Uncovering the hidden wealth in your data for enhanced decision making.
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Presentation on the role of various types of data (Social + Transaction + Device = BigData) with a focus on Social in service delivery. Case studies and examples. This presentation was part of the Feb 2013 - Measuring Service Delivery conference in Canberra.
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Uncovering the hidden wealth in your data for enhanced decision making.
- 1. Measuring Service Delivery 18 – 19 February 2013 Uncovering the hidden wealth in your data for enhanced decision making Dheeraj Chowdhury Principal Consultant – Business Platforms Infosys Australia & New Zealand (Former Group Leader Digital Media – NSW DEC)
- 2. Agenda •Data for deeper insights and informed decision making process •Tools and techniques •Best practice lessons
- 3. In GOD we trust. Everyone else, bring DATA
- 5. Australian Government (DPMC) – Service Delivery Source: http://www.dpmc.gov.au/publications/aga_reform/aga_reform_blueprint/part4.1.cfm
- 6. Australian Government (DPMC) – Service Delivery Source: http://www.dpmc.gov.au/publications/aga_reform/aga_reform_blueprint/part4.1.cfm
- 7. Australian Government (DPMC) – Service Delivery Source: http://www.finance.gov.au/publications/delivering-australian-government-services-access-and-distribution-strategy/principles.html
- 8. Data and Productivity: Potential Source: http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation
- 9. Data
- 11. Why and What DATA Source: Infosys
- 13. Sources of data Source: Infosys
- 17. Do‟s and Don'ts
- 18. Ride the elephant Source: Infosys - http://www.infosys.com/art-and-science/pages/index.aspx
- 20. STOP
- 26. v v v v v
- 27. v v v
- 28. v
- 31. Data vs Reporting It happens again and again. And again. And…again! It goes like this: • Someone asks for some data in a report • Someone else pulls the data • The data raises some additional questions, so the first person asks for more data. • The analyst pulls more data • The initial requestor finds this data useful, so he/she requests that the same data be pulled on a recurring schedule • The analyst starts pulling and compiling the data on a regular schedule • The requestor starts sharing the report with colleagues. The colleagues see that the report certainly should be useful, but they‟re not quite sure that it‟s telling them anything they can act on. They assume that it‟s because there is not enough data, so they ask the analyst to add in yet more data to the report • The report begins to grow. • The recipients now have a very large report to flip through, and, frankly, they don‟t have time month in and month out to go through it. They assume their colleagues are, though, so they keep their mouths shut so as to not advertise that the report isn‟t actually helping them make decisions. Occasionally, they leaf through it until they see something that spikes or dips, and they casually comment on it. It shows that they‟re reading the report! • No one tells the analyst that the report has grown too cumbersome, because they all assume that the report must be driving action somewhere. After all, it takes two weeks of every month to produce, and no one else is speaking up that it is too much to manage or act on! • The analyst (now a team of analysts) and the recipients gradually move on to other jobs at other companies. At this point, they‟re conditioned that part of their job is to produce or receive cumbersome piles of data on a regular basis. Over time, it actually seems odd to not be receiving a large report. So, if someone steps up and asks the naked emperor question: “How are you using this report to actually make decisions and drive the business?”…well…that‟s a threatening question indeed! Source: http://www.gilliganondata.com/index.php/2012/02/22/the-three-legged-stool-of-effective-analytics-plan-measure-analyze/
- 36. 36 Infosys Approach Pre-built transformers for data transformation and cleansing Graphical easy to use User Interface with drag and drop features for configuring data pipelines One-Click Cloud Deployment - Seamless Analytical Cluster Setup, Configuration Metadata driven Data Ingestion Framework with Pre-built Adapters Industry leading Visualization techniques for deep insights Integration with wide variety of industry solutions Comprehensive & easy to use Analytical & Machine Learning algorithms support Full Featured Hub Management Pre-built components for Stream Processing & Real Time Analytics Best Practice - Approach
- 37. Case studies
- 38. Service Delivery – Data = UK Public Sector Source: http://www.policyexchange.org.uk Estimated Savings £16 – £33 billion
- 39. Service Delivery – Data = Value proposition Source: http://www.policyexchange.org.uk
- 40. Service Delivery – Data = Value proposition Source: http://www.policyexchange.org.uk
- 41. Business operations transformation Challenge Inability to determine the “total” liability of the borrower Solution Business Value Establish risk exposure connections using „Record Linkage‟ algorithm Pre-built information sources to both internal systems and external sources significantly improved the accuracy of risk exposure calculations. Agility for insights and actions: 4 weeks vs. 4 months. Real-time discovery: Uncovered hidden exposures for 43% of accounts 41 Risk exposure „hidden‟ and spread across various disconnected levels. Borrowerriskexposureanalysis Industry Financial Services Revenue $8+ Billion Employees 25,000+
- 42. Service Delivery – Data = Value proposition Source: http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation
- 43. Big Data - Launch event 43 Doug Cutting Chief Architect, Cloudera S. D. Shibulal CEO and Managing Director, Infosys Vishnu Bhat VP and Global Head – Cloud & Big data, Infosys Featured Speakers Global Live Streaming (simulcast) of the launch event will be available Event highlights 50 clients and prospects from Global 2000 The future of big data Doug Cutting, Chief Architect, Cloud era Executive keynote S. D. Shibulal, CEO and Managing Director, Infosys Moderator Vishnu Bhat, VP and Global Head – Cloud and Big Data, Infosys Panelists Doug Cutting, Chief Architect, Cloudera Robert Stackowiak, Vice President, Big Data & Analytics Architecture, Oracle 2 Clients/Prospects Unlocking the business value of big data Panel discussion REGISTER NOW for the simulcast
- 44. References • Embracing the Elephant in the Room • Big Data Spectrum • The Big Data Opportunity • Infosys – Art and Science • Big data: The next frontier for innovation, competition, and productivity
- 45. THANK YOU www.infosys.com Dheeraj Chowdhury Principal Consultant – Business Platforms Infosys Australia & New Zealand m: 0412107479 e: dheeraj_chowdhury@infosys.com twitter: dheerajc .
Editor's Notes
- Uncovering the hidden wealth in your data for enhanced decision making Gaining deeper insights with data analysis to inform your decision making processTools and techniques for data analysis – from simple to sophisticatedBest practice lessons from the private sector and international governments Dheeraj ChowdhuryGroup Leader – Digital Media, Business ServicesDepartment of Education & Communities, NSW
- No one way to solve this challenge
- No one way to solve this challenge
- Before we start lets briefly look at:Why social mediaWhy social media metrics
- Before we start lets briefly look at:Why social mediaWhy social media metrics
- Before we start lets briefly look at:Why social mediaWhy social media metrics
- We found three categories of benefits from the use of big data in public sector administration:1. Operational efficiency savings. We applied the percentage of potential operational cost savings to estimated addressable European OECD government expenditure (net of transfers).2. Reduction of cost of fraud and errors. We applied the percentage of potential fraud reduction to estimated addressable European OECD government transfer payments by multiplying the percentage of transfer payments. The estimated addressable transfer payment took into account the percentage of transfer payment that has a non-negligible amount of fraud and error and the estimated percentage of the cost of fraud and errors.3. Increase in tax revenue collection. We applied a percentage potential
- No one way to solve this challenge
- Before we start lets briefly look at:Why social mediaWhy social media metrics
- Before we start lets briefly look at:Why social mediaWhy social media metrics
- No one way to solve this challenge
- Before we start lets briefly look at:Why social mediaWhy social media metrics
- To put it perspective – multi channel, multi formatNo longer is the discussion - Are these going to stay?Is Facebook going to be around or is it going to be like MySpace?A show of hands who is using what
- this is the opportunity
- Demographic DataThis data types enables an effecient way to create context about consumers, yet broad survey-based research may not yield specific nuances and needs about specific individual taste as today’s consumers are given more choices and have more discrete needs. Some marketers are able to glean demographic data from social accounts gender, age range, by profile data, profile pictures, or searching public records like Zabasearch and Spokeo.Product DataA data type commonly used in ecommerce websites, this data type is used to match similar products with each other, in order to cross-sell and up-sell products. Often combined with demographic data, this data type, mixed with referral and behaviorial data yields greater accuracy. Visit any ecommerce website from Amazon, BestBuy and beyond to find examples of product matching.Psychographic DataAs the social web exploded in the past few years, consumers are volunteraily self-expressing their woes, pains, and aspirations in websites. This provides those who want to reach them increased opportunities to market based on lifestyle, painpoints, beyond just product sets. This data type is useful in both message and conversation creation as well as identifying features and products to improve or fix. To learn more about lifestyle and pain point positioning see the 5 stages of positioning by Lifestyle, Pain, Brand, Product, or Features.Behavioral DataThere’s at least two ways to find this data, it’s in both existing customer records like CRM or ecommerce systems or also in the “digital breadcrumbs” that users are leaving in social networks using a variety of web techniques from cookies, FB connect, and other social sign on technologies. The opportunity to suggest content, media, deals, and products to them that matches their previous behaviors will yield a greater conversion.Referral DataCustomers are emitting their recommendations for products, but positively –and negatively. Both explicitly through ratings and reviews, as well as implcity though gestures like the ‘like’ button to their social network. Vendors like Bazaarvoice (disclosure: client) offer a suite of tools for customer feedback and intelligene, Zuberance fosters positive WOM through positive ratings, and ExpoTV is a catalyst for conversation using video reviews, and see the well known case study from Levi’s who implemented the Facebook Like button.Location DataAs location based technology and services emerge for consumers to emit signals where they are using mobile devices, this data helps to triangulate context around location and time for brands to reach them. From Foursquare checkins and the associated contextual ads that emerge to ‘players’ to Facebook places, consumers can now emit their location, in exchange for contextual information, see how Awareness Hub (client) is able to surface influencers by location in Foursquare Perspectives.Intention DataThe most innacurate, this volatile data type holds great opportunity to predict what consumers will do in the future. Wish lists, social calanders like Facebook Events, Zvents, and aspirational websites like PlanCast, 43 Things allow consumers to broadcast their future plansSavvy marketers will harness explicit content and serve up the right messages in advance – as well as poach from competitors. Learn more about intention data –which is faster than real time.
- No one way to solve this challenge
- See this as an opportunity
- Very easy to jump into measurement
- Very easy to jump into measurement
- No one way to solve this challenge
- PeopleProcessTechnolgy
- No one way to solve this challenge
- Before we start lets briefly look at:Why social mediaWhy social media metrics
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