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Business-driven advanced analytics in practice - HiQ Finland

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Mahdollista: Data Driven Business 3.5.2016, Iina Juurinen, HiQ Finland

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Business-driven advanced analytics in practice - HiQ Finland

  1. 1. BUSINESS-DRIVEN ADVANCED ANALYTICS IN PRACTICE IINA JUURINEN DATA SCIENTIST HiQ FINLAND
  2. 2. HOW DID I END UP HERE?
  3. 3. DATA ANALYTICS With data analytics your business is Smarter More productive Better at making predictions
  4. 4. HOW TO GET PAST THE HYPE PERIOD? In order to have a succesful data analytics project you need a plan Important questions: What is the problem you want to solve? What is the data to be used? What type of analytics is applied? How the analytics is going to transform business processes?
  5. 5. TODAY 1. Most important aspects of data analytics project + example case 2. How R in SQL server 2016 affects advanced analytics projects
  6. 6. ADVANCED ANALYTICS PROJECT
  7. 7. EXAMPLE CASE Example data is Eat.fi restaurant reviews ”Ihanat gluteenittomat pannarit. Tilasimme makeat pannarit gluteenittomana kahvin kanssa nam !” ”Naan leipä todella hyvää. Runsaat annokset. Maukas ruoka. Ystävällinen palvelu.” ”Kiireinen, välinpitämätön "palvelu", standardit hampurilaiset ja suolattomat pikku ranskikset eivät paljon mieltä piristäneet.” ”Paras lämmin nacholautanen mitä Helsingissä olen maistanut ja siitä riitti helposti kahdelle. Burgeri ja taco annokset olivat monipuolisia mauiltaan ja eivät jättäneet nälkäiseksi. Palvelu oli myös ystävällistä!” ”Maukasta ruokaa selkeästi hyvistä raaka-aineista. Ensiluokkainen, ystävällinen ja nopea palvelu. Toivottavasti suuri yleisö löytäisi tämän paikan.” ”Erittäin herkullista ruokaa; kaunis esillepano ja tuoreet, sesonginmukaiset raaka-aineet. Ystävällinen ja iloinen henkilökunta. Ravintola viusuaalisesti kaunis kokonaisuus.” Eurooppalaista Kahvilat Pikaruokaa / ketjut Baari / pubiruokaa Fine dining Aasialaista Muuta Big company that owns different type of restaurants Problem: Customer feedback coming through online form is not directed to correct departments
  8. 8. HOW TO HAVE A SUCCESSFUL DATA ANALYTICS PROJECT? Clear metrics Consistent data collection Using data to answer questions STEP 1. DATA-DRIVEN CULTURE STEP 2. SCREENING IDEAS Predictive question Clear path of action Data quality and access Business process STEP 3. PROBLEM FORMULATION Business goal Business metrics Performance requirements Project scope, schedule, resources... STEP 4. CONSUMPTION SCENARIOS Use of results When, how, who, how often Business value creation
  9. 9. EXAMPLE CASE AFTER THE FIRST SPRINT Performance requirements are not met Proven fit to business process
  10. 10. R IN SQL SERVER 2016
  11. 11. WHAT IS R? Tool for statistical computing and graphics Programming language and a software environment WHY IS R POPULAR? Ready-made packages for different types of analytics needs Modifying data Machine learning Statistics Visualizations Fast, easy, and convenient analytics projects
  12. 12. R IN SQL SERVER 2016 Eliminate: Costs Security risks Increase: R script performance 1. By using your own computer and your preferred R tool (e.g. RStudio) - Calculations in the SQL host server 2. By running R scripts like any stored procedure in Transact-SQL TWO-WAY USE ANALYTICS CLOSE TO THE DATA
  13. 13. DATA PROJECTS AND R IN SQL SERVER Clear metrics Consistent data collection Using data to answer questions STEP 1. DATA-DRIVEN CULTURE STEP 2. SCREENING IDEAS Predictive question Clear path of action Data quality and access Business process STEP 3. PROBLEM FORMULATION Business goal Business metrics Performance requirements Project scope, schedule, resources... STEP 4. CONSUMPTION SCENARIOS Use of results When, how, who, how often Business value creation
  14. 14. R AS PART OF SQL SERVER – EXAMPLE CASE Web input SQL server Trained R model Reporting Enriched data Original data
  15. 15. SUMMARY You can gain considerable business benefits from data analytics Smart planning Clear vision Business goals Good question With R in SQL server you can perform analytics where the data is No data transfer Powerful engine STEP 1. DATA-DRIVEN CULTURE STEP 2. SCREENING IDEAS STEP 3. PROBLEM FORMULATION STEP 4. CONSUMPTION SCENARIOS
  16. 16. ANALYTICS WITH BUSINESS IN MIND www.datadrivenbusiness.fi Iina Juurinen Email: iina.juurinen@hiq.fi Puh: 050 4657979

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