Making sense of data whether big or small

Transcription

1 Making sense of data whether big or small PROF SHARM MANWANI OLIVER LEWIS PAUL HINTON NICOLA FULFORD ALEKS KROTOSKI 25/06/2014

2 Making sense of data whether big or small Devices that can access, create, transmit and store data Prevalence of Internet and access to services Cloud ease and cost of storing data gone down enormously Vast increase in the amount of stored data Our understanding and management of that data is poor The stone age was marked by man's clever use of crude tools; the information age, to date, has been marked by man's crude use of clever tools.

3 Making sense of data whether big or small What is new? Software, hardware and cloud which can search vast volumes of structured and unstructured data quickly New techniques for analysing data algorithims New awareness of the potential and value of data analysis Big Data Knowing a great deal is not the same as being smart; intelligence is not information alone but also judgment, the manner in which information is collected and used

4 Making sense of data data potential

5 Making sense of data risks Plans Change

6 Exploiting data whilst remaining compliant and ensuring customer buy-in exploit data remain compliant Customer buy-in allow data subjects control provide value to data subjects non-compliance going too far being found out customer perception lack of trust bad PR loss of business

7 The Big Data Analytical Horizontal Model Input Data Big Data Factory Output Data Social Media Live data feed Internal structured data Third party review Third Party Data Transaction data Data Storage Platform Cloud? Algorithm Search engine database Question Regular Report Internal unstructured Data Public domain, market data, social media, personal data, confidential data, licensed data, government data, employee data, self generated and/or derived Third party apps systems and cloud? Algorithms, pan-enterprise search, one view of information, information re-purposing Internal use Product development, CRM, sales/marketing, product development, External use

8 The vertical model for the data-centric world Information Management Data Regulation Generic: data protection, communications Sector specific: financial services, healthcare, aviation Competition law: Articles 101 & 102 Contracting for Data IP Rights in Data Copyright, database right, confidentiality, patents, trade marks Data Security Data architecture (Structure, schemas, strategies, methods) Platform infrastructure (software) (operating system, database, transaction processing, comms) Platform infrastructure (equipment) (processing, storage, comms and connectivity

9 The vertical model for the data-centric world Information Management Data Regulation Generic: data protection, communications Sector specific: financial services, healthcare, aviation Competition law: Articles 101 & 102 Contracting for Data IP Rights in Data Copyright, database right, confidentiality, patents, trade marks Data Security Data architecture (Structure, schemas, strategies, methods Platform infrastructure (software) (operating system, database, transaction processing, comms) Platform infrastructure (equipment) (processing, storage, comms and connectivity

10 Making sense of data whether big or small Potential and harnessing of Big Data - Prof. Sharm Manwani Making sense of data - Oliver Lewis, General Counsel, Weve Data Protection - Nicola Fulford Legal Role: Information management - Paul Hinton Panel discussion and questions

11 Data Potential and How to Manage It PROF. SHARM MANWANI

12 Potential and Harnessing of Big Data Highlight researched frameworks that can help: map the Potential for Big Data assess e-leaders who champion cross-departmental projects create a Business Change process from Strategy to Benefits Professor Sharm Manwani, Henley Business School KEMP LITTLE CONFERENCE, June 2014 Insert footer on Slide Master

13 Profile Experience contributing to insights on Big Data and e-leadership PROFESSOR PROFESSIONAL EUROPEAN CIO Insights AUTHOR Professor Sharm Manwani - June

14 Potential of Big Data PERFORMANCE The more data-driven, the better they performed on financial and operational results. Companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors. Is Your Company Ready for Big Data? by Erik Brynjolfsson and Andy McAfee (HBR) Professor Sharm Manwani - June

15 Challenges of Big Data Professor Sharm Manwani - June

16 Information Management Dimensions Changing focus of information technology Structured Unstructured Exploitation Control Source: Prof. Sharm Manwani, Henley Business School Professor Sharm Manwani - June 2014

17 Information Management Model Four different worlds of information management Structured Business Unstructured Knowledge IM Group Exploitation Intelligence Management Metropolitan Police Service MoD Yell Control Operational Document British Computer Society Data & Content CILIP Gartner Henley Business School Professor Sharm Manwani - June 2014

18 Scope of Information Management Influencing SKILLS FRAMEWORK FOR THE INFORMATION AGE (SFIA) Structured Unstructured The overall management of the use of all types of information, Exploitation Business Intelligence Knowledge Management structured and unstructured, whether produced internally or externally, to support decision-making and business processes. Encompasses development and promotion of the strategy and BIG DATA policies covering the design of information structures and taxonomies, the setting of policies for the sourcing and Control Operational Document maintenance of the data content, and the development of policies, procedures, working practices and training to promote Data & Content compliance with legislation regulating the management of records, and all aspects of holding, use and disclosure of data. Professor Sharm Manwani - June 2014 Source: SFIA 5 - IRMG

19 Mapping Potential of Big Data Big Data is helping to connect the four different worlds We re changing our company to be much more of a real-time digital enterprise, said the CTO Structured Unstructured Exploitation Business Intelligence Knowledge Management BIG DATA Control Operational Data Document & Content Source: Prof. Sharm Manwani, Henley Business School Professor Sharm Manwani - June 2014

20 Information Management Evaluation Evaluating the potential of big data Structured Unstructured Exploitation Business Intelligence Knowledge Management BIG DATA Control Operational Data Document & Content On a scale of 1 (very low) to 10 (very high) how would you separately rate your Control and Exploitation of Information Source: Prof. Sharm Manwani, Henley Business School Professor Sharm Manwani - June 2014

21 e-leadership Research European e-leadership research and dissemination IT Business Leadership Professor Sharm Manwani - June

22 e-leadership e-leaders have a different focus TRADITIONAL CIO IT DIGITAL STRATEGIST Their top activities are improving IT operations, deploying new systems and These CIOs drive business innovation, cultivating partnerships and developing controlling IT costs. Business Leadership strategy. Anyone who works for Barclays can become a digital eagle, as long as they re willing to help customers with technology. Barclays personal and corporate banking chief Professor Sharm Manwani - June

23 e-leadership Skills e-leadership requires integrated, multi-disciplinary skills IT (Exploit technology) BIG Business (New models) DATA Leadership (Innovation culture) On a scale of 1 (very low) to 10 (very high) how would you rate the quality of e-leadership in your environment Professor Sharm Manwani - June

24 State of the CIO Survey 2014 e-leaders are scarce TRADITIONAL CIO DIGITAL STRATEGIST 48 percent of CIOs acknowledge that their IT groups are viewed by fellow employees as a cost center or service provider. 25 percent report that the IT group is perceived by colleagues as a true business peer--or even a game-changer--that can create and launch new products and open new markets. Their top activities are improving IT operations, deploying new systems and controlling IT costs. These first-class CIOs identify their top activities as driving business innovation, cultivating partnerships and developing business strategy CIOs Professor Sharm Manwani - June

25 Process of Business Change Implementing Big Data is a Business Change Project Understand Potential of Big Data Develop Change Programmes For Big Data Unlock Value of Big Data Align Business & IT Goals Define Business Improvement Design Business Change Implement Business Change Deliver Benefits Direction Of Travel Professor Sharm Manwani - June 2014

26 Business Change Lifecycle Evaluating Business Change Capability Understand Potential of Big Data Develop Change Programmes For Big Data Unlock Value of Big Data Align Business & IT Goals Define Business Improvement Design Business Change Implement Business Change Deliver Benefits Direction Of Travel On a scale of 1 (very low) to 10 (very high) how capable is your organisation in managing business change Professor Sharm Manwani - June 2014

27 Summary Big Data Potential Important to take an integrated Exploitation Structured Business Intelligence Unstructured Knowledge Management approach BIG DATA Control Operational Data Document & Content e-leader Skills Development Need to develop e-leaders who IT can exploit big data BIG DATA Business Leadership Process Treat Big Data projects as Understand Potential of Big Data Develop Change Programmes For Big Data Unlock Value of Big Data Business Change Align Business & IT Goals Define Business Improvement Design Business Change Implement Business Change Deliver Benefits Direction Of Travel Contact Professor Sharm Manwani - June

28 Making sense of data OLIVER LEWIS, WEVE

29 Making sense of data whether big or small Oliver Lewis General Counsel - Weve June 2014

30 Weve reaches 80% of UK mobile customers Backed by three major shareholders 22 million consent based consumers

31 Mobile advertising and usage is growing YOY with mobile growing at a faster rate than online The mobile share of digital UK ad spend Consumer behaviour is changing in terms of time spent 13.4% % 19.4% 19.2% Smartphone ownership reached over two thirds (68%) of the UK population in June 2013 Mobile Desktop *econsultancy.com/uk/blog/63534-mobile-adspend-up-127-to-429m-in-first-half-of-2013

32 Myths about Big Data Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it. Data is the foundation of our business, not the business itself.

33 Weve s data foundation supports all our products Display Messaging Location Sync M commerce

34 This data is combined to build our scaled robust targetable segments E.g. Travellers This segment may for e.g. includes travellers who have activated roaming in at least 3 of the last 12 months and shown interest in travel websites. Social grade BARB region Sports Travel Finance Supermarkets Demographic Entertainment Automotive

35 Getting it right for brands AND the consumer is critical Consumer proposition Consent based If we don t, they opt out and we don t have a business. This means gaining their consent, respecting their wishes and being careful with their data. Easy and speedy opt out To unsubscribe text STOP to XXXXX Data hashing and security

36 Meet user X

37 We can provide targeted ads that are effective for brands AND relevant to the consumer

38 We won t spam consumers or show them things they don t want this is not what brands want

39 We aren t Big Brother, our ads won t creep out consumers this just alienates consumers and brands lose customers

40 Privacy consumers in control Privacy laws often feel vague and fail to keep up with new technology and consumer concerns. The online and mobile industry is searching for ways to facilitate privacy compliance in a world of fast-changing technology Clearly displayed Adchoices logo on ads served by Weve User can easily find out why they have been shown an ad Simple click-through to opt out Opt out given immediate effect Clear instructions provided Works across both rich media and static ads Opt out persistent (vastly more reliable that cookie based system)

41 Weve s core underlying principles For brands and consumers, our services will always be: Smart Relevant Valuable BUT The consumer always has the right to say NO

42 Thank you

43 Big Data Data Protection Implications NICOLA FULFORD Privacy & Data Protection Partner

44 Data Protection Refresher What is data protection in a nutshell? Imposes obligations on those who control the processing of personal data (principles) Confers rights on individuals whose data is processed Regulated and enforced by the ICO (fines up to 500k) Key legislation in the UK is the Data Protection Act 1998 (DPA) implementation of European Directive.

45 What is personal data? Data Protection Act 1998: Data which relate to a living individual who can be identified [or identifiable] from those data or from other information in the [controller s] possession including opinions about or intentions in respect of that person It is not: just name and contact details dependent upon being confidential Personal data is being processed where information is collected and analysed with the intention of distinguishing one individual from another and to take a particular action in respect of an individual even if no obvious identifiers, such as names or addresses, are held Information Commissioner s Office Guidance

46 And sensitive personal data? Commission / alleged commission of an offence or related court proceedings Sexual life Racial / ethnic origin Physical or mental health Personal data consisting of information as to Political opinions Membership of a trade union Religious beliefs must satisfy one of the conditions for sensitive processing and take additional security steps

47 Examples of personal data in a data-centric world There is a lot of personal data out there: location tracking (eg mobiles) call recordings facial recognition communications meta data wearables supermarket loyalty schemes social media cookies fitness apps smart meters hospital outcomes YouTube etc. videos the Internet of Things CCTV / ANPR Beware many of these are likely to also include sensitive personal data

48 Compliance with the DP principles Adequate protection outside EEA Fair & lawful Kept secure Purpose limitation Big Data Comply with subjects rights Adequate, relevant & not excessive Retention limits Accurate & up to date

49 Big Data: Compliance with the first principle Personal data must be fairly and lawfully processed (including compliance with a schedule 2 (and 3) condition) - Consent - Legitimate interests AND - Explicit consent - Deliberately made the information public I consent to [complex algorithms being applied to co-mingled data sets about me to derive information that can tell you all sorts of things about me that neither of us would be expecting] Please tick if you agree Meaning of Consent?

50 Big Data regulation: examples of regulatory guidance ICO: Code of Practice on Anonymisation Article 29 Working Party: Opinions on (i) Anonymisation and (ii) Purpose Limitation European Data Protection Supervisor: Opinion on privacy and competitiveness in the age of Big Data Federal Trade Commission: Investigation into data brokers.but: comments by European Digital Chief Neelie Kroes in March 2014 gold rush

51 Big Data: Is anonymisation the answer? Personal data or anonymous data? ICO Code of Practice Anonymisation ensures the availability of rich data resources, whilst protecting individuals personal data EC2V 6BJ IP address: Cookie ID: ce2123a969 IMEI: Device fingerprint Good practice: methods, governance, risk analysis, restrictions on disclosure Identifiable by means reasonably likely to be used / motivated intruder test Article 29 Working Party Opinion 05/2014 Analysis of anonymisation techniques Pseudonymised data?

52 And the proposed General Data Protection Regulation? Current status? Commission and Parliament have each agreed their own positions and texts Council is still negotiating its position: nothing will be agreed until everything is agreed Next steps: trialogue negotiations May be agreed end 2014?? / in force from 2017???

53 The GDP Regulation key themes for Big Data One law for all member states Profiling restricted Right to be forgotten One stop shop regulator Explicit Consent / Transparency Binding on processors too Accountability Cookies / IP address = personal data Fines up to 100m or 5% w/w turnover

54 Processing Big Data in accordance with data protection laws: Conclusions Consider what personal data may be involved in your big data project Consider whether this may constitute sensitive personal data Consider whether the data can be anonymised Ensure you have a schedule 2/3 condition for processing Check compliance with the other data protection principles in particular purpose limitation Future-proofing will this still be OK under the [GDP Regulation]? Review project over time. and don t assume you only have to do this once. In the world of Big Data nothing stays the same for long..

55 The Legal view of Information Management Navigating the gap PAUL HINTON COMMERCIAL TECHNOLOGY PARTNER

56 The Legal view of Information Management 1. Traditional legal role of ensuring compliance and/or assessing risk 2. Potentially broader role in defining the strategic approach to data We are all faced with a series of great opportunities brilliantly disguised as impossible situations.

57 The traditional legal analysis Input Data Data Factory Output Data Social Media Live data feed Internal structured data Third party review Third Party Data Transaction data Data Storage Platform Cloud? Algorithm Search engine database Question Regular Report Internal unstructured Data Public domain, market data, social media, personal data, confidential data, licensed data, government data, employee data, self generated and/or derived Third party apps systems and cloud? Algorithms, pan-enterprise search, one view of information, information re-purposing Internal use Product development, CRM, sales/marketing, product development, External use

58 Data Input Stage Information Management Data Regulation Generic: data protection, communications Sector specific: financial services, healthcare, aviation Competition law: Articles 101 & 102 Contracting for Data IP Rights in Data Copyright, database right, confidentiality, patents, trade marks Data Security Data architecture (Structure, schemas, strategies, methods Platform infrastructure (software) (operating system, database, transaction processing, comms) Platform infrastructure (equipment) (processing, storage, comms and connectivity

59 Data Input Stage Clear and consistent process NO Regulation? Contract? Intellectual Property Right? Can it be put into database? YES Will limits apply?

60 Data Factory Stage Information Management Data Regulation Generic: data protection, communications Sector specific: financial services, healthcare, aviation Competition law: Articles 101 & 102 Contracting for Data IP Rights in Data Copyright, database right, confidentiality, patents, trade marks Data Security Data architecture (Structure, schemas, strategies, methods Platform infrastructure (software) (operating system, database, transaction processing, comms) Platform infrastructure (equipment) (processing, storage, comms and connectivity

61 Data Factory Stage must ensure ongoing compliance and deal with change 1. Record input data attributes 4. Track and record changes to data 2. Ensure output data compliance 3. Track and record changes to law and regulation

62 Output Data Stage Information Management Data Regulation Generic: data protection, communications Sector specific: financial services, healthcare, aviation Competition law: Articles 101 & 102 Contracting for Data IP Rights in Data Copyright, database right, confidentiality, patents, trade marks Data Security Data architecture (Structure, schemas, strategies, methods Platform infrastructure (software) (operating system, database, transaction processing, comms) Platform infrastructure (equipment) (processing, storage, comms and connectivity

63 Output Data - Metered Access key terms Reflect Input Data Terms Contract is King Contract licence limited, reserve rights, refer to external documents to ensure flexible Explicitly terminate licence on termination

64 Legal Strategic Role: Strategy, Planning, Process and Procedure Cross team Compliance Multiskillset Project management Lawyer Senior buy-in Process procedure Strategy

65 Legal Strategic Role: Strategy, Planning, Process and Procedure The more highly regulated/contracted the input data the more complex the strategy, database and policies The database may need an IT solution to simply manage the data and implement strategy/policy There are a number of IT data methodologies for storing, tracking and managing data, e.g.: - DAMA (Data Management Association International) - MIKE2.0. Method for an Integrated Knowledge Environment - Enterprise Data Management Council Data

66 Legal Strategic Role: Strategy, Planning, Process and Procedure Legal strategic role taking a lead in developing and implementing: - High level data strategy: what does the business want to do with data now and in 3/5 years? what is the ethical business stance? E.g. creepy test - Policy statement: Key responsible people identified day-to day implementation - Processes, procedures and training: actionable guidelines applicable to all staff part of employment contract

67 Panel Discussion & Questions

68 Panel members Professor Sharm Manwani Executive Professor of IT Leadership, Henley Business School Oliver Lewis General Counsel, Weve Aleks Krotoski Leading academic, TV and Radio presenter and journalist Paul Hinton Commercial Partner Nicola Fulford Commercial Partner