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
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.
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?
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.
- 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
- 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.
CHAPTER 1: EMERGING BIG DATA STRATEGIES IN THE LIFE SCIENCES INDUSTRY
- 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
- 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.
- 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
- 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
- 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
- Correct for bias and develop study outcomes in retrospective studies.
- Acknowledge Big Data's issues in the data analysis stage of retrospective
- Involve many functional groups, especially HEOR, Medical Affairs and
- Consider waiting for claims data to become available before conducting
- Plan for incorporation of EHR data in future studies.
- 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
- 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
- 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
- Big Data strategies to guide company social media and digital marketing
- Social media tools, platforms, utility rankings and challenges
CHAPTER 5: BIG DATA CHALLENGES AND TRENDS
- Understand how uncommon ROI tracking remains in Big Data initiatives.
- Consider pilot programs, which are uncommon but useful, to gauge Big Data
- Balance plentiful vendor experience against evolution of internal
- 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
- 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
- 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
- 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
- 227 Enhance Market Intelligence Through A Broad Range Of Big Data
- 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