Data Analytics in Medical Devices - Thematic Intelligence
Summary
Data analytics extracts value from big data
Human activity generates a vast amount of data, from databases containing information about citizens to user-generated content on social media platforms and sensor data generated by smartphones and industrial machinery. Many industry forecasts expect over 175 zettabytes of data to be generated by 2025. A zettabyte is as much information as there are grains of sand on all the world’s beaches. We are drowning in data, and making sense of so much information is becoming more difficult. Data analytics tools can help us convert raw data into valuable insights and actionable knowledge. GlobalData estimates the total data analytics market will be worth $188.8 billion in 2027, implying a 13% compound annual growth rate (CAGR) between 2022 and 2027.
AI is revolutionizing data analytics
Data analytics is a relatively mature market, yet significant innovation has recently emerged. Prescriptive analytics is the most advanced type, aiming to tell organizations what to do next rather than just describing what happened and why. Machine learning (ML) techniques can now provide data-driven recommendations by parsing large amounts of data and assessing “what if” scenarios. The traditional data analytics vendors such as SAS, IBM, Oracle, and SAP, which evolved from descriptive analytics roots, are being disrupted by AI-native vendors, such as C3.ai, CognitiveScale, and H2O.ai, which aim to help companies automate operational decision-making using ML.
Data analytics has many applications in medical devices
Data analytics has many uses in the medical devices industry, including in mHealth apps and other electronic medical devices. It can be used for analyzing data from devices to understand trends in the healthcare industry, and can also be used for quality control purposes and reduce human error. Many medical devices are using insights from data analysis to help shape business decisions, too.
Scope
This report is a thematic brief, which identifies those companies most likely to succeed in a world filled with disruptive threats. Inside, we predict how each theme will evolve and identify the leading and disrupting companies.
The report covers the data analytics theme.
Reasons to Buy
GlobalData’s thematic research ecosystem is a single, integrated global research platform that provides an easy-to-use framework for tracking all themes across all companies in all sectors. It has a proven track record of identifying the important themes early, enabling companies to make the right investments ahead of the competition, and secure that all-important competitive advantage.
Develop and design your corporate strategies through an in-house expert analysis of data analytics by understanding the primary ways in which this theme is impacting the healthcare industry.
Stay up to date on the industry’s major players and where they sit in the value chain.
Identify emerging industry trends to gain a competitive advantage.
Executive Summary
Players
Technology Briefing
The four primary types of data analytics
Data classifications
The data analytics pipeline
Enterprise data architecture
Collect
Store
Process and analyze
Visualize
Action
Governance, privacy, and security
It’s not just what you implement; it’s how you implement it
Trends
Technology trends
Macroeconomic trends
Regulatory trends
Industry Analysis
Market size and growth forecasts
Data analytics in medical devices
Timeline
Signals
M&A trends
Venture financing trends
Patent trends
Company filing trends
Hiring trends
Value Chain
Hardware
Semiconductors
Cameras
Sensors and lasers
Servers
Storage devices
Networking equipment
Edge equipment
Data management
Data integration
Data aggregation
Data processing
Data storage
Data validation
Data governance and security
Applications
Descriptive analytics
Diagnostic analytics
Predictive analytics
Prescriptive analytics
Delivery
Hardware appliance
Licensed software
Analytics as a service
Companies
Public companies
Private companies
Sector Scorecard
Medical devices sector scorecard
Who’s who
Thematic screen
Valuation screen
Risk screen
Glossary
Further Reading
GlobalData reports
Our Thematic Research Methodology
About GlobalData
Contact Us
List of Tables
Table 1: Technology Briefing
Table 2: Technology trends
Table 3: Macroeconomic trends
Table 4: Regulatory trends
Table 5: Key M&A transactions associated with the data analytics theme since January 2022
Table 6: Key venture financing deals associated with the data analytics theme since July 2022
Table 7: Public companies
Table 8: Private companies
Table 9 Glossary:
Table 10: GlobalData reports
List of Figures
Figure 1: Who are the leading players in the data analytics theme, and where do they sit in the value chain?
Figure 2: Technology Briefing
Figure 3: Descriptive and diagnostic describe the past; predictive and prescriptive are about the future.
Figure 4: The many classes of data
Figure 5: The data analytics pipeline includes collecting, storing, processing, analyzing, visualizing, and actioning.
Figure 6: Data analytics technology stack
Figure 7: Data sources are infinite.
Figure 8: Lakehouses aim to combine the benefits of lakes and warehouses with none of the drawbacks
Figure 9: BI dashboards help run businesses
Figure 10: The artificial intelligence value chain
Figure 11: Data science uses statistical techniques, ML, and huge datasets to identify KPIs and make predictions
Figure 12: Generative AI is integrated with data analytics tools
Figure 13: The core principles of data governance
Figure 14: The global data analytics market will be worth $189 billion by 2027
Figure 15: Asia-Pacific and North America are the leading data analytics markets
Figure 16: The data analytics story
Figure 17: M&A deal volume and value were highest in 2021
Figure 18: Data analytics venture financing deal volume peaked in 2020
Figure 19: Patent activity peaked in April 2022
Figure 20: Data analytics is a key part of digital health tools, leading to many mentions in corporate filings
Figure 21: Data analytics-related hiring peaked in March 2022
Figure 22: The data analytics value chain
Figure 23: The data analytics value chain - Hardware – semiconductors
Figure 24: The data analytics value chain - Hardware – cameras
Figure 25: The data analytics value chain - Hardware – sensors and lasers
Figure 26: The data analytics value chain - Hardware – servers
Figure 27: The data analytics value chain - Hardware – storage devices
Figure 28: The data analytics value chain - Hardware – networking equipment
Figure 29: The data analytics value chain - Hardware – edge equipment
Figure 30: The data analytics value chain - Data management – data integration
Figure 31: The data analytics value chain - Data management – data aggregation
Figure 32: The data analytics value chain - Data management – data processing
Figure 33: The data analytics value chain - Data management – data storage
Figure 34: The data analytics value chain - Data management – data validation
Figure 35: The data analytics value chain - Data management – data governance and security
Figure 36: The data analytics value chain - Applications
Figure 37: The AI value chain - Delivery
Figure 38: Who does what in the medical devices space?
Figure 39: Thematic screen
Figure 40: Valuation screen
Figure 41: Risk screen
Figure 42: Our five-step approach for generating a sector scorecard