CeBIT Big Data 2012 - David Cummins, Senior Analytics Manager, PwC
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CeBIT Big Data 2012 - David Cummins, Senior Analytics Manager, PwC

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CeBIT Big Data 2012 - David Cummins, Senior Analytics Manager, PwC CeBIT Big Data 2012 - David Cummins, Senior Analytics Manager, PwC Presentation Transcript

  • David Cummins Senior Manager PwC AustraliaVersion 2 Integrating Big Data into Business2 October 2012 Practice
  • Information advantage is an essential component of buildingcompetitive advantage in any industry Integrating Big Data Analytics into Business Practice
  • Benefits are typically delivered in two ways “The volume and variety of data is rapidly increasing – leading to information overload” “Leading firms to formulate and adopt a comprehensive strategy to leverage data for insights and value” “Analytics is the use of comprehensive datasets together with models, algorithms, statistics and technology to generate insights, support decisions and drive benefits” Drive efficiency Drive growth & productivity & margin “The data comes from the organisations own data repositories, external data sources, or conducting proprietary research ” • Take out costs • Revenue & market share “Leading firms design and build competency in Information • Improve efficiency • Customer profitability Management, Business • Optimise networks • Bundling and segmentation Intelligence and Analytics to embed repeatable solutions and • Improve control • Stop profit leakage drive value” • Workforce productivity • Marketing effectiveness Integrating Big Data Analytics into Business Practice
  • Various forces are driving interest and adoption Pressure on cost In a time of austerity, inefficient processes leading to high costs to generate essential management reports are no longer acceptable. Businesses today are inundated with more data than they can handle, ranging from Explosion of data critical transactional data to social media feeds. Managing and harnessing the power of these sources of information is key Re- balancing of Businesses can no longer rely on “gut feel” and intuition to be successful. Key decisions intuition vs fact need to be supplemented with fact based insight based on deep data analytics Staff are required to make decisions using out-dated information which could lead to Speed to action sub-optimal outcomes. Access to up-to-date information will help drive better decision making and improved operational outcomes Risk and Increasing amounts of external regulation and internal board reporting has placed regulation pressure on companies to deliver complete and accurate reports on a routine basis.101010 Technology Innovations in computing including high performing hardware for data analytics,010101 specialist software applications and mobile handset technology have resulted in a flood of101010 Advancement new technologies hitting the market Integrating Big Data Analytics into Business Practice
  • The type of data that we can exploit has progressively movedfrom structured to semi-structured to unstructured datad ee mr iut T - Social Mediac lu ar et RsnU Sensors Video Audio This creates tremendous efficiency benefits and greatly reduces costs Websitesd Database Averagee n Cost Perr c o i Au i t t Outputt ac a t r eu S trt IS r B e p t s o C C Value of Insight Integrating Big Data Analytics into Business Practice
  • Leading organisations typically deploy analytics capabilityas part of a wider Information Management strategy Source Data Integration Analytics & and Modelling Insights Display Structured Non-Predictive Access Channels Data Warehouse External @ data Internal • Personalised extracts Mobile devices E-mail dashboards Databases http:// • Integration of data across varying • Automated reports sources and formats into a • Ad hoc query analysis • Dashboards, thresholds and trends Printer/Fax HTTP consolidated relational database • Self-service reporting Predictive Audience • Standard published Big Data Unstructured • Analysis of large volumes of Customers Management reports Log files, Documents & unstructured data to social uncover correlations, • Statistical modelling of data to Reports media trends, new facts, forecast outcomes patterns, etc • Scenario modelling to understand cause-effect OperationsNew sources – social media Digitisation – more and more Forward looking – what Hyperconnectivity – aand other beds of data data captured, available and happened is interesting, what growing thirst for data andbecoming available for new, untapped will happen more so... information to be availablericher and more real-time anytime and anywhereinsight Integrating Big Data Analytics into Business Practice
  • PwC Technology Forecast 2012 Reshaping the workforce with the new analyticsIntegrating Big Data Analytics into Business Practice 2
  • Case Study 1: Centralised Marketing Platform /IntegratedMarketing Management • Analysed data from 20 transaction systems processing more than 1 billionClient had tripled its customer base to roughly call detail records (CDR’s) and 200TB of customer information45 million customers in just 5 years. Data • Ran a pilot project for 2-4 weeks over 5 campaigns based on live data tovolumes and complexity proliferated. The trial insight from analysis.marketing team had to transform their • Focussed on the companys medium business segment which is aprocesses to handle: complex, highly fragmented market of 175,000 customers Decentralised and manual marketing • Segmented its mainstream and premium customers intoprocesses microsegments in order to determine the right channel treatment Contractors running manual database and solutions for each customer segment. (Right Price-to-Valuequeries to generate campaign lists proposition) Slow , costly and error prone process thatresulted in large campaign backlogs Campaign lacked relevance and timeliness Duplicated and un-coordinated campaigns • The company now conducts more than 250 campaigns a monthresulting in “ contact stress” for customers • Revenue generated from direct marketing campaigns increased by 25% Customers had become targets of too many resulting from highly sophisticated propensity modelsinitiatives suffering from “ Campaign Fatigue” • Reduced its operation costs by 90% eliminating manual efforts to Performance measurability was limited develop and run campaigns Difficulty determining which campaigns and • Campaign cycle time have reduced from 40 days to 2channels were most effective • Achieved customer retention by 22% over a period of 2 years through targeted intervention • The marketing department can now focus on more strategic tasks such a optimising the planning and execution of more personalised campaigns Integrating Big Data Analytics into Business Practice
  • Case Study 2: Using telematics to manage agriculturalefficiency How it Works • Four Large agricultural companies, Class, John Deere, New Holland and Agco, are now marketing telematics as a way to manage risk and cut costs • All of the machinery such as tractors and plows are outfitted with sensors and are linked to a farm owner or insurance company via GPS • Farmers can monitor data such as how quickly workers are finishing fields, how much field is done, etc • Insurance companies can use the same technology to see how often the machinery is being used, how safely it’s being driven, and how effectively farmers are taking care of their land. This can impact pricing decision and claims management. Integrating Big Data Analytics into Business Practice
  • Case Study 3: Enterprise Wide Customer Centric BusinessTransformationTransition from a business-focused monopoly There was an urgent need to create a customer focused and operationallyto a customer-oriented competitor in a newly efficient organisation i.e. needed an operating model that supported anliberalised market. Market leader with more enterprise wide analytics platform. Key highlights of the solution were:than 85% of the local telephony market. •A new solution infrastructure to focus on customer analyticsServes more than 10million customers and •Created highly sophisticated churn, retention and segmentation modelsemploys approx 25,000 staff that directly tap into the data warehouse The company experienced customer • There was a convergence of marketing, risk and financial data to create aattrition and rapidly declining market share “ Single Customer View”(5-10% of their market share had eviscerated) • Robust and scalable analytical platform that enabled “ Operationalisingas new carriers emerged Analytics” into business processes. Customers were switching carriers and • A clearly defined “Environmental Strategy” for analytics i.e. provisioningmore than a million customers had walked out for operational and discovery analyticsof the door • Use of differentiated hybrid modelling techniques such as predicative Manual paper based, labour intensive models, anomaly detection, social network analysis and user defined costly business practices and no single business rules view of the customer Inability to predict customer behavior, retain long-term valuable customers and measure success • 22% improvement in customer retention rates amounting to a dollar savings of ( US$55M) annually • Exceeded customer retention goal by 47% • Dramatically improved modeling times from 10 hours to 3 minutes • Gained US$50 million in total value through a lift in Integrating Big Data Analytics into Business Practice revenue and reduced customer acquisition costs
  • What should I do next?Integrating Big Data Analytics into Business Practice
  • Understand current business requirements What do your business data consumers expect out of analytics?• There is a risk where analytics is driven out of a strong technical emphasis • a common issue is the focus on delivering a new capability (shiny box syndrome) rather than identifying how it can assist the business • potential to not meet the requirements for the business• Start with understanding the business objectives • mission statement • key performance indicators• Engage early across the entire development life cycle • partner with the business across all stages of the development life cycle• Understanding business objectives and requirements will determine the types of reporting, analyses and predictive models will be required Integrating Big Data Analytics into Business Practice
  • Hype vs Business Value If everyone else is doing it does this mean I should as well?• Differentiate between adoption of new technology that adds value vs following the herd!• Understand the true cost of implementing Big Data • pilot projects are the tip of the iceberg! Consider what it would take to industrialise the pilot• Adopt a fail-fast policy for new technology – do not be afraid of trial and error • State up-front success criteria for any Big Data pilots • Abandon technology or pilots when it is clear that they will not meet your success criteria Integrating Big Data Analytics into Business Practice
  • Identify the new skills required for Big Data • Is Data Scientist the new must- Customer have role? Project Management Solution Architecture Analytics • How can you cross-train or upskill existing team members with Big Data skills? Business Technical • Does the type of analysis require Analysts Data Scientist Delivery the specialist skills? • Don’t be afraid to go externally for specialist skills in order to Data bootstrap your Big Data initiative Testing Infrastructure Analysts Integrating Big Data Analytics into Business Practice
  • Review your IM Strategy• If your IM strategy hasn’t been touched in a year then it is time to dust it off and revisit it • What has to change in order to incorporate Big Data? • How do will you identify and prove the value of use cases? • Does your development life cycle need to change in order to incorporate Big Data?• Ensure that your IM strategy has a plan for data governance that includes reuse and repurpose of data • Big Data creates the ability to merge data sets and key concepts in order to build new insights • Implication is that there is a need to repurpose data for multiple applications • External data sources will require the application of protocols and standards• Benchmark your strategy against comparable industry reference architectures as well as peers Integrating Big Data Analytics into Business Practice
  • Prove Big Data works in your organisation• Engage your business users to find suitable use cases• Determine the value of the insight to the business and the risk of not doing• Identify what constitutes success and what is your exit criteria• Have a plan to transition early success into enterprise readiness Integrating Big Data Analytics into Business Practice
  • In summary...1. Understand where Big Data can be mapped to current business requirements2. Understand what is hype and what can really benefit the business3. Identify new skills required for Big Data and adjust your plan to acquire talent4. Undertake a review of your current Information Management strategy to understand how Big Data is to be positioned5. Prove Big Data can work in your organisation - Identify potential use cases - Design a proof of concept6. Streamline the transition of successful PoC to enterprise readiness Integrating Big Data Analytics into Business Practice