Information Sharing, Big Data and the FRS. Rob Wilson, Jyoti Mishra and James Cornford

Transcription

1 Information Sharing, Big Data and the FRS Rob Wilson, Jyoti Mishra and James Cornford

2 Structure Rob Wilson (Newcastle University) Introduction Jyoti Mishra (Bradford University) Information Sharing in Emergency Services James Cornford (University of East Anglia & ESRC Business and Local Government Data Research Centre) Information sharing and Big Data in FRS

3 Multi-agency information sharing during major incidents Cabinet Office, highlighted the importance of information to respond to an emergency, stating that information is critical to emergency response [ ] and must not be underestimated (Cabinet_Office, 2010) One of the major problems, in emergency management, is the coordination among responders (McEntire, 2002; Quarantelli, 1988) There is a need for research in inter-organizational coordination focusing on communication problem as communication difficulties are coordination difficulties (Heide, 1989). Information sharing is important for getting full picture and a common operating picture Strategic (Gold) Commander Tactical (Silver) Commander Operational (Bronze) Commander Command structure of the emergency services in the UK

4 Findings Personal factor: it was a case of verbally giving all that information over to someone else coming over and therefore things got missed, inevitably. And you probably couldn t do it in a logical manner, you know Culture: I mean sharing information is both explicit and implicit in it but if you have got some elements, some of the information sharers... their culture is not to share information then that becomes quite a challenge Rules: that if you are exchanging information at major event for public safety reasons to save life and property it s a comfortable process, it s allowable. So the constraints are not too onerous when it comes to, when you are dealing with major events

5 Findings Trust: I usually don t have to ask (information) to them (silver commanders from other agencies) because we are all so used to working with each other. It is very rare- you have to actually try and extract the information. It is all usually offered up- we are used to working together so much Over Classification: We tend to over classify. So sometimes in the case of the police and the military, they classify information so strongly and they are not able to share it and I think that is not helpful for the combined response Language/Jargon: So use of the language when we talk to other agencies is really important. And one of the things that you need to talk about decision and you are thinking is: never assuming that the other person on the other end of the phone understands the jargon that you are using

6 Findings Concise communication: It s about the rules of the game really recognizing sometimes that concise information can be a little bit bare and sometimes you do need to have a little bit more background or something but there again in a tight time frame you haven t got time for everybody to give you a full story about everything or the decision time has passed Availability: I worked in one of the police forces that was- we took the call but the way that the telephony works is that if a call is made on a mobile phone it doesn t always go to the area that the accident has happened in- it goes to the place that gets the best signal. So the call was taken by Area X Police, the incident was actually in Area Y police area, we all thought it was in Area Z

7 Interoperability: We didn t have compatible radio channels, we were dealing with three fire brigades, we were dealing with three ambulance services so I think you can see straight away, not only was there complication of the services not being able to talk to each other but then you had got three times three which made it even worse. Different operating procedures, different expectations, different levels of professionalism and it was quite a challenging experience

8 The POSSTT model for information sharing in ad-hoc multi-agency team Technological Dimension Personal Dimension Experience Lack of Perspectives Fear of consequences Organisational Dimension Culture Rules Social Norms Trust Reliability Availability Accessibility Familiarity Complexity Interoperability Information Sharing Social Dimension Language Terminologies Over Classification Primacy of Information Temporal Dimension Time of work Concise Communication Timely Information Time Constraint Environment Spatial Dimension Environment Physical Distance Size of Posting Area

9 What we can learn If people trust each other then confidential information is shared within a group too frequent interaction, training and exercise encourage understanding each other and hence build trust which is necessary for sharing information. Primacy of information was identified to hinder information sharing. When one agency has primacy of information, that agency may be unwilling to share information with others for a better outcome towards information sharing, a common information pool must be designed so that one agency may not have the primacy of information. As different types of technologies are used by different agencies, whenever information needs to be shared, due to the incompatibility of systems, information sharing is hindered policy makers and management of different agencies within the emergency services need to use technology that is compatible with each other

10 What is Big about Big Data? Big volume of data? Big databases (n=all, n ) Petabytes, zettabytes, yottabytes Big complexity of data? Variety of data types (text, pictures, numbers, sounds, etc.) Different sources, qualities, coverage, governance arrangements, etc Different techniques and technologies (Hadoop, NoSQL, Machine learning, etc.) Big questions that the data is expected to (help to) answer? Big ambitions, big vision

11 Gartner Hype Cycle 2014 Big Data Source: hype-cycle-big-data-is-out-machine-learning-isin.html

12 Gartner Hype Cycle 2015 Where is Big Data? See Citizen Data Science, Advanced Analytics with Self Service, Machine Learning... but no big data! Source:

13 Big Data Value Chain: Information Sharing at every stage Problematic Data Assembly Analytics Presentation Action Volume Velocity Variety Visualisation Verification Value

14 Example: The address of every person 75+ in England Problematic What problem could this address? Is 75+ a meaningful category for us? Who else would we need to work with to make it meaningful Data Assembly Analytics How can this data be linked to other data (e.g., PAF, Electoral Register, FRS data) to create value? Who has that data? How will we work with them? What (kind of) model/algorithm will we use to analyse the data? Who do we need to work with on that? Representation Action How do we want to present the results (maps, tables, causal models, etc.?) To whom do we need to present them? Who do we want to work with on that? How will this support action (or help to prevent bad actions)? Who do we need to work with on that?

15 Ten Commandments / a Manifesto The Ten 3-5 Commandments for Big Data in the FRS Thou shalt Thou shalt not A Manifesto for Big Data in the FRS Statement of values A statement of vision (e.g., from each according to his needs, to each according to his means) Minimum Instruction Set (less is more) Imagine you have to carve the words on stone [so there shouldn t be a lot of them]

16 Some (Big) Questions about Information Sharing for Big Data in the FRS Who? Stakeholders, roles, responsibilities What? Problems, Data, Models, (re)presentations, Actions Where and When? Times and Occasions Places and Spaces How and Why? (Business) Process, (Scientific) Method, (Working) Practice, etc.? Business Case, Scientific Theory, Ethical Argument, Political Rationale etc.? So What and Where next? Pay off, Benefit, Risk/Reward, How will you know if it is working? Next Step, Plan, Experiment?

17 Process Each table to work on set of questions and to create at least 3 responses for each heading Each table starts with a different question (see sheets on your tables) After 5 minutes each table pins up its responses and moves to the next set of questions When each table has considered and contributed to all five each participant has two sticky dots to stick on the point that they think is the most important

18 WHO? STAKEHOLDERS, ROLES, RESPONSIBILITIES Who needs to be involved in sharing information for Big Data in the FRS, what should their role/responsibility be?

19 WHAT? PROBLEMS, DATA, MODELS, (RE)PRESENTATIONS, ACTIONS What should be the focus of Information Sharing for Big Data in the FRS (problems, data, models, representations, actions)?

20 WHERE AND WHEN? TIMES AND OCCASIONS PLACES AND SPACES What spaces real and virtual should be used to share information for Big Data in the FRS and what times and occasions should be used to share such information?

21 HOW AND WHY? (BUSINESS) PROCESS, (SCIENTIFIC) METHOD, (WORKING) PRACTICE, ETC.? BUSINESS CASE, SCIENTIFIC THEORY, ETHICAL ARGUMENT, POLITICAL RATIONALE ETC.? How should information be shared for Big Data in the FRS and what are the arguments that are needed to support information sharing?

22 SO WHAT AND WHERE NEXT? PAY OFF, BENEFIT, RISK/REWARD, HOW WILL YOU KNOW IF IT IS WORKING? NEXT STEP, PLAN, EXPERIMENT? What are the pay offs or benefits of information sharing for Big Data in the FRS, how do we know if it is working, what are the key priorities for next steps?

23 Tables Table A Who What Where and When How and Why? So What and Where Next? Table B What Where and When How and Why So What and Where Next Who

24 Tables Table C Where and When How and Why? So What and Where Next? Who What Table D How and Why? So What and Where Next? Who What Where and When

25 Tables Table E So What and Where Next? Who What Where and When How and Why?

26 Prof Rob Wilson Newcastle University Dr Jyoti Mishra Dr James Cornford Faculty of Management & Law University of East Anglia University of Bradford Bradford, BD9 4JL