Pharmaceutical Big Data Insights

$3,495.00

This report offers benchmarks designed to help companies build Big Data strategy and infrastructure. As companies uncover the benefits of harnessing multiple, deep data sources, they allocate resources to enhance and develop their Big Data teams. Based on more than 75 Big Data-driven studies, this report focuses on project budgets, team sizes and specific metrics for prospective studies, retrospective studies and market intelligence initiatives. Use its findings and metrics as your go-to guide for building a diverse 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

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Report Details

Publication Date: Jan 2014
Pages: 357
Chapters: 5
Metrics: 500+
Charts/Graphics: 287

Top Reasons to Buy This Pharmaceutical Big Data Report

Overcome social media challenges to collect data: Social media poses big challenges to drug and device manufacturers, especially when it comes to reporting unsolicited adverse events.  But life science companies have developed methods to better use social media to address those challenges and gather pharmaceutical big data at the same time.  Learn how top companies use social media channels to gather data, such as competitor product usage, patient and physician sentiment and adverse event information.  Implement the best practices from this report to cultivate social media channels as a means to examine target populations’ behaviors, reactions and expectations.

Establish dedicated teams to capitalize on key initiatives: Sixty-three percent of surveyed companies are planning to build dedicated teams.  How they construct those teams and the level of resources they provide to them will determine much of their success.  Use this report to understand why it’s important to build a diverse team that involves expertise from different functional areas.  Consult the resource allocation benchmarks in this report to staff and fund your teams, as well as individual prospective and retrospective studies.

Expand your strategies to increase study efficiency: This report outlines how companies are utilizing Big Data’s efficiency to shorten timelines and decrease budgets of prospective and retrospective studies.  Medical Affairs, Clinical Development and HEOR teams will benefit by the strategies outlined in this report highlighting which data tools to focus on and how to achieve more accurate study results.  Utilizing these strategies during these projects will lead to cost-effective timelines, more accurate study results and an overall stronger drug value proposition.

You may also be interested in our market access library as well as individual market access research reports.

Excerpt from Pharmaceutical Big Data Insights

Companies have initially applied Big Data to streamline three major types of initiatives: prospective studies, retrospective studies and market intelligence initiatives. Companies are eager to enhance product value come time for drug reimbursement discussions in today’s strict payer environment. Harnessing large datasets to enhance prospective and retrospective studies dramatically improves these pre- and postlaunch tasks. Additionally, Big Data’s efficiency offers the potential for shortened timelines and decreased budgets. Post-launch, companies are eager to capitalize on the burgeoning amount of data available via mobile applications and social media. Big Data-driven market intelligence initiatives will help companies communicate more efficiently with physician and patient populations. This communication delivers better products that more precisely meet customer needs.

An executive interviewed in this study pinpoints the potential of Big Data strategies in improving pharmaceutical and device initiatives: “Implementing Big Data strategies leads to smarter business decisions that will impact the cost of care. That’s what we’re all trying to do.” But the implementation of Big Data strategies in the healthcare industry still faces many challenges: Data security concerns threaten widespread data sharing and collaboration; technological advances make data standardization time-consuming and cumbersome; and best-practice data analysis strategies are still under development. However, adopting these strategies and dedicating resources specifically to understanding and leveraging Big Data could mean increased efficiency and improved outcomes industry wide.

Table of Contents

14           Executive Summary

18           Study Methodology

19           Study Definitions

21           Big Data: Five Principles For Success

33           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

73           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

135         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

210         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

316         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

Charts and Graphics

14           Executive Summary

21           Big Data: Five Principles for Success

22           Figure E.1: Companies Planning to Build a Dedicated Big Data/Analytics Team: All Companies

24           Figure E.2: Ratings Comparing Big Data Utility from Social Media Channels for Specific Activities: Consultant and Pharma

25           Figure E.3: Improvement Potential Ratings Across Specific Big Data Strategy Areas: Top 10

26           Figure E.4: Improvement Potential Ratings Across Specific Big Data Strategy Areas: Consultant

28           Figure E.5: Ratings of Data Sources in Overall Prospective Study Use: All Pharma

29           Figure E.6: Ratings of Data Sources in Overall Prospective Study Use: Device

30           Figure E.7: Ratings of Data Sources in Overall Prospective Study Use: Consultant

31           Figure E.8: Ratings of Data Sources in Overall Prospective Study Use: All Companies

33           Emerging Big Data Strategies In The Life Sciences Industry

34           Figure 1.1: Percentage of Companies with a Dedicated Big Data Team: Top 10

34        Develop A Big Data Vision Before Implementing A Formalized Structure

35           Figure 1.2: Percentage of Companies with a Dedicated Big Data Team: Top 50

35           Figure 1.3: Percentage of Companies with a Dedicated Big Data Team: Small Pharma

36           Figure 1.4: Percentage of Companies with a Dedicated Big Data Team: Device

36           Figure 1.5: Percentage of Companies with a Dedicated Big Data Team: Affiliate

37           Figure 1.6: Percentage of Companies with a Dedicated Big Data Team: Consultant

38           Figure 1.7: Companies with Dedicated Big Data Teams, by Company Type

39           Figure 1.8: Breakdown of Companies with Centralized Big Data Teams

39           Figure 1.9: Percentage of Centralized versus Decentralized Big Data Teams, by Company Type

42           Figure 1.10: Companies Planning to Build a Dedicated Big Data Team: All Companies

43           Figure 1.11: Time Frame in Which Companies Plan to Implement Dedicated Big Data Teams: All Companies

44        Harness Available Resources To Maximize Big Data Team Impact

45           Figure 1.12: Age of Big Data Teams: All Companies

45           Figure 1.13: Age of Big Data Teams: Top 10

46           Figure 1.14: Age of Big Data Teams: Top 50

47           Figure 1.15: Age of Big Data Teams: Small Pharma

48           Figure 1.16: Age of Big Data Teams: Affiliate

48           Figure 1.17: Age of Big Data Teams: Device

49           Figure 1.18: Age of Big Data Teams: Consultant

50           Figure 1.19: Big Data Team Staffing: All Companies

52           Figure 1.20: Big Data Team Budgets: All Companies

53        Diversify Big Data Activities To Support Multiple Company Objectives

54           Figure 1.21: Functions Involved in Big Data: Top 10

55           Figure 1.22: Functions Involved in Big Data: Top 50

55           Figure 1.23: Functions Involved in Big Data: Small Pharma

56           Figure 1.24: Functions Involved in Big Data: Affiliate

56           Figure 1.25: Functions Involved in Big Data: Device

57           Figure 1.26: Functions Involved in Big Data: Consultant

58           Figure 1.27: Functions Involved in Big Data: All Companies

59           Figure 1.28: Medical Affairs Subfunctions Involved in Big Data Initiatives: All Companies

61           Figure 1.29: Market Access Subfunctions Involved in Big Data Initiatives: All Companies

63           Figure 1.30: Goals of Big Data Usage: Top 10

64           Figure 1.31: Goals of Big Data Usage: Top 50

65           Figure 1.32: Goals of Big Data Usage: Small Pharma

65           Figure 1.33: Goals of Big Data Usage: Affiliate

66           Figure 1.34: Goals of Big Data Usage: Device

67           Figure 1.35: Goals of Big Data Usage: Consultant

67           Figure 1.36: Goals of Big Data Usage: All Companies

68           Figure 1.37: Types of Big Data Initiatives Present Companywide: Top 10

70           Figure 1.38: Types of Big Data Initiatives Present Companywide: Top 50

70           Figure 1.39: Types of Big Data Initiatives Present Companywide: Small Pharma

71           Figure 1.40: Types of Big Data Initiatives Present Companywide: Affiliate

71           Figure 1.41: Types of Big Data Initiatives Present Companywide: Device

72           Figure 1.42: Types of Big Data Initiatives Present Companywide: Consultant

72           Figure 1.43: Types of Big Data Initiatives Present Companywide: All Companies

73        Prospective Studies: Using Big Data To Examine Forward-Looking Initiatives

75        Embracing Big Data Strategies To Improve Prospective Studies

76           Figure 2.1: Functions Conducting Prospective Studies: All Pharma

77           Figure 2.2: Functions Conducting Prospective Studies: Device

78           Figure 2.3: Functions Conducting Prospective Studies: Consultant

79           Figure 2.4: Functions Conducting Prospective Studies: All Companies

81           Figure 2.5: Average Stage at Which Teams Conduct Prospective Studies: All Pharma

82           Figure 2.6: Average Stage at Which Teams Conduct Prospective Studies: Consultant

83           Figure 2.7: Average Stage at Which Teams Conduct Prospective Studies: All Companies

85           Figure 2.8: Ratings of Data Sources in Overall Prospective Study Use: Top 50

86           Figure 2.9: Ratings of Data Sources in Overall Prospective Study Use: Small Pharma

87           Figure 2.10: Ratings of Data Sources in Overall Prospective Study Use: All Pharma

88           Figure 2.11: Ratings of Data Sources in Overall Prospective Study Use: Device

89           Figure 2.12: Ratings of Data Sources in Overall Prospective Study Use: Consultant

90           Figure 2.13: Ratings of Data Sources in Overall Prospective Study Use: All Companies

91        Overcoming Obstacles Working With Big Data In Prospective Studies

92           Figure 2.14: Ratings of Prospective Study Challenges: Top 50

93           Figure 2.15: Ratings of Prospective Study Challenges: Small Pharma

94           Figure 2.16: Ratings of Prospective Study Challenges: All Pharma

95           Figure 2.17: Ratings of Prospective Study Challenges: Consultant

96           Figure 2.18: Ratings of Prospective Study Challenges: All Companies

98           Figure 2.19: Improvement Potential Ratings for Specific Big Data Strategy Areas: All Pharma

99           Figure 2.20: Improvement Potential Ratings for Specific Big Data Strategy Areas: Consultant

100         Figure 2.21: Improvement Potential Ratings for Specific Big Data Strategy Areas: All Companies

101      Exploring Big Data Prospective Studies

102         Figure 2.22: Prospective Study Duration: All Companies

103         Figure 2.23: Prospective Study Staffing: All Companies

104         Figure 2.24: Prospective Study Budget: All Companies

105         Figure 2.25: Percentage of Prospective Study Budget Outsourced: All Companies

106         Figure 2.26: Data Sources Used in Prospective Studies: All Pharma

107         Figure 2.27: Data Sources Used in Prospective Studies: Consultant

108         Figure 2.28: Data Sources Used in Prospective Studies: All Companies

110         Figure 2.29: Prospective Study Resource Allocation: All Pharma and Device

111         Figure 2.30: Prospective Study Resource Allocation: Consultant

113         Figure 2.31: Study Goals Big Data Strategies Will Help Achieve: All Pharma

114         Figure 2.32: Study Goals Big Data Strategies Will Help Achieve: Consultant

115         Figure 2.33: Study Goals Big Data Strategies Will Help Achieve: All Companies

116      Prospective Studies: Profiles

117         Figure 2.34:  Prospective Study 1: Companywide Strategy and Big Data Impact

118         Figure 2.35:  Prospective Study 1: Highlights and Data Use

119         Figure 2.36:  Prospective Study 1: Resource Allocation and Challenges

120         Figure 2.37:  Prospective Study 2: Companywide Strategy and Big Data Impact

121         Figure 2.38:  Prospective Study 2: Highlights and Data Use

122         Figure 2.39:  Prospective Study 2: Resource Allocation and Challenges

123         Figure 2.40:  Prospective Study 3: Companywide Strategy and Big Data Impact

124         Figure 2.41:  Prospective Study 3: Highlights and Data Use

125         Figure 2.42:  Prospective Study 3: Resource Allocation and Challenges

126         Figure 2.43:  Prospective Study 4: Companywide Strategy and Big Data Impact

127         Figure 2.44:  Prospective Study 4: Highlights and Data Use

128         Figure 2.45:  Prospective Study 4: Resource Allocation and Challenges

129         Figure 2.46:  Prospective Study 5: Companywide Strategy and Big Data Impact

130         Figure 2.47:  Prospective Study 5: Highlights and Data Use

131         Figure 2.48:  Prospective Study 5: Resource Allocation and Challenges

132         Figure 2.49:  Prospective Study 6: Companywide Strategy and Big Data Impact

133         Figure 2.50:  Prospective Study 6: Highlights and Data Use

134         Figure 2.51:  Prospective Study 6: Resource Allocation and Challenges

135      Retrospective Studies: Increasing Product Value Through Historic Big Data Analysis

136      Leveraging Big Data Strategies To Improve Retrospective Study Efficiency

137         Figure 3.1: Functions Conducting Retrospective Studies: Top 10

138         Figure 3.2: Functions Conducting Retrospective Studies: Top 50

139         Figure 3.3: Functions Conducting Retrospective Studies: Small Pharma

140         Figure 3.4: Functions Conducting Retrospective Studies: All Pharma

141         Figure 3.5: Functions Conducting Retrospective Studies: Device

142         Figure 3.6: Functions Conducting Retrospective Studies: Consultant

143         Figure 3.7: Functions Conducting Retrospective Studies: All Companies

145         Figure 3.8: Average Stage at Which Teams Conduct Retrospective Studies: All Pharma

146         Figure 3.9: Average Stage at Which Teams Conduct Retrospective Studies: All Pharma

147         Figure 3.10: Average Stage at Which Teams Conduct Retrospective Studies: Device

148         Figure 3.11: Average Stage at Which Teams Conduct Retrospective Studies: Consultant

149         Figure 3.12: Average Stage at Which Teams Conduct Retrospective Studies: All Companies

150         Figure 3.13: Ratings of Data Sources in Overall Retrospective Study Use: Top 10

151         Figure 3.14: Ratings of Data Sources in Overall Retrospective Study Use: All Pharma

152         Figure 3.15: Ratings of Data Sources in Overall Retrospective Study Use: Device

153         Figure 3.16: Ratings of Data Sources in Overall Retrospective Study Use: Consultant

154         Figure 3.17: Ratings of Data Sources in Overall Retrospective Study Use: All Companies

155      Apply Big Data Strategies To Overcome Retrospective Study Challenges

156         Figure 3.18: Ratings of Retrospective Study Challenges: Top 10

157         Figure 3.19: Ratings of Retrospective Study Challenges: All Pharma

158         Figure 3.20: Ratings of Retrospective Study Challenges: Device

159         Figure 3.21: Ratings of Retrospective Study Challenges: Consultant

160         Figure 3.22: Ratings of Retrospective Study Challenges: All Companies

161         Figure 3.23: Improvement Potential Ratings Across Specific Big Data Strategy Areas: Top 10

162         Figure 3.24: Improvement Potential Ratings Across Specific Big Data Strategy Areas: All Pharma

163         Figure 3.25: Improvement Potential Ratings Across Specific Big Data Strategy Areas: Device

164         Figure 3.26: Improvement Potential Ratings Across Specific Big Data Strategy Areas: Consultant

165         Figure 3.27: Improvement Potential Ratings Across Specific Big Data Strategy Areas: All Companies

166      Exploring Specific Big Data Retrospective Studies

167         Figure 3.28: Duration of Retrospective Big Data Studies: All Companies

168         Figure 3.29: In-House Staffing for Retrospective Big Data Studies: All Companies

169         Figure 3.30: Budget for Retrospective Big Data Studies: All Companies

170         Figure 3.31: Retrospective Study Duration Versus Total Study Budget: All Companies

171         Figure 3.32: Percentage of Budget Outsourced for Retrospective Big Data Studies: All Companies

172         Figure 3.33: Retrospective Study Duration Versus Percentage of Budget Outsourced: All Companies

173         Figure 3.34: Percentage of Companies Using Specific Data Sources: All Pharma

174         Figure 3.35: Percentage of Companies Using Specific Data Sources: All Companies

175         Figure 3.36: Percentage of Time Dedicated to Specific Stages of Retrospective Studies: All Pharma

176         Figure 3.37: Percentage of Time Dedicated to Specific Stages of Retrospective Studies: Device

177         Figure 3.38: Percentage of Time Dedicated to Specific Stages of Retrospective Studies: Consultant

178         Figure 3.39: Percentage of Time Dedicated to Specific Stages of Retrospective Studies: All Companies

179         Figure 3.40: Percentage of Budget Dedicated to Specific Stages of Retrospective Studies: All Pharma

180         Figure 3.41: Percentage of Budget Dedicated to Specific Stages of Retrospective Studies: Device

181         Figure 3.42: Percentage of Budget Dedicated to Specific Stages of Retrospective Studies: Consultant

183         Figure 3.43: Percentage of Time and Budget Dedicated to Each Stage of Retrospective Studies: All Companies

184         Figure 3.44: Study Goals Big Data Strategies Will Help Achieve: All Pharma

185         Figure 3.45: Study Goals Big Data Strategies Will Help Achieve: Device

186         Figure 3.46: Study Goals Big Data Strategies Will Help Achieve: Consultant

187         Figure 3.47: Study Goals Big Data Strategies Will Help Achieve: All Companies

188      Retrospective Studies: Profiles

189         Figure 3.48:  Retrospective Study 1: Companywide Strategy and Big Data Impact

190         Figure 3.49:  Retrospective Study 1: Highlights and Data Use

191         Figure 3.50:  Retrospective Study 1: Resource Allocation and Challenges

192         Figure 3.51:  Retrospective Study 2: Companywide Strategy and Big Data Impact

193         Figure 3.52:  Retrospective Study 2: Highlights and Data Use

194         Figure 3.53:  Retrospective Study 2: Resource Allocation and Challenges

195         Figure 3.54:  Retrospective Study 3: Companywide Strategy and Big Data Impact

196         Figure 3.55:  Retrospective Study 3: Highlights and Data Use

197         Figure 3.56:  Retrospective Study 3: Resource Allocation and Challenges

198         Figure 3.57:  Retrospective Study 4: Companywide Strategy and Big Data Impact

199         Figure 3.58:  Retrospective Study 4: Highlights and Data Use

200         Figure 3.59:  Retrospective Study 4: Resource Allocation and Challenges

201         Figure 3.60:  Retrospective Study 5: Companywide Strategy and Big Data Impact

202         Figure 3.61:  Retrospective Study 5: Highlights and Data Use

203         Figure 3.62:  Retrospective Study 5: Resource Allocation and Challenges

204         Figure 3.63:  Retrospective Study 6: Companywide Strategy and Big Data Impact

205         Figure 3.64:  Retrospective Study 6: Highlights and Data Use

206         Figure 3.65:  Retrospective Study 6: Resource Allocation and Challenges

207         Figure 3.66:  Retrospective Study 7: Companywide Strategy and Big Data Impact

208         Figure 3.67:  Retrospective Study 7: Highlights and Data Use

209         Figure 3.68:  Retrospective Study 7: Resource Allocation and Challenges

210      Market Intelligence: Channeling Big Data Strategies To Visualize The Competitive Landscape

211      Using Big Data Strategies To Drive Product Performance In A Complex Market

212         Figure 4.1: Functions Conducting Market Intelligence Initiatives: Top 10

213         Figure 4.2: Functions Conducting Market Intelligence Initiatives: Top 50

215         Figure 4.3: Functions Conducting Market Intelligence Initiatives: Small Pharma

216         Figure 4.4: Functions Conducting Market Intelligence Initiatives: Affiliate

217         Figure 4.5: Functions Conducting Market Intelligence Initiatives: All Pharma

218         Figure 4.6: Functions Conducting Market Intelligence Initiatives: Consultant

219         Figure 4.7: Functions Conducting Market Intelligence Initiatives: All Companies

222         Figure 4.8: Overall Ratings of Data Sources for Market Intelligence Initiatives: Top 10, Top 50, Small and Affiliate Pharma

223         Figure 4.9: Overall Ratings of Data Sources for Market Intelligence Initiatives: All Pharma

225         Figure 4.10: Overall Ratings of Data Sources for Market Intelligence Initiatives: Consultant

226         Figure 4.11: Overall Ratings of Data Sources for Market Intelligence Initiatives: All Companies

227         Enhance Market Intelligence Through A Broad Range Of Big Data Applications

228         Figure 4.12: Market Intelligence Big Data Applications: Top 10

229         Figure 4.13: Market Intelligence Big Data Applications: Top 50

230         Figure 4.14: Market Intelligence Big Data Applications: Small Pharma

231         Figure 4.15: Market Intelligence Big Data Applications: Affiliate

232         Figure 4.16: Market Intelligence Big Data Applications: All Pharma

233         Figure 4.17: Market Intelligence Big Data Applications: Consultant

234         Figure 4.18: Market Intelligence Applications Using Big Data: Consultant and All Pharma

236         Figure 4.19: Activities Using Big Data to Characterize Diseases and Patient Populations: Top 10

236         Figure 4.20: Activities Using Big Data to Characterize Diseases and Patient Populations: Top 50

237         Figure 4.21: Activities Using Big Data to Characterize Diseases and Patient Populations: Small Pharma

238         Figure 4.22: Activities Using Big Data to Characterize Diseases and Patient Populations: All Pharma

239         Figure 4.23: Activities Using Big Data to Characterize Diseases and Patient Populations: Consultant

240         Figure 4.24: Activities Using Big Data to Characterize Diseases and Patient Populations: Consultant and All Pharma

242         Figure 4.25: Activities Using Big Data to Develop New Products and Therapies: All Pharma

242         Figure 4.26: Activities Using Big Data to Develop New Products and Therapies: All Companies

244         Figure 4.27: Activities Leveraging Big Data to Assess the Performance of Products and Therapies on the Market: Top 10, Top 50, Small and       Affiliate Pharma

245         Figure 4.28: Activities Using Big Data to Assess Performance of Products and Therapies on the Market: All Pharma

246         Figure 4.29: Activities Using Big Data to Assess Performance of Products and Therapies on the Market: Consultant

247         Figure 4.30: Activities Using Big Data to Assess Performance of Products and Therapies on the Market: Consultants and All Pharma

249         Figure 4.31: Activities Using Big Data to Target Products and Services: Top 50

249         Figure 4.32: Activities Using Big Data to Target Products and Services: All Pharma

250         Figure 4.33: Activities Using Big Data to Target Products and Services: Consultant

251         Figure 4.34: Activities Using Big Data to Target Products and Services: Consultant and All Pharma

253         Figure 4.35: Big Data Strategies to Guide Company Social Media and Digital Marketing Usage: All Pharma

254      Leverage Social Media Channels In Market Intelligence Initiatives

256         Figure 4.36: Percentage of Companies Leveraging Social Media Channels to Collect Big Data: Top 10

256         Figure 4.37: Percentage of Companies Leveraging Social Media Channels to Collect Big Data: Top 50

257         Figure 4.38: Percentage of Companies Leveraging Social Media Channels to Collect Big Data: Small Pharma

258         Figure 4.39: Percentage of Companies Leveraging Social Media Channels to Collect Big Data: All Pharma

259         Figure 4.40: Percentage of Companies Leveraging Social Media Channels to Collect Big Data: Consultant

260         Figure 4.41: Percentage of Companies Leveraging Social Media Channels to Collect Big Data: All Companies

261         Figure 4.42: Social Media Platforms Used to Collect Big Data: All Companies

263         Figure 4.43: Tools Used to Collect Social Media Big Data: Top 10

264         Figure 4.44: Tools Used to Collect Social Media Big Data: Top 50

265         Figure 4.45: Tools Used to Collect Social Media Big Data: Small Pharma

266         Figure 4.46: Tools Used to Collect Social Media Big Data: All Pharma

267         Figure 4.47: Tools Used to Collect Social Media Big Data: Consultant

268         Figure 4.48: Overall Use of Tools to Collect Social Media Big Data: All Companies

270         Figure 4.49: Big Data Utility Ratings from Social Media Channels for Specific Activities: Top 10

271         Figure 4.50: Big Data Utility Ratings from Social Media Channels for Specific Activities: Top 50

272         Figure 4.51: Big Data Utility Ratings from Social Media Channels for Specific Activities: Small Pharma

273         Figure 4.52: Big Data Utility Ratings from Social Media Channels for Specific Activities: All Pharma

274         Figure 4.53: Big Data Utility Ratings from Social Media Channels for Specific Activities: Consultant

275         Figure 4.54: Ratings Comparing Utility of Big Data from Social Media Channels for Specific Activities: Consultant and Pharma

277         Figure 4.55: Challenges in Working with Social Media for Big Data Collection and Analysis: Top 10

277         Figure 4.56: Challenges in Working with Social Media for Big Data Collection and Analysis: Top 50

278         Figure 4.57: Challenges in Working with Social Media for Big Data Collection and Analysis: Small Pharma

278         Figure 4.58: Challenges in Working with Social Media for Big Data Collection and Analysis: Affiliate

279         Figure 4.59: Challenges in Working with Social Media for Big Data Collection and Analysis: All Pharma

280         Figure 4.60: Challenges in Working with Social Media for Big Data Collection and Analysis: Consultant

281         Figure 4.61: Challenges in Working with Social Media for Big Data Collection and Analysis: Consultant and All Pharma

283         Figure 4.62: Ratings Showing Value of Information Gathered from Social Channels: Top 10, Top 50, Small and Affiliate Pharma

284         Figure 4.63: Ratings Showing Value of Information Gathered from Social Channels: All Pharma

285         Figure 4.64: Ratings Showing Value of Information Gathered from Social Channels: Consultant

286         Figure 4.65: Ratings Showing Value of Information Gathered from Social Channels: Consultant and All Pharma

288         Figure 4.66: Companies Leveraging Gamification to Collect Big Data: All Companies

289      Market Intelligence Initiatives: Profiles

290         Figure 4.67:  Company 6 Market Intelligence Initiatives:  Companywide Strategy

291         Figure 4.68: Company 6 Market Intelligence Initiatives:  Big Data Applications

292         Figure 4.69: Company 6 Market Intelligence Initiatives: Big Data Applications (cont.)

293         Figure 4.70: Company 6 Market Intelligence Initiatives: Social Media Strategies and Utility

294         Figure 4.71: Company 6 Market Intelligence Initiatives: Social Media Challenges and Metrics Value

295         Figure 4.72: Company 10 Market Intelligence Initiatives:  Companywide Strategy

296         Figure 4.73: Company 10 Market Intelligence Initiatives: Big Data Applications

297         Figure 4.74: Company 10 Market Intelligence Initiatives: Social Media Strategies and Utility

298         Figure 4.75: Company 10 Market Intelligence Initiatives: Social Media Challenges and Metrics Value

299         Figure 4.76:  Company 15 Marketing Intelligence Initiatives:  Companywide Strategy

300         Figure 4.77: Company 15 Market Intelligence Initiatives:  Big Data Applications

301         Figure 4.78: Company 15 Market Intelligence Initiatives: Social Media Strategies and Utility

302         Figure 4.79: Company 15 Market Intelligence Initiatives: Social Media Challenges and Metrics Value

303         Figure 4.80:  Company 21 Market Intelligence Initiatives:  Companywide Strategy

304         Figure 4.81: Company 21 Market Intelligence Initiatives:  Big Data Applications

305         Figure 4.82: Company 21 Market Intelligence Initiatives: Big Data Applications (cont.)

306         Figure 4.83: Company 21 Market Intelligence Initiatives: Social Media Strategies and Utility

307         Figure 4.84: Company 21 Market Intelligence Initiatives: Social Media Challenges and Metrics Value

308         Figure 4.85:  Company 26 Market Intelligence Initiatives:  Companywide Strategy

309         Figure 4.86: Company 26 Market Intelligence Initiatives:  Big Data Applications

310         Figure 4.87: Company 26 Market Intelligence Initiatives: Social Media Strategies and Utility

311         Figure 4.88: Company 26 Market Intelligence Initiatives: Social Media Challenges and Metrics Value

312         Figure 4.89:  Company 47 Market Intelligence Initiatives:  Companywide Strategy

313         Figure 4.90: Company 47 Market Intelligence Initiatives:  Big Data Applications

314         Figure 4.91: Company 47 Market Intelligence Initiatives: Social Media Strategies and Utility

315         Figure 4.92: Company 47 Market Intelligence Initiatives: Social Media Challenges and Metrics Value

316      Big Data Challenges And Trends

317      Involve Third-Party Vendors To Develop Big Data Strategies

319         Figure 5.1: Prevalence of Outsourcing for Specific Big Data Tasks: All Pharma

320         Figure 5.2: Prevalence of Outsourcing for Specific Big Data Tasks: Device

321         Figure 5.3: Prevalence of Outsourcing for Specific Big Data Tasks: Consultant

322         Figure 5.4: Prevalence of Outsourcing for Specific Big Data Tasks: All Companies

323         Figure 5.5: Percentage of Initiative Budget Outsourced for Data Collection: All Companies

324         Figure 5.6: Percentage of Initiative Budget Outsourced for Data Storage: All Companies

325         Figure 5.7: Percentage of Initiative Budget Outsourced for Data Analysis: All Companies

327      Plan For Successful Big Data Strategy Implementation

328         Figure 5.8: Percentage of Companies Measuring ROI of Big Data Initiatives, by Company Type

329         Figure 5.9: Percentage of Companies That Have Implemented a Big Data Pilot Program: All Companies

330         Figure 5.10: Effectiveness of Implemented Pilot Programs: All Companies

332         Figure 5.11: Preparations for Big Data/Analytics Activities: Top 10

333         Figure 5.12: Preparations for Big Data/Analytics Activities: Top 50

334         Figure 5.13: Preparations for Big Data/Analytics Activities: Small Pharma

335         Figure 5.14: Preparations for Big Data/Analytics Activities: Affiliate

336         Figure 5.15: Preparations for Big Data/Analytics Activities: Device

337         Figure 5.16: Preparations for Big Data/Analytics Activities: Consultant

338         Figure 5.17: Preparations for Big Data/Analytics Activities: All Companies

339      Overcome Challenges To Accelerate Big Data Success

340         Figure 5.18: Ratings of Big Data Challenges: Top 10

340         Figure 5.19: Ratings of Big Data Challenges: Top 50

341         Figure 5.20: Ratings of Big Data Challenges: Small Pharma

342         Figure 5.21: Ratings of Big Data Challenges: Affiliate

343         Figure 5.22: Ratings of Big Data Challenges: All Pharma

344         Figure 5.23: Ratings of Big Data Challenges: Device

345         Figure 5.24: Ratings of Big Data Challenges: Consultant

346         Figure 5.25: Ratings of Big Data Challenges: All Companies