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Solita HUB: Älykkäämpää liiketoiminnan suunnittelua ja ennustamista - Juha Teljo, IBM

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Solita HUB: Älykkäämpää liiketoiminnan suunnittelua ja ennustamista - Juha Teljo, IBM

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Solita HUB: Älykkäämpää liiketoiminnan suunnittelua ja ennustamista -tilaisuuden esitys 4.11.2015. Samassa esityksessä Ennakoivasta analytiikasta uutta arvoa liiketoiminnalle- ja Asiakaskokemuksia älykkäästä ennustamisesta -puheenvuoro.
Juha Teljo, Business Intelligence and Predictive Analytics Sales Lead Europe, IBM.

Solita HUB: Älykkäämpää liiketoiminnan suunnittelua ja ennustamista -tilaisuuden esitys 4.11.2015. Samassa esityksessä Ennakoivasta analytiikasta uutta arvoa liiketoiminnalle- ja Asiakaskokemuksia älykkäästä ennustamisesta -puheenvuoro.
Juha Teljo, Business Intelligence and Predictive Analytics Sales Lead Europe, IBM.

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Solita HUB: Älykkäämpää liiketoiminnan suunnittelua ja ennustamista - Juha Teljo, IBM

  1. 1. © 2015 IBM Corporation Ennakoivasta analytiikasta uutta arvoa liiketoiminnalle Juha Teljo Business Intelligence and Predictive Analytics, IBM Europe
  2. 2. © 2015 IBM Corporation2 @teljoj #SolitaHUB How to look into the future without DeLorean ? § Ask People - Budgeting and forecasting • Few things but often • Drivers instead of accounts § Ask Data - Reporting / Business Intelligence - Use of predictive analytics
  3. 3. © 2015 IBM Corporation3 Analytics Breadth to Enable Decisions How can everyone be more right… ….more often? Descriptive Prescriptive Predictive Cognitive What has happened? What could happen? How can we achieve the best outcome? Tell me the best course of action? Big Data & Analytics How is data managed and stored? Business Value Information Layer
  4. 4. © 2015 IBM Corporation4 • IBM® Cognos® Business Intelligence • IBM Cognos TM1® • IBM SPSS® Modeler • IBM WebSphere® MQ • IBM InfoSphere® DataStage® Elie Tahari and Tahari ASL combines fashion savvy with powerful business analytics 97% accuracy in predicting customers orders 4 months in advance Solution Components Business Challenge: Elie Tahari and Tahari ASL faced issues with decision- making and keeping pace with customer demand. They had a need to predict customers’ future demand for each product including the sizes and colors required. The Solution: The client implemented IBM® Global Business Services and IBM Cognos® Business Intelligence which helped them create a seamless real-time reporting framework with accurate, predictive demand-planning capabilities ensuring the most popular products are available at the right place at the right time. "The ability to look four months into the future and know what our inventory levels need to be on a weekly basis is absolutely key to our success... It allows us to adjust production to a level that reduces our exposure and still gives us the ability to supply our customer with close to 100 percent of their orders." — Nihad Aytaman, Director of Business Applications, Elie Tahari and Tahari ASL 30% decrease in supply chain and logistics costs Decrease in reporting cycle from two days to a few minutes
  5. 5. © 2015 IBM Corporation5 @teljoj #SolitaHUB The Analytical Edge “At a time when companies in many industries offer similar products and use comparable technology, high-performance business processes are among the last remaining points of differentiation.” Tom Davenport, author of “Competing on Analytics” Analytical Competitor: “an organization that uses analytics extensively and systematically to outthink and outexecute the competition.”
  6. 6. © 2015 IBM Corporation6 @teljoj #SolitaHUB What is data science? Overarching goal: Extract knowledge from data The idea of “data science” is nothing new: - Charles Babbage envisioned “The Analytical Engine” 1837 - IBM Journal “A Business Intelligence System” 1958 Today, changes in environment – data availability, software, hardware
  7. 7. © 2015 IBM Corporation7
  8. 8. © 2015 IBM Corporation8 @teljoj #SolitaHUB How do I monetize my data ?
  9. 9. © 2015 IBM Corporation9 @teljoj #SolitaHUB New data sources increase predictive value of existing data
  10. 10. © 2015 IBM Corporation10 What is Geo-spatial Analytics? §  Approach to apply statistical and other informational techniques to data which has a geospatial aspect. §  Gain new insights into your data by including time and location §  Understand your business like never before
  11. 11. © 2015 IBM Corporation11 Example: Creating a 360 degree customer view Marketing & sales history Service interactions Complaints Channel usage … Interactions Customer satisfaction Financial acumen/interest Risk appetite … Attitudes Gender Age Marital status Socio-economic Time as a customer … Demographics Renewal history Payment history Claims history Product holdings Coverage/deductibles Sales channel Recent policy changes … Behavior
  12. 12. © 2015 IBM Corporation12 @teljoj #SolitaHUB What is Predictive Analytics? Predictive Analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events • Gareth Herschel, Research Director, Gartner Group
  13. 13. © 2015 IBM Corporation13 Analytics is not always easy – Correlation does not imply causality
  14. 14. © 2015 IBM Corporation14 @teljoj #SolitaHUB Why is Predictive Analytics different? “NOW” Traditional BI and Analytics: • Insight, metrics, etc. up to this point in time • User hypotethics driven initiatives to explore aggregate data Predictive Analytics: - Algorithms automatically detecting important insights in data - Deliver deep insights to improve strategic and operational decision making - “Learn” from historical data – create predictive models “NOW” “NOW” Deploying Predictive Models • Leverage current and historical data • Make robust predictions on current and future cases • Embed in business processes to transform decision making and drive better outcomes M KPI KPI KPI Understand& React Predict & Act
  15. 15. © 2015 IBM Corporation15 @teljoj #SolitaHUB What Does Predictive Analytics Do? Predict - Future outcomes based upon historical information •  What product to cross-sell to a customer •  Where to staff police force based upon crime patterns •  What is the risk for issuing credit to a customer Group - Creates natural forming clusters •  Customer segmentation •  Pricing clusters Associate - Creates relationships between entities •  What products do customers own together •  What medical treatments lead to improved recovery Anomaly - Find instances that fall outside of “normal” behavior •  Anti-money laundering activity •  Insurance and Healthcare fraud Forecast – Future results based upon historical inputs •  Demand forecasting for supply chain efficiencies •  Quarterly results for more effective financial planning
  16. 16. © 2015 IBM Corporation16 @teljoj #SolitaHUB Statistical Analysis & Data Mining: Feeding Predictive Analytics §  A statistical approach involves -  forming a theory about a possible relationship -  converting it to a hypothesis -  testing that hypothesis using statistical methods §  It is a manual, user-driven, top- down approach to data analysis Top-Down Approach §  Data mining involves the interrogation of the data and is performed by the data mining method rather than by the user §  It is a data-driven, self- organizing, bottom-up approach to data analysis that works on very large data sets “Statistical Modeling: The Two Cultures,” Leo Breiman, Statistical Science, 2001, Vol.16 (3), pp.199-231. Bottom-up Approach Source: DM Review Note that Both Approaches can Drive Predictive Analytics
  17. 17. © 2015 IBM Corporation17 @teljoj #SolitaHUB CRISP-DM: Cross Industry Standard Process for Data Mining The Typical Data Science Workflow
  18. 18. © 2015 IBM Corporation18 Example: The mechanics of personalization Create a 360° customer view Automate behavior capture Envision the customer journey 1.  Collect & Analyze Develop actionable customer insights Collect response details Optimize campaigns based on results & feedback Measure & track results 3. Measure & Optimize Assign the right offers to the right customers Deploy offers to the right channel at the right time Personalize and tailor offers to the customer 2. Decide & Execute
  19. 19. © 2015 IBM Corporation19 Business challenge: Predicting which students are at risk of dropping out based on academic variables alone is like looking for a lost key under a streetlight. You may find it there, but there’s a good chance it’s somewhere else. To counter student attrition, this university in Australia had long relied on the efforts of individual faculty members, whose narrowly focused, spreadsheet-based models were missing the many behavioral patterns that foreshadowed attrition. . The smarter solution: The university is using predictive analytics to understand the complex patterns at the intersection of demographics, academics and social behavior that correlate with the risk of dropping out. It’s enabling advisors to keep more students from falling through the cracks and providing evidence-based guidance on which intervention policies work best at keeping students on track. With a deeper understanding of the behavioral factors behind student attrition, we can adapt intervention strategies to students’ specific needs and get better results. Solution components >30% improvement in the predictive accuracy of at-risk student identification efforts 100% payback achieved within one year of implementation >10% lower attrition rate expected through more inclusive screening and more effective intervention A university in Australia uses sophisticated models to better predict student drop-out risk and the best way to prevent it •  IBM® Cognos® TM1® Web •  IBM SPSS® Modeler •  IBM Business Partner Cortell Australia Pty Ltd
  20. 20. © 2015 IBM Corporation20 @teljoj #SolitaHUB Collaboration and Deployment Services Analytic Server Modeler and ADM Decision Optimization Data Collection Statistics Watson Analytics IBM Predictive Product & Solution Portfolio Differen'ated  Analy'c  Solu'ons   Predictive Maintenance and Quality (PMQ) Predictive Customer Intelligence (PCI) Custom ApplicationsCounter Fraud Management (CFM)
  21. 21. © 2015 IBM Corporation21 @teljoj #SolitaHUB21 IBM SPSS Statistics §  Advanced statistics and data management for professional analysts and data scientists §  Easy to use and fully features to support a faster time to value and a shorter learning curve §  Provides insight into a sample of data and tools for prediction and forecasting based on the data §  Programmability for advanced users that leverages common statistical programming languages in the market (Python, R)
  22. 22. © 2015 IBM Corporation22 @teljoj #SolitaHUB IBM SPSS Modeler §  Comprehensive predictive analytics platform §  Improve outcomes through predictive intelligence §  Flexible adoption and configuration -- on premises, in cloud, and everything in between §  Scale from personal usage, point solution(s) to enterprise deployment §  Providing a range of advanced analytics -decision management, text analytics, entity analytics, social network analysis and optimization.
  23. 23. © 2015 IBM Corporation23 @teljoj #SolitaHUB The building blocks of ROI § Inserting “intelligence” at key decision points in business processes to improve outcomes - and improve processes + - + -
  24. 24. © 2015 IBM Corporation24 Cross-selling in the call center: 1st Year Results 1,000,000 inbound calls 180,000 action suggestions (cross-sell) 60,000 offers made by agent 30,000 high quality leads 22,000 products sold Over €30M additional sales in the call center IBM  provided  so-ware  and  services  to  help  AEGON   implement  IBM  Predic<ve  Analy<cs  so-ware  for   SCI  across  all  channels:   •  Call  Center,  Voice  Response   •  Outbound  marke<ng   •  Web  site   •  Intermediaries   The Opportunity: As one of the largest insurance companies in Hungary, AEGON manages a vast stock of customer data, which needs to be processed and analysed quickly and intelligently in order to provide the company with better insight into customer behaviour. What Makes it Smarter AEGON Hungary uses IBM® SPSS® software to drive improved analysis of raw customer data. Company product developers use SPSS Statistics Base, while the data mining team also works with SPSS Modeler. Real Business Results: §  Enables rapid, efficient analysis of large volumes of data, making it easy to manage 50-100 million charts. §  Enhances insight into customer behaviour and needs, resulting in increased client revenue, stronger customer loyalty, and reduced cost of sales and canvassing. §  Generates better quality, more structured data and analyses through the use of extensive procedures and tests.
  25. 25. © 2015 IBM Corporation25 What is Decision Optimization? What to build, where and when? How to best allocate aircrafts and crews? Risk vs. potential reward Inventory cost vs. customer satisfaction Cost vs.carbon emission Optimization helps businesses: • create the best possible plans • explore alternatives and understand trade-off • respond to changes in business operations
  26. 26. © 2015 IBM Corporation26 What COULD happen? What SHOULD happen? Predictive Prescriptive Consumer Goods Retail Manufacturing From Predictive to Prescriptive – The Next Step Based on the history of product sales, what will be my sales forecast for the next month? With the forecasted data, where should I place my inventory? How much safety stock do I need? Based on the history of customer purchasing data, will you be able to predict what items will interest them? Based on the history of machine data, will you be able to predict when your machine might breakdown? What items should I place in which stores? What campaigns would be best to promote those items? How do I minimize discounts? Using the predicted machine breakdown data to conduct preventive maintenance, do you know the best times to schedule these? (Demand, inventory, etc..)
  27. 27. © 2015 IBM Corporation27 @teljoj #SolitaHUB Increasing data volume requires scalable analytics § Organizations require capabilities that drive better outcomes while: -  Leveraging existing IT investments & user skill sets -  Scaling well, performing rapidly & minimizing latency § Key to achieving these goals is operating on data where it resides -  Whether pre-processing data, building models or scoring -  Accelerates productivity, maximizes resources & minimizes network traffic -  Deploying into operational systems extends performance benefits
  28. 28. © 2015 IBM Corporation28 An automobile manufacturer in France creates revenue streams and raises customer satisfaction with an Internet of Vehicles platform Solution components Business challenge: As consumer demand for mobile applications increases, this automobile manufacturer in France needed to deliver various connected services to drivers while using data to create new revenue streams. The transformation: With an Internet of Vehicles (IoV) platform, the manufacturer can use connected devices to deploy new services to its cars while gathering data from the vehicles to drive additional revenue. Information about a car’s condition, driving patterns, location and weather is collected and analyzed for insight into driving behaviors, traffic and even weather conditions in certain areas. The manufacturer can create new revenue streams by providing this data to third parties such as governments, insurance agencies and weather companies. The company can also offer drivers a level of connected services such as improved traffic navigation, smartphone connectivity and preventative maintenance alerts that no other manufacturer in the French market can match, improving the driving experience and increasing brand loyalty. Consumers can experience a new level of comfort and convenience from their cars while industries seize new opportunities to deliver personalized services. •  IBM® InfoSphere® BigInsights® •  IBM InfoSphere Streams •  IBM InfoSphere Information Server •  IBM SPSS® Analytics Server •  IBM SPSS Modeler •  IBM API Management •  IBM MessageSight •  IBM SPSS Lab Services •  IBM Software Services for Information Management 6% increase in lead generation expected by understanding drivers’ habits and recommending new cars based on those behaviors 20% reduction expected in drivers’ maintenance costs through preventive alerts 40% boost in anticipated revenues from connected services over a seven-year period
  29. 29. © 2015 IBM Corporation29 A predictive enterprise applies advanced analytics to improve processes and optimize outcomes across all areas of its business to drive profitable revenue growth Vision: The Predictive Enterprise
  30. 30. © 2015 IBM Corporation30 @teljoj #SolitaHUB Becoming a Predictive Enterprise § No organisation becomes a Predictive Enterprise overnight § A journey, not a single “big bang” project § Prioritize projects and plan a road map to: - Ensure quick, incremental ROI to fund further investment - Move closer to full infrastructural adoption of predictive analytics § All driven by overarching vision - Not just point solutions
  31. 31. © 2015 IBM Corporation31 Mueller, Inc. uses advanced business analytics to transform its business model, becoming an information-driven enterprise Business Challenge: A shift in business strategy from manufacturing to retail drove a comprehensive cultural transformation within US manufacturer Mueller Inc. The company needed to analyze its business processes and performance to assess how well employees were adapting to its new business strategy. The Smarter Solution: Mueller implemented business analytics technology that enables all company employees to view and analyze company data in near-real time, empowering workers to measure individual performance and assess how their work affects the bottom line. “We can show sales teams exactly how they are contributing to the business and explain what they need to do to improve their metrics ... a much more effective way of driving the changes in behavior that are vital for business transformation.” —Mark Lack, manager of Strategy Analytics and Business Intelligence 20 – 30% reduction in scrap metal manufacturing waste 20% return on assets resulting from rapid identification and tracking of business process improvements 113% ROI through use of business analytics Solution Components •  IBM® Cognos® Business Intelligence •  IBM Cognos TM1® •  IBM SPSS® Modeler •  IBM Business Analytics Software Services
  32. 32. #SolitaHUB

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