製薬におけるビッグデータ - Strategic Intelligence

ビッグデータは製薬産業全体で広範囲に生成されています

国際製薬情勢では、患者登録、臨床試験、ウェアラブル技術などのさまざまなソースから膨大なデータが生成されます。このようなデータセットは非常に広大で、複雑かつ構造化されていないため、従来のビッグデータアナリティクスの手法では処理効率が低いです。

その結果、製薬産業では、新たな知見を得る目的で、膨大で複雑なデータセットを保護、保存、処理、分析、集約、統合できるビッグデータアナリティクス技術の革新が進んでいます。さらに、これらの技術は機械学習（ML）、AI、IoT、デジタルツインなどで実行することができます。

当レポートでは、世界の製薬産業について調査分析し、医療、マクロ経済、技術、規制の動向が製薬部門のビッグデータにどのような影響を与えるかに関する最新の見解と予測をはじめ、バリューチェーン全体の主要企業と将来のディスラプターに関する考察や、主要な医薬品と市場に関する考察などを提供しています。

目次

図表のリスト

表のリスト

• 参入企業
• テーマのブリーフィング
• 動向
• 産業分析
• バリューチェーン
• 企業
• 医薬品製造スコアカード
• 略語
• 参考文献
• 著者について
• テーマの調査手法

List of Tables

• Table 1: Healthcare trends in the big data space
• Table 2: Technology trends in the big data space
• Table 3: Macroeconomic trends in the big data space
• Table 4: Regulatory trends in the big data space
• Table 5: Examples of M&A in the big data and pharma spaces, 2022-24
• Table 6: Examples of strategic partnerships in the pharma big data space, 2022-24
• Table 7: Examples of large funding deals in the big data space since 2022
• Table 8: Drug target discovery big data analytical tools
• Table 9: VCT big data analytical tools
• Table 10: Precision medicine big data analytical tools
• Table 11: Digital twins big data analytical tools
• Table 12: Telemedicine big data analytical tools
• Table 13: Digital biomarkers big data analytical tools
• Table 14: Genomics integration within the big data space
• Table 15: Examples of leading big data vendors within the big data theme
• Table 16: Examples of specialist big data vendors in the big data theme
• Table 17: Examples of leading big data adopters in the big data theme
• Table 18: GlobalData reports
• Table 19: Target identification and validation
• Table 20: The time taken to identify novel drug targets.
• Table 21: The different platforms and libraries used for drug repurposing
• Table 22: The leading technology players within the AI in drug discovery theme and summarizes their competitive position
• Table 13: The specialist AI vendors in drug discovery and summarizes their competitive position
• Table 24: The leading adopters of AI in drug discovery and summarizes their competitive position
• Table 25: Abbreviations
• Table 26: GlobalData reports

List of Figures

• Figure 1: Who are the leading players in the big data pharma space?
• Figure 2: Pharma contributed 1.1% of the total global data and analytics market in 2022
• Figure 3: Pharma will contribute 1.1% of the total global data and analytics market in 2028
• Figure 4: The total global data and analytics market for pharma was valued at $1.1B in 2022, led by China
• Figure 5: China will lead the global data and analytics market in pharma in 2028
• Figure 6: Confidence levels in big data's potential to transform the pharmaceutical industry
• Figure 7: Key pharmaceutical value chain components poised to benefit most from big data
• Figure 8: Big data trends in GlobalData's social media analytics database, September 2023 - September 2024
• Figure 9: Examples of top posts related to big data, 2023-24
• Figure 10: The big data value chain in pharma
• Figure 11: Who does what in the drug manufacturing space?
• Figure 12: Our thematic screen ranks companies based on overall leadership in the 10 themes that matter most to their industry, generating a leading indicator of future performance
• Figure 13: Our valuation screen ranks our universe of companies within a sector based on selected valuation metrics
• Figure 14: Our risk screen ranks companies within a particular sector based on overall investment risk

Big data is generated extensively across pharma

The international pharmaceutical landscape generates vast amounts of data from a variety of sources, such as patient registries, clinical trials, wearable technologies, and more. Such datasets are extremely vast, complex, and unstructured, rendering traditional big data analytical methodologies inefficient for processing.

As a result, organizations within the pharmaceutical industry are innovating big data analytical technologies that can secure, store, process, analyze, aggregate, and integrate vast and complex datasets for the purpose of acquiring novel insights. Furthermore, these technologies can be implemented with machine learning (ML), artificial intelligence (AI), Internet of Things (IoT), digital twins, and more.

This report consolidates GlobalData's latest thinking and forecasts around how the healthcare, macroeconomic, technology, and regulatory trends will impact the big data in pharma space, as well as providing insights into the leading players and future disruptors across the value chain, and providing insights into key drugs and markets from GlobalData's Pharma Intelligence Center. Additionally, this report is designed to provide strategic planners, competitive intelligence professionals and key stakeholders in the pharmaceutical industry a clear view of the opportunities and risks over the foreseeable future for big data.

Scope

• A dedicated report examining the pivotal healthcare, technological, macroeconomic, and regulatory trends shaping the big data in pharma landscape. This report also provides an in-depth analysis of how these trends are poised to either accelerate progress or create obstacles for the growth of the big data market.

Reasons to Buy

• Understand the key trends accelerating or hindering the big data in pharma space.
• See market forecasts for different therapies within big data up to 2028.
• Understand recent and influential developments in big data.
• Review of leaders and disruptors across the big data value chain.

Table of Contents

Table of Contents

List of Figures

List of Tables

• Players
• Thematic Briefing
• Trends
• Industry Analysis
• Value Chain
• Companies
• Drug Manufacturing Scorecard
• Abbreviations
• Further Reading
• About the Authors
• Our Thematic Research Methodology