Connecting data driving productivity and innovation

Transcription

1 Presentation to RDA Europe Policy Meeting on: Weaving the Internet of Data 6 th April 2016 Connecting data driving productivity and innovation Professor Jim Norton FREng FIET Chair Digital Systems Engineering Community of Practice UK Royal Academy of Engineering

2 Areas to be covered Why is broad exploitation of Data Analytics important? What UK economic sectors did we explore? What engineering challenges did we identify? What key recommendations did we make?

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4 Cumulative benefits of big data: 216 billion for (CEBR)

5 The sectors Advanced manufacturing Built environment Transport

6 The sectors Energy Healthcare Aerospace and defence Insurance

7 The workshops What are the economic and other benefits of big data? What progress has been made by sectors and organisations? What are the future opportunities? What are the barriers to success, both sector-specific and universal?

8 The Engineering Challenges: Data, Analytics and Systems ensuring good quality data and analytics visualisation extracting information from data dealing with volumes of data / heterogeneous data data legacy and security managing the uncertainty of complex systems data standards These challenges are present across all sectors

9 Visualisation Understanding the user and the decisions they will make based on analytics is key

10 Quality data good data governance is essential an information engineering approach the challenge of acquiring and preparing data is considerable data quality depends on bias, timeliness, granularity, metadata The ability to link reliably datasets that have originated in different contexts is often a challenge. Quality analytics quality of analytics depends on: - quality, availability and appropriateness of data - correctness of the algorithm - issues around software development developers need to work closely with users experimentation with real data during development is key to success analytics should be evaluated throughout the development process

11 Dealing with volumes of data Data and acting on information it provides has the highest value within a certain timeframe The engineering skill lies in deciding the rules for its selection The role of machine learning? A trade-off: throw away data and lose valuable information or keep unmanageable quantities of data Dealing with heterogeneous data Soft data from social media and hard data from physical systems will be increasingly available Combining data in meaningful ways is a challenge Understanding the nature of questions that can be asked is a first step developing tools follows

12 Data and system legacy applications of data and its sensitivity change over time metadata that lasts over time or data migration? a robust security, classification and governance model system components stored digitally future access? Security security in data-driven systems is paramount...but numerous products are unfit-for-purpose malicious access to data is also a risk

13 Managing the uncertainty of complex systems When you use complex communicating sets of machines, you don t know what that world looks like Denton Clutterbuck FIET, Altran UK Integrity / assurance of data, devices and systems is crucial Complex, integrated systems will be created, by design or accident How can systems be certified if their behaviour is unpredictable? How should the risks be managed? Graceful degradation for interconnected systems?

14 Standards for data Interoperability unleashes innovation John Scott FIET, IET Energy Policy Panel a national strategy for standards? standards applicable across sectors data concept models, data taxonomies, data integration, data privacy, security and accessibility + ICT UK must retain first mover advantage Good quality metadata allows datasets to be curated and used well beyond the applications and sectors for which they were originally collected.

15 Conclusions The UK is strongly placed to develop a leading dataenabled economy The area is still immature, with great potential for innovation and value generation There are opportunities to combine data across sectors in innovative ways, and to use novel types of data But Technical, organisational, regulatory and legal barriers are present The right conditions need to be in place for innovation

16 Recommendations in 6 key areas Strengthening the foundations Enhancing the infrastructure Creating markets in data Establishing methodologies for the formal valuation of assets Sharing best practice Developing multidisciplinary skills

17 Strengthening the foundations A growing level of interconnecting and interdependence of supporting systems with vulnerabilities Best engineering practices are vital Recommendation 1: Infrastructure regulators, professional bodies and standards bodies need to create an enabling structure that promotes innovation and ensures safety and resilience. Subsequent to the report publication, an Academy workshop on Strengthening Digital Systems and Enhancing Resilience was held on 21 st March 2016.

18 Enhancing the infrastructure Ubiquitous access to fixed and mobile broadband services is a prerequisite of a data-enabled economy Competitor economies are implementing EU targets Some sectors are particularly disadvantaged by their rural locations Recommendation 2: DCMS and Ofcom should explore extending the Universal Service Obligation structures to broadband access. EU target of 30 Mbits/sec download speeds should be matched. Subsequent to the report publication, the UK Government is consulting on the appropriate parameters for a universal broadband service obligation.

19 Creating markets in data Open data initiatives by government are welcome first steps towards a data-enabled economy Much potentially valuable data remains locked away in corporate silos or within sectors Recommendation 3: Multidisciplinary groups should explore mechanisms to trade engineering data up and down supply chains. Standards will provide a foundation for data capture, trading and re-use. Subsequent to the report publication, The UK Government Bean Review of economic statistics also identified this as a key area to address

20 Establishing methodologies for the formal valuation of data assets Many information age companies have almost no reported physical assets UK assets are being underestimated Poor decision-making on how best to develop, trade, protect and exploit such assets. Recommendation 4: The Academy should investigate data valuation methodologies with relevant stakeholders. Subsequent to the report publication, a workshop with all the key stakeholders will be held on 28 th April 2016.

21 Sharing best practice There are areas of excellent practice and innovation across all sectors Substantial improvements could be realised by sharing best practice Recommendation 5: Sector Catapults should develop and implement mechanisms for sharing best practice focusing on SME s. Professional networks could provide advice on best practice codes and guides.

22 Developing multidisciplinary skills A multi-skilled workforce is needed to convert data analytics theory into business practice and performance Engineers with data science skills and specialist data scientists are in short supply Recommendation 6: The Academy and the IET should convene a group of professional bodies to recommend changes in university education and CPD to address the new skills requirements for individuals and teams.

23 Hopefully, if we can successfully address these challenges, Cinderella can come to the ball

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