RESEARCHING THE POTENTIAL OF MOBILE BIG DATA FOR BUSINESS DECISION-MAKING Case of Elgiganten AB - Haparanda Hung Duc Luu Bachelor's Thesis Lapland University of Applied Sciences Degree Programme in Business Information Technology Bachelor of Business Administration 2014 Abstract of thesis School of Business and Culture Degree Programme in Business Information Technology Author Duc Hung Luu Year 2014 Supervisor Vladimir Ryabov Commissioned by Harri Putila, Elgiganten AB - Haparanda Title of thesis Researching the Potential of Mobile Big Data for Business Decision-Making Case of Elgiganten AB - Haparanda No. of pages + app. 74 + 10 This research is focused on the area of mobile Big Data, particularly personal location data and its usage to gain insights into customers’ in-store behaviours. The objective of this research is to study the practical potential of mobile Big Data in order to evaluate its impact on business decision-making in the case company. This study was commissioned by Elgiganten AB – Haparanda. The case company is a part of Elkjøp Nordic AS, the largest consumer electronics and home appliances retailer in Sweden. The study stemmed from the need to increase the case company’s understanding of the customers’ behaviours. This research is practically oriented with the theoretical framework revolving around studying the mobile Big Data, sensors’ tracking technologies and Apache Hadoop applications. To accomplish the objectives of this research, the business scenario using mobile Big Data to support decision making in the case company was formulated and scrutinised. The qualitative research method was used in this single case study for the case company. Exploratory research approach was chosen due to the novelty of the research area. This research makes extensive use of both primary and secondary data. The primary data was gathered through in-depth interviews and questionnaire. The secondary data was collected from established research works conducted on mobile Big Data and its related issues, i.e. Big Data technologies, business value and ethics. On the basis of the theoretical discussions, the business scenario, and the in- depth interviews and questionnaire, the research results indicate that mobile Big Data can provide various valuable insights into the customers’ behaviours in the case company’s retail store. Consequently, the business decision-making can be enhanced. Keywords: mobile Big Data, sensor technologies, personal location data, Apache Hadoop, retail store, business value, privacy CONTENTS ABSTRACT FIGURES AND TABLES 1 INTRODUCTION ............................................................................................. 6 1.1 Background and motivation....................................................................... 6 1.2 Description of the case company .............................................................. 8 1.3 Research objectives .................................................................................. 9 1.4 Structure of the thesis ............................................................................. 10 2 RESEARCH SCOPE, QUESTIONS AND METHODOLOGY ......................... 11 2.1 Research scope ...................................................................................... 11 2.2 Research questions ................................................................................ 12 2.3 Research methodology ........................................................................... 13 3 ONTOLOGY OF BIG DATA IN MOBILE COMPUTING ................................. 15 3.1 Big Data .................................................................................................. 15 3.2 Mobile Big Data ...................................................................................... 16 3.3 Location data .......................................................................................... 17 3.4 Activity and location tracking sensors ..................................................... 18 3.4.1 Global Positioning System (GPS) sensors .................................... 18 3.4.2 Wi-Fi-based positioning system (WPS) sensors............................ 19 3.4.3 Accelerometer sensors .................................................................. 20 3.4.4 Gyroscope sensors ....................................................................... 21 3.4.5 Other technologies ........................................................................ 22 3.5 Apache Hadoop ...................................................................................... 23 3.5.1 Apache Flume and Apache Sqoop ............................................... 25 3.5.2 Hadoop Distributed File System (HDFS) ....................................... 26 3.5.3 Hadoop YARN ............................................................................... 28 3.5.4 Apache Hive .................................................................................. 30 3.5.5 Apache Pig .................................................................................... 31 3.6 Mobile Big Data visualisation .................................................................. 32 3.6.1 Traffic flow map ............................................................................. 34 3.6.2 Route map ..................................................................................... 35 3.6.3 Density map .................................................................................. 36 3.6.4 Single-point motion map ................................................................ 37 3.6.5 Walking-speed map ....................................................................... 38 3.7 Business value of mobile Big Data .......................................................... 39 3.8 Privacy and ethics ................................................................................... 42 4 ANALYSIS OF EMPIRICAL DATA ................................................................. 44 4.1 Analysis of interview data ....................................................................... 44 4.1.1 The case company IT infrastructure .............................................. 45 4.1.2 The importance of understanding customers’ in-store behaviours 45 4.1.3 The case company policies to protect customer rights .................. 46 4.2 Analysis of questionnaire data ................................................................ 47 4.2.1 Customers’ demographics ............................................................. 48 4.2.2 Customers’ shopping habits and behaviours ................................. 49 4.2.3 Customers’ attitude toward location data ...................................... 52 4.3 Summary on the results of data collection .............................................. 53 5-BUSINESS SCENARIO USING MOBILE BIG DATA TO SUPPORT DECISION-MAKING IN THE CASE COMPANY ............................................ 55 5.1 Business scenario overview .................................................................... 55 5.2 Activity and location sensing systems ..................................................... 56 5.3 Total solution cost of business scenario in the case company's retail store ....................................................................................................... 58 5.4 Findings and recommendations .............................................................. 63 6 CONCLUSIONS ............................................................................................. 66 REFERENCES ................................................................................................. 69 APPENDICES ................................................................................................... 75 5 FIGURES AND TABLES Figure 1. Input and output information of mobile location data (Manyika et al. 2011, 92) ............................................................................................. 7 Figure 2. Hadoop mobile data platform ............................................................ 25 Figure 3. A client writing data to HDFS (White 2012, 71) ................................. 28 Figure 4. Apache Hadoop NextGenMapReduce (YARN) (The Apache Software Foundation 2014) .............................................................................. 30 Figure 5. The example map of the customers’ traffic flow in the store ............. 34 Figure 6. The example map of the customers’ routes in the store ................... 35 Figure 7. The example map of the customers’ distribution rate in the store ..... 36 Figure 8. The example map of the customers’ angular frequency ................. 37 Figure 9. The example map of the customers’ walking speed in the store ....... 38 Figure 10. The biz-tech ecosystem reflects the complexity of today’s business (Devlin 2014) ..................................................................................... 39 Figure 11. Respondents’ domicile and gender ................................................. 48 Figure 12. Percentage of respondents’ age range ........................................... 49 Figure 13. Store visiting frequency ................................................................... 50 Figure 14. Respondents’ time spent and purposes when visiting the store ..... 50 Figure 15. Respondents’ habit when visiting the store ..................................... 51 Figure 16. Respondents’ perception of collecting location data ....................... 52 Figure 17. The Aaronia GPS – Logger (Aaronia AG 2011) .............................