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The Power of Insights - Using Analytics to Create Business Value

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Now is the time to turn the explosion of data into actionable insight in order to achieve competitive advantage. Organizations need to harness the value of data coming from increasingly complex sources, especially the data coming from the new breed of IoT. The use of advanced analytical techniques, such as machine learning and predictive modelling, will reduce operational costs and improve customer centricity as well as increase sales and customer value. It is basically about how data, technology and analytics can deliver a real impact to your bottom line and improve shareholder value.
Speaker:
Naeem Sarwar

Veröffentlicht in: Technologie
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The Power of Insights - Using Analytics to Create Business Value

  1. 1. 0 Copyright 2016 FUJITSU Fujitsu Forum 2016 #FujitsuForum
  2. 2. 1 Copyright 2016 FUJITSU The Power of Insights - Using Analytics to Create Business Value Naeem Sarwar Head of Analytics, Fujitsu Digital BAS, EMEIA
  3. 3. 2 Copyright 2016 FUJITSU Digital is different things to different people Transforming customer & user experience Digitalizing business operations Product leadership & innovation Business-model transformation
  4. 4. 3 Copyright 2016 FUJITSU Fujitsu Digital – our capability Engagement & Incubation Strategic Consulting Digital Applied Technologies Digital Business Solutions Internet of Things Analytics Software as a Service Digital Industry Solutions RetailFinancial ServicesTransport
  5. 5. 4 Copyright 2016 FUJITSU Customers of yester year
  6. 6. 5 Copyright 2016 FUJITSU Customers of today
  7. 7. 6 Copyright 2016 FUJITSU The era of BIG DATA
  8. 8. 7 Copyright 2016 FUJITSU Customers Sharing Data 82 You have checked in at Train Station 189 161
  9. 9. 8 Copyright 2016 FUJITSU The New Consumer EMPOWERED HYPERCONNECTED OPINIONATED HIGHLY VOCAL
  10. 10. 9 Copyright 2016 FUJITSU New Breed Of Data – IoT
  11. 11. 10 Copyright 2016 FUJITSU Shifts in the ecosystem are driving advanced analytics… Big Data: real-time analytics of in-flight transitory data Human Centricity1.  Consumers now demand to be placed at the centre of the organisation.  Organisations are looking to improve productivity and staff morale.  Workforce are demanding agility in their own working life and the use of technology to make their jobs easier. New Channels & Data2.  Emergence of new channels is creating significant data deluge.  A wider range of connected devices – the ‘internet of things’ -will contribute to ever growing quantities of data. Operational & Asset Management3.  Organisations are now looking to use Big Data to extract value from IoT and move towards a more proactive maintenance model and prevent instead of detect.  Through cutting edge analytics and platforms, organisations now have the ability to deploy strategies in real time. Complexity of Interactions4.  The growing complexity of interactions between marketing channels is proving difficult to navigate.  A convergence of marketing, analytics and technology will help drive effectiveness across every channel.
  12. 12. 11 Copyright 2016 FUJITSU Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Deployed across multiple markets Financial Services and banks Retail Telecommunications and utilities Insurance Healthcare Travel Leisure and media Automotive Public sector How can we make it happen? What will happen? Why did it happen? What happened? Difficulty Value Advancement in analytics Data leads to decisions, actions & enablement… Business Value ComplexityofData Next generation analytical techniques that enable you to move from descriptive to prescriptive analytics
  13. 13. 12 Copyright 2016 FUJITSU The Big Data Landscape
  14. 14. 13 Copyright 2016 FUJITSU Data Strategy/Scoping Platform/ Architecture Advanced Analytics Visualisation WhatIsIt/Howdowedoit? Business Problem analysis Enterprise Architecture Descriptive and Predictive Real-time Requirement Engineering Solution Architecture Clustering Dashboards Workshops Platform Transformation Time Series Automated reports Interviews Data Integration Network Analytics Business User Access Data profiling Data Enrichment Recommendation Engine Querying Initiation & Scoping Linkage and Matching Classification Location & Geo Spatial Deep Learning Machine Learning Big Data – Fujitsu’s Expertise and Alliances
  15. 15. 14 Copyright 2016 FUJITSU Big Data Value Chain – Execution & Deployment Input can be structured data (from traditional database systems), unstructured data (such as image data, Twitter updates, online reviews, location data) or data from web- or network-connected devices such as weather sensors. Data is analyzed and filtered – “crunched” – to turn it into meaningful intelligence. These insights can be deployed in various ways – e.g. be mined by business users via visualization tools or reports, or serve to trigger automated actions in production. 1 2 3
  16. 16. 15 Copyright 2016 FUJITSU Fujitsu Proposition and Capabilities Strategy & Approach SMART Technology SMART People SMART Data SMART Themes Detailed strategy of what we need to have and what we need to do with a tried and tested approach to deliver Comprehensive themes that tackle customer lifecycle and IoT analytics. Built around solving business problems NOT IT Extensive toolkit of for technology enablement and platforms where we will work with you to deploy the most effective solution into your business Without people we can not deliver. The practice is staffed by MSc / PhD data scientists and architects as well as consultants that understand your business issues with over 50 years experience Data is fundamental and acts as a USP with the ability to provide that 360 view – Access to the most comprehensive datasets. Consisting of 857 M individuals and 500+ attributes Analytics Centre of Excellence that drives cutting edge innovative analytical solutions to solve business problems by leveraging Fujitsu and its partners’ technologies (via a consultative, collaborative Think big, start small approach).
  17. 17. 16 Copyright 2016 FUJITSU Business Objectives & Strategy Understanding the business problem to assess what data will be required as a starting point – Consultative Approach Assess Current Capability Detail the current picture in terms of data, sources, process Design Future Capability Create both a vision, architecture and roadmap of future capability, ordered by anticipated return on investment Valuing and Building the Data Asset What data do you need and what is going to deliver the solution The Business Case Produce a high-level business case and implementation plan to unlock the benefit streams identified Build Strong Partnerships Fujitsu’s Approach and Methodology
  18. 18. 17 Copyright 2016 FUJITSU Comprehensive partner agreements allowing access to data across 26 countries consumer classification that connects to over 2 billion consumers and approximately 857 million households Global Data Reach
  19. 19. 18 Copyright 2016 FUJITSU Analytics and Propositions  Make your data and infrastructure fully optimised and fit for analytics and marketing purposes to drive your strategy  Add forensic insight to your business by understanding and targeting customers and prospects for maximum return  Identify the best locations for your stores and what to stock in them.  Comprehensively understand footfall patterns throughout your store  Have the ability to serve tailored communications to your consumers digitally in real time whilst they are browsing on line or in store  Is your business geared up to be best in class to identify fraudulent clams utilising a 360 view of your applicants across big data sources  Improve operating cost efficiencies by utilising big data and IoT across your infrastructure and predictively identify problems before they start.
  20. 20. 19 Copyright 2016 FUJITSU • Consultancy and analytics engagement • Tailored prioritised data driven strategy to optimise collections strategy and enable individuals to enrol in a payment plan, placing the customer at the heart of the business • Management and development of customer behavioural, value segmentation models, propensity to pay models and key performance indicators Outcomes • Client can make knowledge based decisions • Identification of customer ability to pay, payment plans and contact strategy across best channels • Ability to launch new tailored products • Increase in productivity and efficiencies across customer contact • Collections rose £12.7m in 9 months Response • Leading debt collection agency was not aware of customers not on a payment plan due to a lack of data driven strategy • Business diagnostic we helped identify that 1.5m individuals were not in any payment plan which equated to £2bn of uncollected debt Situation Financial Services Optimising Collections Strategy
  21. 21. 20 Copyright 2016 FUJITSU • Working in partnership with the agency Fujitsu developed a PoC predictive model geared to predicting fraudulent claims at point of application to prove the robustness of the model • The predictive model utilised a vast array of data attributes using agency data as well as third party data Outcomes • The initial PoC model identified an uplift on fraudulent/erroneous claims of 25% • Now in full BAU and runs in a dynamic automated real time environment, preventing fraud from entering into the system • Identification of more than £85m per annum of fraud and error activity via the solution • On average 13% of new applications were subject to fraud and error Response • Agency needed to reduce and understand level of fraud. Move towards a prevention and detect strategy at the point of application • Improve monitoring and evaluation to ensure resources focused on areas of greatest financial loss and risk • Need to quickly identify incorrect cases and deploy the appropriate follow up actions Situation Government Reducing fraud and error
  22. 22. 21 Copyright 2016 FUJITSU • Roadmap identifying current capabilities and how to deliver best in class analytics identified • Developed a data strategy for hygiene, enhancement and a single version of the truth • Utilisation of data for predictive maintenance • Streaming of data in a near real time • PoC predictive models across a number of assets • Real time social media listening linked to call centre management Outcomes • Improved Customer Experience Scores • Increased knowledge share across internal team through utilisation of self serve analytics, single version of truth for data, analytics in a real time environment, effective dash-boarding and reporting on KPI’s • Reduction in operating costs • Enablement of cross channel communications to customers Response • Needed to improve Customer Experience Scores and customer centricity • Increase engineer productivity • Move from scheduled maintenance programmes to conditional based programmes via advanced analytics • To enable the internal data and analytics teams to be best in class Situation Utilities company
  23. 23. 22 Copyright 2016 FUJITSU A few Quotes… In 2016, more businesses will see that customer success is a data job. Companies that are not capitalizing on data analytics will start to go out of business The true value of analytics will be realized when ROI is maximized by analytics that tell you what to do. Today, most analytics projects start from the wrong place, end too soon, take too long – and still fall short. The reason: they start with available data sources as the primary constraint. The solution: start with the business questions you want to answer Companies will continue to seek competitive advantage by adopting new big data technologies Technologists will shift their attention from Big Data to machine learning and providing proactive insights. Active intelligence will become the new focus Automated personalization will be a critical business benefit that big data analytics will begin to deliver Companies … will take a more thoughtful approach to analyzing “useful” data to reach fast, meaningful, holistic insights. Rather than investing time and money in IT infrastructure to manage high volumes of dataData itself is no longer the number one problem; connected data is the problem. It is becoming increasingly difficult to reach that data, secure that data, much less draw insight and enable a person or process to take action on the data
  24. 24. 23 Copyright 2016 FUJITSU

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