Data Warehousing Market 2019 – 2025 - Top Vendors Analysis, Market Challenges and Geographical Analysis

Data warehousing market is projected to surpass USD 30 billion by 2025. The market growth is attributed to the rising adoption of data warehousing solutions among enterprises to simplify big data management. Such solutions enable enterprises to efficiently store and analyze vast volumes of enterprise data and are widely deployed for a variety of applications such as data mining, statistical reporting, and knowledge management. The data warehousing solutions are slowly substituting traditional data repositories and legacy database management systems for enhanced business intelligence. As data warehousing systems offer critical business insights into enterprises for sustained business growth and maintenance of competitive advantage, the demand for these solutions is expected to accelerate over the forecast timeline.
Huge investments in cloud technology and the increasing adoption of cloud platforms by enterprises will also drive the demand for data warehousing solutions. On-premise appliances are consistently falling behind on storage and computing power requirements due to the excessive volume and speed of data generation. This has compelled the enterprises to adopt cloud-based solutions with minimum infrastructure requirements, on-demand computing, and unlimited storage benefits.
The unstructured data segment held a major market share in 2018 and is expected to maintain its dominance in the market with a share of over 65% in 2025. Unstructured data is gaining significant traction as enterprises realize the economic potential of this data. Unstructured data from various sources including social media, email, search history, and geolocation details is enabling organizations to design successful strategies for business growth. For instance, in December 2017, the healthcare firm Cerner Corporation deployed data warehousing solutions from Microfocus to analyze unstructured data sourced from patient queries and medical searches. These solutions helped Cerner in providing a better prognosis and in reducing patient waiting times.
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The large enterprises segment is expected to grow at a CAGR of over 10% from 2019 to 2025. The increasing volume of enterprise data, high budget, and dedicated IT staff will propel the early adoption of data warehousing solutions among large enterprises. With the development of hybrid deployment models and unstructured data handling tools, large enterprises are expected to significantly increase the usage of data warehousing systems for economic benefits and business growth.
Hybrid solutions in the data warehousing market are expected to grow at a CAGR of over 15% from 2019 to 2025. Hybrid solutions offer efficient integration of on-premise appliances with a cloud platform. This provides enterprises with the flexibility and scalability of cloud-based solutions with additional data security and control. As more enterprises are adopting hybrid solutions to upgrade their existing on-premise data warehousing appliances, the segment will witness a significant market growth over the forecast timeline.
The data mining segment is projected to grow at a CAGR of over 10% over the forecast timeline. Data mining holds great potential for organizations as it helps to analyze critical information in vast volumes of enterprise data. The development of new data mining tools such as parallel processing algorithms and natural language processing systems has profoundly assisted enterprises in critical business applications such as customer segmentation, fraud detection, corporate surveillance, and product research analysis.
The healthcare segment is expected to grow at a CAGR of over 15% from 2019 to 2025. As the adoption of digitalized platforms and IoT technology continues to grow, healthcare institutions face challenges related to the management of important healthcare data. This drives the demand for data warehousing solutions enabling healthcare enterprises to leverage the data for smart healthcare solutions such as automated patient monitoring, predictive disease diagnosis, and new medicine development.
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The Europe data warehousing market held a share of over 20% in 2018. This growth is due to rise in the adoption of cloud computing, hefty government funding in smart cities & IoT, and establishment of data centers in major European cities (Zurich, London, Frankfurt, and Madrid) by several data warehousing vendors. As enterprises in the region are rapidly migrating to cloud-based solutions, the demand for data warehousing will increase due to the flexibility and cost-effectiveness of these solutions.
The companies operating in the data warehousing market are focusing on various business growth strategies including new product developments, partnerships, and geographical expansion. Through such strategic moves, the companies are trying to gain a broader market share and maintain their leadership in the market. For instance, in April 2019, SAP launched its new data center in Moscow to offer SAP4/HANA ERP and data warehousing solutions for Russian enterprises. In May 2019, Qlik acquired Attunity for USD 560 million. This acquisition helped Qlik to add data warehousing solutions into its product portfolio and strengthen its position as a big data and BI solutions provider.
Table of Contents
Chapter 1. Methodology & Scope
	
1.1. Methodology	
		
1.1.1. Initial data exploration
		
1.1.2. Statistical model and forecast
		
1.1.3. Industry insights and validation
		
1.1.4. Scope
		
1.1.5. Definitions
		
1.1.6. Methodology & forecast parameters
	
	
	
1.2. Data Sources	
		
1.2.1. Secondary
		
1.2.2. Primary
	
	
Chapter 2. Executive Summary
	
2.1. Data warehousing industry 360 degree synopsis, 2014 - 2025
	
2.2. Business trends
	
2.3. Regional trends
	
2.4. Data type trends
	
2.5. Deployment model trends
	
2.6. Organization type trends
	
2.7. Offering trends
	
2.8. Application trends
Chapter 3. Data Warehousing Industry Insights
	
3.1. Introduction
	
3.2. Industry segmentation
	
3.3. Industry landscape, 2014 - 2025
	
3.4. Data warehousing evolution
	
3.5. Data warehousing architecture analysis
	
3.6. Data warehousing industry ecosystem analysis
	
3.7. Technology & innovation landscape	
		
3.7.1. Integration of Machine Learning (ML) with data warehousing
		
3.7.2. Technological advancements in Internet-of-Things (IoT)
		
3.7.3. Proliferation of cloud technology in data warehousing
	
	
	
3.8. Regulatory landscape	
		
3.8.1. Information Security Technology- Personal Information Security Specification GB/T 35273-2017
		
3.8.2. General Data Protection Regulation (GDPR), EU
		
3.8.3. The NIST Special Publication 800-144 - Guidelines on Security and Privacy in Public Cloud Computing, U.S.
		
3.8.4. The Health Insurance Portability and Accountability Act (HIPAA) of 1996
		
3.8.5. Secure India National Digital Communications Policy 2018 - Draft
		
3.8.6. Payment Card Industry Data Security Standard (PCI DSS)- version 3.2.1
	
	
	
3.9. Industry impact forces	
		
3.9.1. Growth drivers		
			
3.9.1.1. Rising need of data warehouses for disparate data storage
			
3.9.1.2. Growing demand of data mining for BI and data analytics
			
3.9.1.3. Increasing use of historical data for enhancing customer experience
			
3.9.1.4. Proliferation of cloud technology in data warehousing
		
		
		
3.9.2. Industry pitfalls & challenges		
			
3.9.2.1. Data rigidity and inefficient architecture
			
3.9.2.2. High deployment costs and IT complexity
			
3.9.2.3. Threat of data breaches and cyber attacks
		
		
	
	
	
3.10. Porter's analysis
	
3.11. PESTEL analysis
	
3.12. Growth potential analysis