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The Best Way To Choose Your Data Science Projects: Webinar

Gramener's Head of Analytics & Co-Founder, Ganes Kesari talks about how to create a data science roadmap that maximizes ROI.<br><br>Ganes shares his valuable insights on how and why 80% of analytics projects fail by not being able to solve the right problem. Using real-world examples, this webinar shows you how to unlock business value by following a simple step-by-step approach. You'll discover how to convince and onboard your technology and business team for the next best project.<br><br>Webinar Link: https://info.gramener.com/data-science-roadmap<br><br>Book A Free Demo: https://gramener.com/demorequest/

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The Best Way To Choose Your Data Science Projects: Webinar

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  1. The Best Way to Choose your Data Science Projects Ganes Kesari Webinar Nov 2019 Gramener

  2. 1000 Data Scientists 1000 Data Scientists $250,000 $250,000 Avg Avg cost cost “80% of analytics projects will fail projects will fail… 80% of analytics - - Gartner Gartner Reference: McKinsey; Gartner report

  3. 3

  4. INTRODUCTION Ganes Kesari Co-founder & Head of Analytics Insights as Stories @kesaritweets Help start, apply and adopt Data Science “Simplify Data Science for all” 100+ Clients 4

  5. OUTCOME CHOICE ROADMAP EXAMPLES 5

  6. MATURITY LEVELS WITH DATA Phases Phases Data as Data as ‘Culture Culture’ Data Engineering Data Engineering Data Science Data Science Maturity Maturity Data Data Data Data Storage Storage Data Data Reporting Reporting Insights Insights Consumption Consumption Decisions Decisions Collection Collection Transformation Transformation Int Int/External /External Metrics/KPI Metrics/KPI EDA EDA Un/Structured Un/Structured ETL ETL Narrative Narrative Change Change Mgmt Mgmt Activities Activities Logs, IOT Logs, IOT Aggregates Aggregates SQL, Spark.. SQL, Spark.. ML ML Info Design Info Design Workflows Workflows Cleaning Cleaning Stage/Stream Stage/Stream Reports Reports Data lake.. Data lake.. AI AI Data Stories Data Stories Actions Actions Preparation Preparation 6

  7. MATURITY LEVELS WITH DATA Phases Phases Data as Data as ‘Culture Culture’ Data Engineering Data Engineering Data Science Data Science Maturity Maturity Data Data Data Data Storage Storage Data Data Reporting Reporting Insights Insights Consumption Consumption Decisions Decisions Collection Collection Transformation Transformation Int Int/External /External Metrics/KPI Metrics/KPI EDA EDA Un/Structured Un/Structured ETL ETL Narrative Narrative Change Change Mgmt Mgmt Activities Activities Logs, IOT Logs, IOT Aggregates Aggregates SQL, Spark.. SQL, Spark.. ML ML Info Design Info Design Workflows Workflows Cleaning Cleaning Stage/Stream Stage/Stream Reports Reports Data lake.. Data lake.. AI AI Data Stories Data Stories Actions Actions Preparation Preparation 7

  8. DATA SCIENCE MATURITY: REPORTING - EXAMPLE 8

  9. MATURITY LEVELS WITH DATA Phases Phases Data as Data as ‘Culture Culture’ Data Engineering Data Engineering Data Science Data Science Maturity Maturity Data Data Data Data Storage Storage Data Data Reporting Reporting Insights Insights Consumption Consumption Decisions Decisions Collection Collection Transformation Transformation Int Int/External /External Metrics/KPI Metrics/KPI EDA EDA Un/Structured Un/Structured ETL ETL Narrative Narrative Change Change Mgmt Mgmt Activities Activities Logs, IOT Logs, IOT Aggregates Aggregates SQL, Spark.. SQL, Spark.. ML ML Info Design Info Design Workflows Workflows Cleaning Cleaning Stage/Stream Stage/Stream Reports Reports Data lake.. Data lake.. AI AI Data Stories Data Stories Actions Actions Preparation Preparation 9

  10. DATA SCIENCE MATURITY: INSIGHT - EXAMPLES 10

  11. DATA SCIENCE MATURITY: INSIGHT - EXAMPLES Identifying Salmon Identifying Salmon using AI using AI 11 https://partner.microsoft.com/en-us/case-studies/gramener

  12. MATURITY LEVELS WITH DATA Phases Phases Data as Data as ‘Culture Culture’ Data Engineering Data Engineering Data Science Data Science Maturity Maturity Data Data Data Data Storage Storage Data Data Reporting Reporting Insights Insights Consumption Consumption Decisions Decisions Collection Collection Transformation Transformation Int Int/External /External Metrics/KPI Metrics/KPI EDA EDA Un/Structured Un/Structured ETL ETL Narrative Narrative Change Change Mgmt Mgmt Activities Activities Logs, IOT Logs, IOT Aggregates Aggregates SQL, Spark.. SQL, Spark.. ML ML Info Design Info Design Workflows Workflows Cleaning Cleaning Stage/Stream Stage/Stream Reports Reports Data lake.. Data lake.. AI AI Data Stories Data Stories Actions Actions Preparation Preparation 12

  13. DATA SCIENCE MATURITY: CONSUMPTION - EXAMPLES This is a dataset (1975 – 1990) that has been around for several years, and has been studied extensively. Yet, a visualization can reveal patterns that are neither obvious nor well known. More births For example, • Are birthdays uniformly distributed? • Do doctors or parents exercise the C-section option to move dates? • Is there any day of the month that has unusually high or low births? • Are there any months with relatively high or low births? … on average, for each day of the year (from 1975 to 1990) Fewer births Some special days like April Fool’s day are avoided, but Valentine’s Day is quite popular Most people prefer not to have children on the 13thof any month, given that it’s an unlucky day Relatively few births during the Christmas and Thanksgiving holidays, as well as New Year and Independence Day. Very high births in September. But this is fairly well known. Most conceptions happen during the winter holiday season 13 https://gramener.com/posters/Birthdays.pdf

  14. DATA SCIENCE MATURITY: CONSUMPTION - EXAMPLES This is a birth date dataset that’s obtained from school admission data for over 10 million children. When we compare this with births in the US, we see none of the same patterns. For example, • Is there an aversion to the 13thor is there a local cultural nuance? • Are holidays avoided for births? • Which months have a higher propensity for births, and why? • Are there any patterns not found in the US data? … on average, for each day of the year (from 2007 to 2013) More births Fewer births Such round numbered patterns a typical indication of fraud. Here, birthdates are brought forward to aid early school We see a large number of children born on the 5th, 10th, 15th, 20thand 25thof each month – that is, round numbered dates Very few children are born in the month of August, and thereafter. Most births are concentrated in the first half of the year admission 14 https://gramener.com/posters/Birthdays.pdf

  15. MATURITY LEVELS WITH DATA Phases Phases Data as Data as ‘Culture Culture’ Data Engineering Data Engineering Data Science Data Science Maturity Maturity Data Data Data Data Storage Storage Data Data Reporting Reporting Insights Insights Consumption Consumption Decisions Decisions Collection Collection Transformation Transformation Int Int/External /External Metrics/KPI Metrics/KPI EDA EDA Un/Structured Un/Structured ETL ETL Narrative Narrative Change Change Mgmt Mgmt Activities Activities Logs, IOT Logs, IOT Aggregates Aggregates SQL, Spark.. SQL, Spark.. ML ML Info Design Info Design Workflows Workflows Cleaning Cleaning Stage/Stream Stage/Stream Reports Reports Data lake.. Data lake.. AI AI Data Stories Data Stories Actions Actions Preparation Preparation 15

  16. DATA SCIENCE MATURITY: INSIGHT + CONSUMPTION - EXAMPLES 16 https://tcdata360.worldbank.org/stories/tech-entrepreneurship/

  17. DATA SCIENCE MATURITY: INSIGHT + CONSUMPTION - EXAMPLES Navigation filters Visual flow diagram indicating bottlenecks & volume of requests Automated analysis to identify areas which need work and which can create maximum impact 17 https://gramener.com/servicerequests/

  18. MATURITY LEVELS WITH DATA Phases Phases Data as Data as ‘Culture Culture’ Data Engineering Data Engineering Data Science Data Science Maturity Maturity Data Data Data Data Storage Storage Data Data Reporting Reporting Insights Insights Consumption Consumption Decisions Decisions Collection Collection Transformation Transformation Int Int/External /External Metrics/KPI Metrics/KPI EDA EDA Un/Structured Un/Structured ETL ETL Narrative Narrative Change Change Mgmt Mgmt Activities Activities Logs, IOT Logs, IOT Aggregates Aggregates SQL, Spark.. SQL, Spark.. ML ML Info Design Info Design Workflows Workflows Cleaning Cleaning Stage/Stream Stage/Stream Reports Reports Data lake.. Data lake.. AI AI Data Stories Data Stories Actions Actions Preparation Preparation 18

  19. POLL #1 DATA SCIENCE MATURITY LEVELS 19

  20. 1 WHY PROJECTS FAIL? 2 HOW TO IDENTIFY INITIATIVES? 3 HOW TO BUILD YOUR ROADMAP? 20

  21. CHOOSING DATA SCIENCE PROJECTS A WHY DATA SCIENCE PROJECTS FAIL 21

  22. TEAMS OFTEN GET THEIR PRIORITIES WRONG Interesting Interesting vs vs Impactful Impactful Urgent Urgent vs vs Strategic Strategic Possible Possible vs vs Feasible Feasible 22

  23. THE THE DATA SCIENCE JOURNEY DATA SCIENCE JOURNEY 23

  24. “ Most insights don Most insights don’t deliver business benefits because they business benefits because they solve the solve the wrong problem wrong problem t deliver 24

  25. CHOOSING DATA SCIENCE PROJECTS B IDENTIFYING POTENTIAL DATA INITIATIVES 25

  26. POLL #2 DATA SCIENCE ADOPTION CHALLENGES 26

  27. COMPANY CULTURE IS THE BIGGEST ROADBLOCK FOR ADOPTION https://www.oreilly.com/data/free/ai-adoption-in-the-enterprise.csp 27

  28. ASK EXECUTIVES FOR THEIR TIME, NOT JUST THEIR BUDGET Start data science Start data science initiatives top initiatives top- -down Use Exec power to Use Exec power to navigate change navigate change down • Brainstorming workshop • Identify core team & champions • Planned change management • Roadshows, reviews, incentives 28

  29. “ Success of the data science Success of the data science journey is proportional to the journey is proportional to the level of level of executive attendance executive attendance in the kick the kick- -off workshop off workshop in 29

  30. EXAMPLE: MEDIA BROADCASTER Executive Executive Mandate Mandate Change Change Management Management 30

  31. IDENTIFY THE USERS, THEIR OBJECTIVES & CHALLENGES Who are your Who are your users? users? What are their What are their priorities? priorities? What are their What are their pain areas? pain areas? • Define target users • Roles, designations • Qtrly/Yearly goals • Interview, focus groups • Metrics to quantify • Ranked challenges 31

  32. A FRAMEWORK TO IDENTIFY THE POTENTIAL INITIATIVES Business driven approach 1 have a set of that can be met by which answer specific using Stakeholder groups Objectives Initiatives Questions Data for that meet that can address suggests Data driven approach 2 Your list of Your list of relevant data relevant data science initiatives science initiatives 32

  33. EXAMPLE: MEDIA BROADCASTER • Ground level Salesforce • Sales Managers Users Users • Acquire & retain clients • Cross-sell to clients • Utilize ad inventories Priorities Priorities • Loss of market share • Poor client monetization • Wasted ad inventory Pain areas Pain areas 33

  34. CHOOSING DATA SCIENCE PROJECTS C PRIORITIZING THEM INTO A ROADMAP 34

  35. USE THE 3 LEVERS FOR PRIORITIZATION OF PROJECTS Impact Impact Feasibility Feasibility Urgency Urgency • Revenue, Cost, Effort • Quantified impact • Data, Tech, Budget • Low – medium – high • Timeframe available • Low – medium – high 35

  36. A FRAMEWORK TO CHOOSE THE RIGHT PROJECTS Business driven approach 1 have a set of that can be met by which answer specific using Stakeholder groups Objectives Initiatives Questions Data for that meet that can address suggests Data driven approach 2 Feasibility Feasibility Evaluate Quick wins High Prioritised Roadmap Prioritised Roadmap Strategic Med Deferred Urgency Urgency High Evaluate Low Med Low Impact Impact 36

  37. BUILD YOUR ROADMAP WITH PROJECTS ACROSS THE LEVELS Phases Phases Data as Data as ‘Culture Culture’ Data Engineering Data Engineering Data Science Data Science Maturity Maturity Data Data Data Data Data Storage Data Storage Reporting Reporting Insights Insights Consumption Consumption Decisions Decisions Collection Collection Transformation Transformation Feasibility Feasibility Evaluate Quick wins High Prioritised Roadmap Prioritised Roadmap Strategic Med Deferred Urgency Urgency High Evaluate Low Med Low Impact Impact 37

  38. EXAMPLE: MEDIA BROADCASTER Business Business Challenge Challenge Loss of Loss of Market Market Share Share Initiative to solve it Initiative to solve it Impact Impact Urgency Urgency Feasibility Feasibility •Customer acquisition $5 M High Low •Customer retention $4 M Medium High •Cross selling $2 M High High Poor client Poor client monetization monetization •Pricing improvements $0.5M Medium Low •Improve fulfilment $0.7M Medium High Wasted ad Wasted ad inventory inventory •Sell to new/existing clients $1 M High Medium 38

  39. POLL #3 CHALLENGES IN BUILDING YOUR DATA SCIENCE ROADMAP 39

  40. RECAP: BEST WAY TO CHOOSE YOUR DATA SCIENCE PROJECTS Building your roadmap Building your roadmap • Impact, Feasibility, Urgency • Framework to sequencing Maturity levels in data Maturity levels in data • Scaling the stages • Insights + Consumption Identifying initiatives Identifying initiatives • Start initiatives top-down • Users, priorities, pain areas Why projects fail? Why projects fail? • 80% failure rate • Misplaced priorities 40

  41. “Amongst organizations that Amongst organizations that reached the highest level of Data reached the highest level of Data Maturity, nearly half of them Maturity, nearly half of them significantly exceeded business significantly exceeded business goals goals. . - - Deloitte Deloitte 41 Reference: Deloitte report

  42. Thank You! gramener.com /ganes-kesari gramener.com/solutions @kesaritweets 42

  43. EXAMPLE: MEDIA BROADCASTER 43

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