Demystifying Big Data. James Rawlins Senior Consultant

Transcription

1 Demystifying Big Data James Rawlins Senior Consultant

2 What s the Big Idea? Objectives for the session Understand what is meant by Big Data Provide some context around what is Big Are we big? Understand how big data is used? Recognise the challenges See some practical applications Leave knowing more than when we came in

3 What does Big Data mean? Definition 1. big data - noun [mass noun] Computing Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. Definition 2. Big data is a popular term used to describe the exponential growth and availability of data.

4 Every day, we create 2.5 quintillion bytes of data (2,500,000,000,000,000,000,000,000,000,000 bytes) 90% of the data in the world today has been created in the last two years. Data storage capacity is typically increasing by 60% year on year This data comes from everywhere: banking transaction records, posts to social media sites, purchase transaction records, mobile phone GPS signals to name a few. How big is BIG? This data is big data.

5 Big in context 2014 Newspaper circulations Newspaper Circulation The Sun 2,213,659 Daily Mail 1,780,565 Daily Mirror 992,256 Daily Telegraph 544,546 Daily Express 500,473 Daily Star 489,067 The Times 384,304 I (Independent) 298,266 Financial Times 234,193 The Guardian 207,958 The Independent 66,576 England & Wales 2011 census Local Authority size Population Count % Small Up to 100k % Medium 100k - 250k % Large 250k - 500k 50 14% Very large 500k+ 7 2% Customer Interactions: Applications & Service delivery etc. Telephone Counter Visit Snail mail post Website & e-channels Financial transactions to name a few This is our big data

6 Is Big Data important? Big data may be as important to business and society as the Internet has become. Why? More data could lead to more accurate analyses. More confident decision making Better decisions Greater operational efficiencies And cost reductions And reduced risk

7 What can Big Data do? The goal is to be able to take data from any source, harness relevant data and analyse it to find answers. Business applications have clear parallels with public sector objectives Determine root causes of failures, issues and defects in near-real time Optimize routes for many thousands of package delivery vehicles while they are on the road. Send tailored recommendations to mobile devices while customers are in the right area to take advantage of offers. Recalculate entire risk portfolios in minutes. Use clickstream analysis and data mining to detect fraudulent behaviour. Quickly identify customers who matter the most.

8 3 Big Steps Access the data Understand what it means Action It s not the size of the data that s important. It s what you do with it that matters.

9 The 3 Vs of Big Data 1. Volume Unstructured data difficult to use (e.g. social media) Decreasing storage costs makes keeping it easier How to determine relevance within large data volumes? How to use analytics tools to create value from relevant data 2. Velocity Data is streaming in at unprecedented speed and must be dealt with in a timely manner Driving the need to deal with torrents of data in near-real time Reacting quickly enough to deal with data velocity is a challenge 3. Variety Data today comes in all types of formats. Structured, numeric data in traditional databases From multiple sources Unstructured text documents, , video, audio and financial transactions Complexity: Managing, merging and governing different varieties of data is challenging. You must connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.

10 Big Data logistics Systems Privacy Governance Storage Security Cloud Do you capture what you need? Can you extract what you need? When you need it? Policies and practices around privacy and data protection, safeguarding reputation and insight Clearly control how information is created, shared, cleansed, consolidated, protected, maintained, retired and integrated. Big enough, cheap enough. Strong security measures built in to guard your organisation against internal and external threats. Host big data and analytics solutions in the cloud. Achieve the scalability, flexibility, expandability and economics into the future.

11 Big Data in the Public Sector Public Sector already uses lots of data e.g... Unemployment data used in regeneration and programmes like Communities First Age Profile of City 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% Local Authorities use demographic data about the population to plan things like school places and adult services 0.00% Increasing Age Profile % Leeds Postcodes % Core City Postcodes

12 Big Data in the Public Sector Leeds profile compared to Core Cities and UK averages Segmentation data can add depth to our understanding of our community Communial Properties Poorer Council Tenants Including Many Single Parents Poorer Family & Single Parent Households Poorer White & Blue Collar Workers Less Affluent Singles & Students In Urban Areas Less Affluent Family Neighbourhoods Comfortable Mixed Tenure Neighbourhoods Suburban Homeowners In Smaller Private Family Homes Affluent Home Owning Couples & Families In Large Houses Wealthy Neighbourhoods Nearing & Enjoying Retirement Leeds has fewer Poorer & Single Parent Households than other Core Cities - changes to things like benefits will have different effect Affluent Singles & Couples In Exclusive Urban Neighbourhoods 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% Leeds Core City Average UK Average Average Credit Score By Band 25.00% 20.00% 15.00% 10.00% 5.00% Leeds citizens have a relatively high average credit score they will be better placed to ride out economic downturns 0.00% Under to to to to to to to and Over Leeds Birmingham Bristol Liverpool Manchester Nottingham Sheffield

13 Big Data in the Public Sector CIG hold, manage and supply all kinds of data sets including demographic segmentation data, personal data, public domain data (not all of it available for all uses) A UK Local Authority is using CIG demographic segmentation data to inform a volunteering pilot for maintaining parks and green spaces

14 Civica and Coactiva Solutions in partnership

15 Risk Based Verification New Claims Predictive data analytics determine the level of risk of Fraud and Error present in new applications for Housing Benefit and Council Tax Support Changes in Circumstances Predictive data analytics determine the level of risk of Fraud and Error present in notified changes in circumstance for Housing Benefit and Council Tax Support CRA enhancements Credit Reference Agency data combines LA data with detailed financial information to improve accuracy of predictive outcomes FERIS RBV In Claim Predictive data analytics and data matching identify the highest risk cases of unreported change and early detection of fraud and error, generating high risk target accounts to enable effective distribution of resources to maximise DWP incentive payments. Civica enable the risk score to populate the Open Revs system enabling access to actionable intelligence from the data held.

16 Single Person Discounts / Empty Homes Civica is enhancing the revenues & benefits services already provides to local authorities with Coactiva's ThreeSixty Council Tax View and ThreeSixty Empty Homes solutions. ThreeSixty Council Tax View enables local authorities to detect fraudulent single persons discount at point of application and across existing claimants. Provides granular risk classifications within simple Red, Amber, Green status provides a complete view of the household including a residency score for each potential cohabitant monitors existing claimants for change of circumstances ThreeSixty Empty Homes identifies empty homes that can be brought back into use or have recently been reoccupied. Gone away and forwarding address links are returned with accurate move in dates A complete view of household residency is built from our Proprietary ResidencyPLUS technology

17 Retriever Retriever has a unique single search entry box similar to that of popular search engines, making it easier for the trace agent to copy and paste the information they know into a single field. Retriever uses big data searching technology and knowledge graphs to establish connections between people, places and things such as mobiles, s and landlines. Results are ranked according to best-fit, with the most relevant results to your search appearing at the top, enabling trace agents to make more informed decisions. Retriever does not leave a credit search footprint on a consumer s credit report. 2 minute video : Retriever Authorities can procure Retriever using their existing Civica contract.

18 Future projects DataDNA DataDNA is a unique and persistent identifying number for each adult individual in the UK even if you hold different addresses or names for that individual across each data source, DataDNA enables a single view of your citizen or debtor. ThreeSixty Connections A data management and analytics platform with a real time interface to Callcredit s data bureau giving ordinary users easy access to actionable intelligence in specific business areas. ThreeSixty API Civica is working with Coactiva to integrate the ThreeSixty API into various back-office systems enabling real-time access for Identity, Tracing, Validation of Circumstances, Validation of Household and residency, indebtedness & vulnerability indices. Cameo Public Sector Understand ability and attitudes for aspects such as technology usage or financial literacy Establish household composition, ethnicity, educational attainment level and age profile Select the right channel, with the right message to cut your costs and improve results Aid local planning, such as allocating housing stock or optimum service delivery site points CallMonitor Up to 250 proactive alerts on changes of circumstances notified on a daily basis including household move ins/outs, new CCJs, missed payments, balance changes etc CallValidate 3D CallValidate 3D provides challenge and response multiple-choice questions for sensitive transactions. The questions are fully configurable and are intelligently generated on the fly These questions are designed to ensure that a genuine consumer will know the answers but fraudsters will not

19 Big Data, Big Conclusion Big data is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an incredible velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills. We can help.