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Get your data analytics strategy right!

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Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.

Published in: Data & Analytics
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Get your data analytics strategy right!

  1. 1. 1 Get Your Data Analytics Strategy Right!Get Your Data Analytics Strategy Right! Date: May 21, 2014, 12 pm EDT Sponsored by: SPAN Systems Corporation Produced and Presented by: The Outsourcing Institute
  2. 2. 2 Today’s Speakers Stephanie Blackwell, Director of Technology, ScanSee Ram Mohan, Advisor - Strategy, SPAN Infotech (India) Pvt. Ltd. Somashekara T. S, (Soma) Director - BI and Database Services, SPAN Infotech (India) Pvt. Ltd.
  3. 3. 3 The Outsourcing Institute • Located at outsourcing.com – Over 70,000 Executive Members Globally • Trends, Best Practices, Case Studies • Training Through OI University • Specialize in Low Cost Alternatives for Outsourcing Buyers Needing Assistance with RFP Development and/or Vendor Selection: – Outsourcing RFP Builder Software – Matchmaker Service • Qualified Demand Generation Programs • Outsourcing Jobs Opportunities and Recruiting Services Through CMS Inc. • Local, Intimate and Interactive Outsourcing Road Show • Sponsorship and New Business Development Opportunities & Programs For more information contact us at: info@outsourcing.com or 516-279-6850 ext. 712
  4. 4. 4 Topics
  5. 5. 5 Data Everywhere Traditional Enterprise Data Edge of the Enterprise External Data Mobile Cloud Data Aggregators Data from Partners Internet of Things Structured News and Journals Social Media Review User Generated Data
  6. 6. 6 Trend of Analytics New (with Analytics and Business Intelligence)  Optimization  Predictive Modeling  Forecasting / Extrapolation  Statistical Analysis  What is the best that can happen?  What will happen next?  What if these trends continue?  Why is this happening? Traditional  KPIs / Alerts  Query / Drill down  Adhoc Reports  Standard Reports  What actions are needed?  What exactly is the problem?  How many, how often, and where?  What happened? INSIGHTS INFORMATION DATA ACTIONS DESCRIPTIVE PRESCRIPTIVE OPERATIONS INNOVATION
  7. 7. 7 The Analysis Gap Analysis Gap Ability to Analyze Volume Variety Velocity Almost 2/3Almost 2/3rdsrds of the Analytics Projects Fail To Meet Expectationsof the Analytics Projects Fail To Meet Expectations
  8. 8. 8 The Analytics Journey – Mind The Gaps
  9. 9. 9 The Analytics Life Cycle Business Consulting ( Domain / Enterprise Understanding, Need Definition, Industry Trends) Data Engineering ( Technology Roadmap, Integration, Cleansing, Organizing, Visualization) Analytics Modeling Business Operations Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Understanding the business need / vision Relevance, Readiness and Preparation of the existing data In-depth study to identify the influencers from the data to achieve business vision / need Derive and Evaluate the right Analytics Model Test the model for accuracy and tune it Productize the Analytics Model Common Pitfalls  Treating as an IT Initiative  Not having the Right Resource  Taking a Big Bang Approach
  10. 10. 10 Challenges Where to Start and Stakeholder Buy-in Data Availability and Relevance Readiness for Initiative  Utilization of Data Aggregators  Provide access to Trial Users  Prepare the data  Where to start? - Pick up the Relevant / Demand  Educate the stakeholder using the bottom-up approach  Analytics complements the business  Executive sponsorship  Competition
  11. 11. 11 Challenges Human Resources Process Resource  Data Scientist - Utilize Statistics, Product Specialist  Agile method – Involve, Evolve and Improve (IEI)  Realign to the goal of the project at every step  Tools have eased analytics  External expertise
  12. 12. 12 Challenges  MS-Excel could be the right fit  Adopt cloud wherever possible to reduce cost  Technologies have evolved to extract information from compressed data, in memory  Using a minimum of 2 technologies before you decide to address the memory, visualization and processing needs Technology Investment Multiple Choices of Technology In-memory Analytics, BI Tool and specialized Analytics Tool Columnar Database and Appliance Hadoop Technologies High Processing Machine and Lesser Cost Cloud Infrastructure
  13. 13. 13 Challenges Data Privacy and Security Protection  Trusting the Model  Presenting the Value of the Model Governance  State the goal of the project and the assumptions  Fitment of the model using “hold out data” etc.  Explain the value of the Analytics Project in Business rather than in statistics / technical terms.  Don’t claim a “Magic Bullet” - State the Outliers of the Model  Concept of key to link the real data  Technologies such as dynamic masking  Industry-specific compliances Implementation  Validate your model with at least two tools  Re-validate your model with the Business User
  14. 14. 14 Challenges & Analytics Journey Readiness Resource Abundant Technology Options Governance Mission Data Preparation Modelling Actionable Insights EnactmentImplementation
  15. 15. 15 Challenges & Analytics Journey Key Success Factor  Analytics is a “Global Business Initiative” supported by IT  Right resource, “All the Time”  Involve, Evolve and Improve
  16. 16. 16 ScanSee® Business Case ScanSee® provides its consumers what they want, when they want it. In order to achieve this, we implemented tracking consumer behavior for retailers / businesses to view, and, for consumers to manage without sacrificing consumer privacy. ■ Aggregation of Data within the Dashboard ■ Categorizing Information ■ Recommending Products
  17. 17. 17 Aggregation of Data ScanSee® provides a Dashboard for Businesses engaged in Consumer Behavior. We aggregate data from consumers and display that information to the businesses. This allows the business to understand what products, coupons, deals, pages are working and what are not working, as well as allow them to target information onto key areas.
  18. 18. 18 Path to Analytics ■ Mission : Improve Customer Experience, Value for Consumer / Retailer / Supplier ■ Version 1.0 with Dashboards using data gathered from Click Stream Analysis ■ Version 2.0 Implementation of Recommendation Engine  Preparation: Prepare the data by implementing Click Stream Analysis into the product  Modeling: Usage of Microsoft Analytics, R and Rapid Miner  Actionable Insight/Enactment: Recommending a Product Relevant to User Preferences and Similar Users
  19. 19. 19 Analytics @ Work
  20. 20. 20 Copyright: SPAN Systems Corporation www.spansystems.com • Offshore Centers Certified – CMMI 5, PCMM 3 – ISO 9001:2008 – ISO 27001: 2005 20 SPAN Overview Processes Client Engagement Stability Domain Technology SPAN • Almost two decades in IT solution providing • Part of US$ 2.3 Billion Norwegian company & 10,000+ employees • #7 in Best Employer in India • Insurance & Healthcare • Banking & Finance • Retail • Independent Software Vendors • DW / BI • Enterprise Mobility • ERP • Independent Testing Services • Remote Infrastructure Management Services • Product Engineering Services • Application Management • Relationship Model – Management focus – Governance team • Innovative Pricing • Tailored Business Models • Pavilion® Engagement Model
  21. 21. 21 Thank you for joining Get Your Data Analytics Strategy Right!Get Your Data Analytics Strategy Right! This webinar was sponsored by SPAN Systems Corporation in conjunction with The Outsourcing Institute. Ram Mohan, Advisor - Strategy, SPAN Infotech (India) Pvt. Ltd. Email Id: ramamohan_pr@spanservices.com T.S. Somashekara, Director - BI and Database Services, SPAN Infotech (India) Pvt. Ltd. Email id : soma_ts@spanservices.com Stephanie Blackwell, Director of Technology, ScanSee Email id: sblackwell@scansee.com

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