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市場調査レポート

薬剤ビッグデータの展望:リアルタイムデータを利用した意思決定と技術革新の加速

Pharmaceutical Big Data Insights: harnessing Real-Time Data to Drive Decision Making and Innovation

発行 Cutting Edge Information 商品コード 296694
出版日 ページ情報 英文 375 pages
納期: 即日から翌営業日
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本日の銀行送金レート: 1USD=106.71円で換算しております。
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薬剤ビッグデータの展望:リアルタイムデータを利用した意思決定と技術革新の加速 Pharmaceutical Big Data Insights: harnessing Real-Time Data to Drive Decision Making and Innovation
出版日: 2014年01月01日 ページ情報: 英文 375 pages
概要

製薬業界では、臨床開発や市場アクセス、医師と患者へのマーケティングなど、幅広い分野でビッグデータを活用する動きが広がっています。ビッグデータを活用するには、データと解析技術の価値を最大限引き出すことができる専門のチームが必要であり、ソーシャルメディアを利用して追加情報を収集したり、製品の性能を評価するといった取り組みも欠かせません。しかし、ビッグデータを利用すれば、レトロスペクティブ研究のなかで行われるデータ解析の効率を高めることが可能であり、医療効果や患者報告結果、実際のデータなどを利用してさまざまなプロスペクティブビッグデータ研究を行うこともできます。

当レポートは、ビッグデータを活用した75件以上の研究プロジェクトに注目し、プロジェクトの予算やチームの規模、プロスペクティブ研究やレトロスペクティブ研究、市場情報収集プロジェクトでのビッグデータ利用のあり方などを分析したもので、概略下記の構成でお届けします。

エグゼクティブサマリー

  • 調査方法
  • 定義
  • ビッグデータ:成功のための5原則

第1章 生命科学業界における新たなビッグデータ戦略

  • 正式な体制を導入する前に必要となるビッグデータに関するビジョンの構築
  • 入手可能なリソースを利用したビッグデータチームの影響力強化
  • ビッグデータ関連活動の多様化で各種目標の達成を支援

第2章 プロスペクティブ研究:ビッグデータを利用した前向き研究構想の分析

  • ビッグデータ戦略の導入によるプロスペクティブ研究の改善
  • ビッグデータを利用したプロスペクティブ研究の障害克服
  • ビッグデータプロスペクティブ研究の検討
  • プロスペクティブ研究の概要

第3章 レトロスペクティブ研究:過去のビッグデータ分析による製品の価値向上

  • ビッグデータ戦略を利用したレトロスペクティブ研究の効率向上
  • ビッグデータ戦略を応用したレトロスペクティブ研究の課題克服
  • ビッグデータレトロスペクティブ研究の検討
  • レトロスペクティブ研究の概要

第4章 市場に関する情報:競合状況の視覚化を目的としたビッグデータ戦略

  • ビッグデータ戦略を利用した複雑な市場での製品性能向上に向けた取り組み
  • 多様なビッグデータアプリケーションを利用した市場情報収集の強化
  • 市場情報収集構想におけるソーシャルメディアチャンネルの利用
  • 市場情報収集構想のプロファイル

第5章 ビッグデータの課題とトレンド

  • ビッグデータ戦略策定作業へのサードパーティベンダーの参加
  • ビッグデータ戦略を成功裏に導入するための計画
  • 各種の課題を克服しビッグデータ戦略の成果を拡大するための取り組み
目次
Product Code: Ph195

THE BOTTOM LINE:

The pharmaceutical industry is harnessing Big Data to leap forward in areas as wide-ranging as clinical development, market access, and physician and patient marketing.

The benchmarks within this report will help companies build Big Data strategy and infrastructure. Based on more than 75 Big Data-driven studies, findings focus on project budgets, team sizes and specific metrics for prospective studies, retrospective studies and market intelligence initiatives. Use the insights and metrics as your go-to guide for building a successful Big Data team - one positioned to support varied groups, including medical affairs, business development and market access - and to recognize and overcome critical Big Data challenges.

KEY QUESTIONS

ANSWERING CRITICAL QUESTIONS FOR OUR CLIENTS

The data and analysis contained in this report will help you answer many questions as your company incorporates Big Data into its strategic decisions. Here are some of the key questions answered in this benchmarking study:

KEY QUESTIONS ANSWERED IN THIS REPORT

  • How can Big Data improve initiatives across departments?
  • How can companies measure ROI for Big Data initiatives?
  • Why should companies implement centralized/dedicated teams?
  • How should companies allocate resources to Big Data teams and studies?
  • Which functions are leading contributors to Big Data strategies?
  • Why should companies involve multiple functions in Big Data activities?
  • What information can companies gather from social media?
  • What challenges do companies encounter in using social media to collect and analyze Big Data?
  • Which tasks are better suited for vendor expertise?

KEY FINDINGS

CRITICAL FINDINGS FOR MARKET ACCESS EXECUTIVES

Cutting Edge Information analysts synthesized the following principles from the full breadth and depth of this project's research. The principles are signposts to help improve your company's Big Data strategies. While these points are not inclusive of all elements in this report, they emphasize its central and most critical concepts.

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  • 1. ADD DEDICATED TEAMS TO MAXIMIZE VALUE OF BIG DATA/ANALYTICS.
  • 2. IMPLEMENT SOCIAL MEDIA INITIATIVES TO SUPPLEMENT COMPETITIVE INTELLIGENCE AND ASSESS PRODUCT PERFORMANCE.


  • 3. HARNESS BIG DATA TO IMPROVE EFFICIENCY OF DATA ANALYSIS DURING RETROSPECTIVE STUDIES.
  • 4. LEVERAGE HEALTH OUTCOMES, PATIENT-REPORTED OUTCOMES AND REAL-WORLD DATA TO DRIVE A BROAD RANGE OF PROSPECTIVE BIG DATA STRATEGIES.
  • 5. BUILD TEAMS AROUND ANALYSTS WITH INDUSTRY AND ANALYTICS EXPERTISE.

KEY METRICS

CHAPTER 1: EMERGING BIG DATA STRATEGIES IN THE LIFE SCIENCES INDUSTRY

MAJOR TAKEAWAYS

  • Structure a diversely skilled team to support multiple groups and objectives, including medical affairs, business development and market access.
  • Leverage centralized databases to store, filter and promote Big Data accessibility among internal functions.
  • Weigh levels of expertise when deciding to centralize or decentralize Big Data.
  • Prepare for a time-consuming transition while existing infrastructure reorganizes for a dedicated Big Data group.
  • Develop a Big Data vision before implementing a formalized structure.
  • Harness resources to maximize Big Data team impact.

CHAPTER DATA

  • Big Data team budgets, staffing and goals
  • Percentage of companies with dedicated Big Data teams
  • Breakdown of centralized versus decentralized teams
  • Companies planning to build a dedicated Big Data team (including implementation time frame)
  • Functions involved in Big Data, including subfunctions for medical affairs and market access
  • Types of prospective, retrospective or market intelligence Big Data initiatives present companywide

CHAPTER 2: PROSPECTIVE STUDIES: USING BIG DATA TO EXAMINE FORWARD-LOOKING INITIATIVES

MAJOR TAKEAWAYS

  • Establish plans to maintain timelines and funding - two chief challenges for prospective Big Data studies.
  • Prepare teams to handle studies that vary widely in size, scope and goals.
  • Focus on health outcomes, PROs and other real-world data to increase chances for success.
  • Address common, key obstacles in working with Big Data around prospective research.

CHAPTER DATA

  • Resource support for prospective studies
  • Functions conducting prospective studies
  • Stage at which teams conduct prospective studies
  • Ratings of data sources in overall prospective study use
  • Ratings of prospective study challenges
  • Improvement potential ratings for specific Big Data strategy areas

CHAPTER 3: RETROSPECTIVE STUDIES: INCREASING PRODUCT VALUE THROUGH HISTORIC BIG DATA ANALYSIS

MAJOR TAKEAWAYS

  • Correct for bias and develop study outcomes in retrospective studies.
  • Acknowledge Big Data's issues in the data analysis stage of retrospective studies.
  • Involve many functional groups, especially HEOR, Medical Affairs and Market Access.
  • Consider waiting for claims data to become available before conducting retrospective
  • Plan for incorporation of EHR data in future studies.

CHAPTER DATA

  • Resource support for retrospective studies
  • Functions conducting retrospective studies
  • Stage at which teams conduct studies
  • Ratings of data sources in overall retrospective study use
  • Ratings of retrospective study challenges
  • Improvement potential across specific Big Data strategy areas
  • Duration of retrospective Big Data studies

CHAPTER 4: MARKET INTELLIGENCE: CHANNELING BIG DATA STRATEGIES TO VISUALIZE THE COMPETITIVE LANDSCAPE

MAJOR TAKEAWAYS

  • Zero in on key types of data to feed market intel initiatives.
  • Prepare for increased use of social media in Big Data undertakings.
  • Inform competitive intelligence by using social media tools to collect physician and patient community information.
  • Consider gamification to support Big Data efforts.
  • Employ commercial and business development teams to drive Big Data market intelligence efforts.

CHAPTER DATA

  • Budgets, staffing and departmental responsibility for Big Data-linked market intelligence initiatives
  • Market intelligence Big Data applications
  • Big Data application in disease and patient population characterization, product development, marketed product performance, and targeting of products/services
  • Big Data strategies to guide company social media and digital marketing usage
  • Social media tools, platforms, utility rankings and challenges

CHAPTER 5: BIG DATA CHALLENGES AND TRENDS

MAJOR TAKEAWAYS

  • Understand how uncommon ROI tracking remains in Big Data initiatives.
  • Consider pilot programs, which are uncommon but useful, to gauge Big Data success.
  • Balance plentiful vendor experience against evolution of internal capabilities.
  • Rely on vendors for data collection and standardization, then use internal resources to drive analysis and decision-making.
  • Seek analysts with statistical and analytics experience as well as industry knowledge.

CHAPTER DATA

  • Prevalence and effectiveness of Big Data pilot programs
  • Percentage of companies measuring ROI of Big Data initiatives
  • Percentage of initiative budget outsourced for data collection, storage and analysis
  • Preparations for Big Data/analytics activities
  • Ratings of Big Data challenges

Table of Contents

ES EXECUTIVE SUMMARY

  • 18 Study Methodology
  • 19 Study Definitions
  • 21 Big Data: Five Principles For Success

CH1 EMERGING BIG DATA STRATEGIES IN THE LIFE SCIENCES INDUSTRY

  • 34 Develop A Big Data Vision Before Implementing A Formalized Structure
  • 44 Harness Available Resources To Maximize Big Data Team Impact
  • 53 Diversify Big Data Activities To Support Multiple Company Objectives

CH2 PROSPECTIVE STUDIES: USING BIG DATA TO EXAMINE FORWARD-LOOKING INITIATIVES

  • 75 Embracing Big Data Strategies To Improve Prospective Studies
  • 91 Overcoming Obstacles Working With Big Data In Prospective Studies
  • 101 Exploring Big Data Prospective Studies
  • 116 Prospective Studies: Profiles

CH3 RETROSPECTIVE STUDIES: INCREASING PRODUCT VALUE THROUGH HISTORIC BIG DATA ANALYSIS

  • 136 Leveraging Big Data Strategies To Improve Retrospective Study Efficiency
  • 155 Apply Big Data Strategies To Overcome Retrospective Study Challenges
  • 166 Exploring Specific Big Data Retrospective Studies
  • 188 Retrospective Studies: Profiles

CH4 MARKET INTELLIGENCE: CHANNELING BIG DATA STRATEGIES TO VISUALIZE THE COMPETITIVE LANDSCAPE

  • 211 Using Big Data Strategies To Drive Product Performance In A Complex Market
  • 227 Enhance Market Intelligence Through A Broad Range Of Big Data Applications
  • 254 Leverage Social Media Channels In Market Intelligence Initiatives
  • 289 Market Intelligence Initiatives: Profiles

CH5 BIG DATA CHALLENGES AND TRENDS

  • 317 Involve Third-Party Vendors To Develop Big Data Strategies
  • 327 Plan For Successful Big Data Strategy Implementation
  • 339 Overcome Challenges To Accelerate Big Data Success
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