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市場調查報告書

藥物巨量資料的展望:即時資料所利用的決策及技術創新的加速

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

出版商 Cutting Edge Information 商品編碼 296694
出版日期 內容資訊 英文 375 pages
商品交期: 最快1-2個工作天內
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藥物巨量資料的展望:即時資料所利用的決策及技術創新的加速 Pharmaceutical Big Data Insights: harnessing Real-Time Data to Drive Decision Making and Innovation
出版日期: 2014年01月01日 內容資訊: 英文 375 pages
簡介

在製藥產業,臨床開發和進入市場、醫生患者的行銷等廣泛的領域中,活用巨量資料的動向擴展開來。要活用巨量資料,需要能引出資料和分析技術最大限度價值的專門隊伍,也不可缺少評價產品的性能的配合措施。可是,若利用巨量資料,可提高在回顧性研究中進行的資料分析效率,也能進行利用醫療效果和患者報告結果,實際的資料等各種前瞻性巨量資料的研究。

本報告以75件以上巨量資料的研究企劃為焦點,提供企劃的預算和隊伍的規模,前瞻性研究和回顧性研究,市場資訊收集企劃的巨量資料利用的方法等分析,為您概述為以下內容。

摘要整理

  • 調查方法
  • 定義
  • 巨量資料:成功的5個原則

第1章 生命科學產業的新巨量資料策略

  • 導入正式體制前所需巨量資料相關願景的建立
  • 強化利用可取得資源的巨量資料團隊的影響力 的所
  • 巨量資料相關活動的多樣化支援各種目標的達成

第2章 前瞻性研究:巨量資料所利用的前瞻性研究概念分析

  • 巨量資料策略的引進改善前瞻性研究
  • 克服巨量資料所利用的前瞻性研究的障礙
  • 巨量資料前瞻性研究檢討
  • 前瞻性研究概要

第3章 由於回顧性研究:提高過去巨量資料分析的產品價值

  • 提高巨量資料策略所利用的回顧性研究的效率
  • 克服應用巨量資料策略的回顧性研究的課題
  • 巨量資料回顧性研究檢討
  • 回顧性研究概要

第4章 市場相關資訊:以視覺化競爭情形為目的巨量資料策略

  • 利用巨量資料策略的複雜市場上的改善產品性能的配合措施
  • 強化利用多樣巨量資料應用的市場資訊收集
  • 市場資訊收集概念的社群媒體頻道之利用
  • 市場資訊收集概念簡介

第5章 巨量資料的課題與趨勢

  • 第三方供應商參加巨量資料策略制定工作
  • 巨量資料策略導入成功背面的計劃
  • 克服各種課題並擴大巨量資料策略成果的配合措施
目錄
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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