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Big Data by Mind Map: Big Data

1. Sector

1.1. Analysis

1.2. Finding trends in data the previously wouldn't have been possible

1.3. Turnover £40m

1.4. Hardware (anything connected)

1.5. Algorithyms

1.6. Visualisation software

1.7. Technology

1.8. Predictions

1.9. Need for new ways to process large complex data sets

1.10. Connectivity

1.11. In 2013, big data services and products grew to $18 billion

1.12. Store data

1.13. Created prior to internet - about new ways to handle very large data sets

1.14. People

1.15. Capture data

1.16. Philosophy: From patterns to profits

2. Change

2.1. New methods

2.2. Mid to small market expansion

2.3. Cloud computing

2.4. Expanding

2.5. Integration

2.6. Instantaneous analysis

2.7. Internet of things

2.8. Changing trends

2.9. More widespread

2.10. Personalised

3. Questions

3.1. Partners?

4. Network

4.1. Issue: Quality of Data Sets

4.2. Supply networks

4.3. Software/technology companies

4.4. Suppliers of Data Management

4.4.1. Hadoop

4.4.2. NoSQL

4.5. Suppliers of Analysis

4.5.1. PWC

4.5.2. Big 4

4.5.3. Capita

4.5.4. SAS

4.5.5. Other SAS

4.6. Software/technology companies

4.7. Servers

4.8. Hardware that gathers data

4.8.1. Sensors

4.8.2. Mobile Phones

4.8.3. Internet of things

4.9. Needs of existing users of technology (stretching to big data) vs needs of new users of technology (starting from scratch)

4.10. Companies of innovation within Big Data

4.11. 75% of North American organsations believe the volume and complexity of their data requires big data analytics, applications and tools.

4.12. Clients

4.13. Hardware suppliers

4.14. Are there are bottlenecks in the supply chain?

5. Competencies

5.1. Business Expertise

5.2. Statiticians/data scientists

5.3. Data users, data suppliers, data facilitators - which one is Capita?

5.4. Data visualisation

5.5. Software engineers

5.6. Domain/context knowledge

5.7. Working with big data

5.8. Make sense of complex data

5.9. IT engineers

5.10. Key competencies in sector

5.11. Technological

5.12. For big data analysis

6. Products and Services

6.1. Practical uses of big data

6.2. Software/technology

6.3. Analysis of data and predictions/trends

6.4. Different uses for big data

6.5. Barriers to adoption

6.6. Top outcomes for organisations that use big data

6.7. Market trends

6.8. Early warning systems

6.9. Advertising

6.10. Data Mining

6.11. Customer behaviour data

7. Intellectual Property/Legal Environment

7.1. Sensitivity (commercial/personal)

7.2. Hacking

7.3. Security of data

7.4. Exclusivity over data source

7.5. Data protection act

7.6. Algorithms would use IP