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List of figures and tables |
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xii | |
About the authors |
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xviii | |
Foreword |
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xix | |
How to read this hook |
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xxv | |
Acknowledgements |
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xxvii | |
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PART ONE The L&D value gap and how to close it |
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01 The rise of learning analytics |
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3 | (18) |
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Why is all of this important? |
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6 | (1) |
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7 | (1) |
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8 | (1) |
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Changing the way talent analytics work gets done |
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9 | (3) |
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Providing unique insight into employee behaviour |
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12 | (1) |
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13 | (6) |
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19 | (2) |
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02 What is learning analytics? |
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21 | (28) |
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21 | (2) |
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Learning analytics today: measure for measure, what should be measured? |
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23 | (2) |
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25 | (1) |
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Most organizations start with the simple: measure training activity and satisfaction |
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26 | (2) |
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Efficiency, effectiveness and business outcomes: closing the learning measurement gap |
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28 | (1) |
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The journey to learning analytics |
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29 | (1) |
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The Four Levels of Evaluation |
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30 | (1) |
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The Return on Investment Methodology |
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31 | (2) |
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Impact Measurement Framework |
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33 | (1) |
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34 | (2) |
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Performance-Based Evaluation |
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36 | (8) |
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44 | (2) |
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46 | (3) |
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03 The value-centred learning organization: A new evaluation paradigm |
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49 | (22) |
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49 | (1) |
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We're already delivering value, though right? |
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50 | (2) |
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Delivering and demonstrating value: the Talent Development Value Framework |
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52 | (9) |
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The Talent Development Value Framework in action |
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61 | (2) |
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Advancing measurement maturity |
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63 | (2) |
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65 | (1) |
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66 | (5) |
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PART TWO Establishing sound measurement practices |
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04 Aligning L&D's value with the C-suite: The Four Value Drivers and Portfolio Evaluation |
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71 | (22) |
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What the C-suite wants from L&D |
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71 | (2) |
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Connecting L&D with the business strategy |
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73 | (1) |
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74 | (2) |
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Building business alignment |
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76 | (2) |
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Translating value drivers to action: Portfolio Evaluation for L&D |
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78 | (5) |
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Immediate benefits of portfolio alignment |
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83 | (2) |
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Additional benefits: portfolio management |
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85 | (5) |
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90 | (1) |
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91 | (1) |
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91 | (2) |
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05 Linking learning to business impact |
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93 | (25) |
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93 | (2) |
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95 | (4) |
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99 | (4) |
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Alternatives to experimental designs |
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103 | (1) |
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Alternative designs: practical ways forward |
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103 | (11) |
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114 | (2) |
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116 | (2) |
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06 The new leading indicators of success and how to manage them |
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118 | (38) |
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Your training programmes may not be as good as you think they are |
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118 | (3) |
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Scrap learning and how to reduce it |
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121 | (5) |
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126 | (3) |
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129 | (2) |
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Manager support and how to improve it |
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131 | (9) |
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Predictive Learning Impact Model 2.0: Causal Modelling |
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140 | (14) |
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154 | (1) |
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155 | (1) |
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07 Developing a sustainable reporting strategy |
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156 | (36) |
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The role of reporting in learning analytics |
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156 | (3) |
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Getting started: design principles |
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159 | (2) |
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Components of an effective reporting strategy |
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161 | (9) |
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Reporting strategy development |
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170 | (2) |
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172 | (3) |
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175 | (6) |
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Implementing the strategy |
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181 | (2) |
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Special cases: dashboards and scorecards |
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183 | (5) |
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Monitor the strategy: success indicators |
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188 | (1) |
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189 | (1) |
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190 | (2) |
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08 Technology's role in learning measurement |
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192 | (29) |
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What should technology do? |
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193 | (1) |
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Benefits and costs of learning technologies |
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194 | (6) |
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The requirements for a new technology system in the BI space |
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200 | (6) |
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The challenge of self-reported data |
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206 | (3) |
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What is the ROI of technology systems? |
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209 | (1) |
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Applying principles of business intelligence systems to L&D |
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210 | (5) |
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215 | (3) |
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218 | (1) |
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219 | (2) |
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221 | (22) |
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Comparison to standards provides insights for decision-making |
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221 | (1) |
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Benchmarking improves maturity |
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222 | (2) |
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Why are benchmarks valuable in the L&D space? |
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224 | (1) |
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What benchmarks are available? |
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225 | (3) |
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Benchmarks and statistical significance |
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228 | (6) |
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What does MTM bring to the market beyond benchmarks? |
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234 | (1) |
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How do clients use benchmarks to support decision-making? |
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235 | (2) |
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237 | (1) |
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237 | (6) |
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PART THREE Refine the strategy: Evolution, ongoing transformation and innovation |
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10 Driving alignment from strategy through execution |
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243 | (28) |
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243 | (8) |
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The ADDIE Model: sustaining alignment using a cyclical approach |
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251 | (15) |
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266 | (2) |
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268 | (1) |
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269 | (2) |
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11 Optimizing investments in learning |
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271 | (26) |
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Learning and development groups struggle to create value |
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271 | (2) |
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273 | (2) |
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Reporting measures to the business |
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275 | (10) |
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Working with business leaders |
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285 | (1) |
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Continuous improvement and management approaches |
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285 | (3) |
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288 | (1) |
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289 | (2) |
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291 | (1) |
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Moving from reporting to action |
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291 | (4) |
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295 | (1) |
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295 | (2) |
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12 Measuring informal learning outcomes |
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297 | (27) |
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297 | (2) |
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What is informal learning? What is social learning? |
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299 | (1) |
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The new learning landscape |
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300 | (4) |
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Learning from the past: e-learning lessons |
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304 | (2) |
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Organizational ecosystem for informal learning |
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306 | (2) |
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Traps, potholes and pitfalls of informal learning |
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308 | (3) |
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Showing value through measurement |
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311 | (1) |
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What should we measure to show value? |
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312 | (5) |
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317 | (3) |
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320 | (1) |
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321 | (3) |
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13 Beyond learning analytics to talent management analytics |
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324 | (33) |
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The future is for those who can predict it |
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324 | (1) |
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Defining what to measure in talent management |
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325 | (3) |
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Understanding the employee life cycle |
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328 | (3) |
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331 | (1) |
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Research on talent analytics |
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331 | (6) |
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It's not the analytics that matter: it's how they are applied |
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337 | (2) |
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Managing data in the analytics process |
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339 | (2) |
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Improving analytic impact |
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341 | (3) |
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How companies are addressing the challenge of talent analytics impact |
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344 | (6) |
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Analytics across the talent life cycle |
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350 | (7) |
Conclusion |
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357 | (1) |
Notes |
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358 | (2) |
Index |
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360 | |