performance
  • 5.3 Impact of COVID-19
  • 5.4 Market Forecast

6 Market Breakup by Application

  • 6.1 Customer Experience Management
    • 6.1.1 Market Trends
    • 6.1.2 Market Forecast
  • 6.2 Design and Innovation
    • 6.2.1 Market Trends
    • 6.2.2 Market Forecast
  • 6.3 Operation Optimization
    • 6.3.1 Market Trends
    • 6.3.2 Market Forecast
  • 6.4 Marketing Management
    • 6.4.1 Market Trends
    • 6.4.2 Market Forecast
  • 6.5 Real-Time Analysis and Decision-making
    • 6.5.1 Market Trends
    • 6.5.2 Market Forecast
  • 6.6 Others
    • 6.6.1 Market Trends
    • 6.6.2 Market Forecast

7 Market Breakup by Organization Size

  • 7.1 Small and Medium Enterprises
    • 7.1.1 Market Trends
    • 7.1.2 Market Forecast
  • 7.2 Large Enterprises
    • 7.2.1 Market Trends
    • 7.2.2 Market Forecast

8 Market Breakup by Vertical

  • 8.1 BFSI
    • 8.1.1 Market Trends
    • 8.1.2 Market Forecast
  • 8.2 Retail and E-commerce
    • 8.2.1 Market Trends
    • 8.2.2 Market Forecast
  • 8.3 Government and Defense
    • 8.3.1 Market Trends
    • 8.3.2 Market Forecast
  • 8.4 Healthcare
    • 8.4.1 Market Trends
    • 8.4.2 Market Forecast
  • 8.5 Manufacturing
    • 8.5.1 Market Trends
    • 8.5.2 Market Forecast
  • 8.6 IT and Telecommunication
    • 8.6.1 Market Trends
    • 8.6.2 Market Forecast
  • 8.7 Others
    • 8.7.1 Market Trends
    • 8.7.2 Market Forecast

9 Market Breakup by Region

  • 9.1 North America
    • 9.1.1 United States
      • 9.1.1.1 Market Trends
      • 9.1.1.2 Market Forecast
    • 9.1.2 Canada
      • 9.1.2.1 Market Trends
      • 9.1.2.2 Market Forecast
  • 9.2 Asia-Pacific
    • 9.2.1 China
      • 9.2.1.1 Market Trends
      • 9.2.1.2 Market Forecast
    • 9.2.2 Japan
      • 9.2.2.1 Market Trends
      • 9.2.2.2 Market Forecast
    • 9.2.3 India
      • 9.2.3.1 Market Trends
      • 9.2.3.2 Market Forecast
    • 9.2.4 South Korea
      • 9.2.4.1 Market Trends
      • 9.2.4.2 Market Forecast
    • 9.2.5 Australia
      • 9.2.5.1 Market Trends
      • 9.2.5.2 Market Forecast
    • 9.2.6 Indonesia
      • 9.2.6.1 Market Trends
      • 9.2.6.2 Market Forecast
    • 9.2.7 Others
      • 9.2.7.1 Market Trends
      • 9.2.7.2 Market Forecast
  • 9.3 Europe
    • 9.3.1 Germany
      • 9.3.1.1 Market Trends
      • 9.3.1.2 Market Forecast
    • 9.3.2 France
      • 9.3.2.1 Market Trends
      • 9.3.2.2 Market Forecast
    • 9.3.3 United Kingdom
      • 9.3.3.1 Market Trends
      • 9.3.3.2 Market Forecast
    • 9.3.4 Italy
      • 9.3.4.1 Market Trends
      • 9.3.4.2 Market Forecast
    • 9.3.5 Spain
      • 9.3.5.1 Market Trends
      • 9.3.5.2 Market Forecast
    • 9.3.6 Russia
      • 9.3.6.1 Market Trends
      • 9.3.6.2 Market Forecast
    • 9.3.7 Others
      • 9.3.7.1 Market Trends
      • 9.3.7.2 Market Forecast
  • 9.4 Latin America
    • 9.4.1 Brazil
      • 9.4.1.1 Market Trends
      • 9.4.1.2 Market Forecast
    • 9.4.2 Mexico
      • 9.4.2.1 Market Trends
      • 9.4.2.2 Market Forecast
    • 9.4.3 Others
      • 9.4.3.1 Market Trends
      • 9.4.3.2 Market Forecast
  • 9.5 Middle East and Africa
    • 9.5.1 Market Trends
    • 9.5.2 Market Breakup by Country
    • 9.5.3 Market Forecast

10 SWOT Analysis

  • 10.1 Overview
  • 10.2 Strengths
  • 10.3 Weaknesses
  • 10.4 Opportunities
  • 10.5 Threats

11 Value Chain Analysis

12 Porters Five Forces Analysis

  • 12.1 Overview
  • 12.2 Bargaining Power of Buyers
  • 12.3 Bargaining Power of Suppliers
  • 12.4 Degree of Competition
  • 12.5 Threat of New Entrants
  • 12.6 Threat of Substitutes

13 Price Analysis

14 Competitive Landscape

  • 14.1 Market Structure
  • 14.2 Key Players
  • 14.3 Profiles of Key Players
    • 14.3.1 ActiveViam
      • 14.3.1.1 Company Overview
      • 14.3.1.2 Product Portfolio
    • 14.3.2 Amazon Web Services Inc.
      • 14.3.2.1 Company Overview
      • 14.3.2.2 Product Portfolio
      • 14.3.2.3 Financials
      • 14.3.2.4 SWOT Analysis
    • 14.3.3 Hitachi Ltd.
      • 14.3.3.1 Company Overview
      • 14.3.3.2 Product Portfolio
      • 14.3.3.3 Financials
      • 14.3.3.4 SWOT Analysis
    • 14.3.4 Information Builders Inc. (Tibco Software Inc.)
      • 14.3.4.1 Company Overview
      • 14.3.4.2 Product Portfolio
    • 14.3.5 International Business Machines Corporation
      • 14.3.5.1 Company Overview
      • 14.3.5.2 Product Portfolio
      • 14.3.5.3 Financials
      • 14.3.5.4 SWOT Analysis
    • 14.3.6 Kognitio Ltd
      • 14.3.6.1 Company Overview
      • 14.3.6.2 Product Portfolio
    • 14.3.7 Microstrategy Incorporated
      • 14.3.7.1 Company Overview
      • 14.3.7.2 Product Portfolio
      • 14.3.7.3 Financials
      • 14.3.7.4 SWOT Analysis
    • 14.3.8 Oracle Corporation
      • 14.3.8.1 Company Overview
      • 14.3.8.2 Product Portfolio
      • 14.3.8.3 Financials
      • 14.3.8.4 SWOT Analysis
    • 14.3.9 Qlik Technologies
      • 14.3.9.1 Company Overview
      • 14.3.9.2 Product Portfolio
    • 14.3.10 SAP SE
      • 14.3.10.1 Company Overview
      • 14.3.10.2 Product Portfolio
      • 14.3.10.3 Financials
      • 14.3.10.4 SWOT Analysis
    • 14.3.11 SAS Institute Inc.
      • 14.3.11.1 Company Overview
      • 14.3.11.2 Product Portfolio
      • 14.3.11.3 SWOT Analysis
    • 14.3.12 Software AG
      • 14.3.12.1 Company Overview
      • 14.3.12.2 Product Portfolio
      • 14.3.12.3 Financials
ºñ±³¸®½ºÆ®
0 °ÇÀÇ »óǰÀ» ¼±Åà Áß
»óǰ ºñ±³Çϱâ
Àüü»èÁ¦