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Data Storytelling - Game changer for Analytics
This document discusses the importance of data storytelling and provides recommendations for data leaders. It argues that data stories are more memorable and impactful than raw data or facts. The document outlines four patterns for telling data stories and provides examples. It recommends that organizations embed design skills, automate storytelling, and embrace storytelling as part of the data insights process. Telling data stories can help people really understand data intuitively and aid decision making.
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Data Storytelling - Game changer for Analytics
- 1. “War is 90%information” Napoleon Bonaparte
- 2. 2 Source: "Decisive Action:How Businesses Make Decisions and How They Could Do It Better," The Economist, Intelligence Unit. 90% Proportion of business decision makers would prioritize gut feel over data if there was a contradiction between the two.
- 3. “50% of data scienceprojects will never get consumed… Reference: Gartner
- 4. 4 Roadblocks to Success– Gartner CDAO survey Credit: Gartner “Consumption of Data as key enabler ”
- 5. 5 Data Engineering ActivitiesMaturityPhases Data Science Dataas ‘Culture’ Data Collection Data Storage Data Transformation Reporting Insights Consumption Decisions LOGS, IOT INT/EXTERNAL STAGE/STREAM SQL, SPARK.. UN/STRUCTURED DATA LAKE.. CLEANING ETL PREPARATION AGGREGATES METRICS/KPI REPORTS ML EDA AI Info Design Narrative Data Stories WORKFLOWS CHANGE MGMT ACTIONS Driving Data Supply Driving Data Value Maturity Levels with Data
- 6. 6 Insights Output :Examples Data as Culture’ Data Transformation Consumption MaturityPhases “Language of Data Scientist”
- 7. 7 Consumable Insights output: Examples https://gramener.com/securities/ MaturityPhases “Language of Decision Maker”
- 8. 8 Data generation andanalysis are not sufficient. “Cohesive Consumption of Data” Most decision-making discussions assume that only senior executives make decisions or that only senior executives’ decisions matter. This is a dangerous mistake… It’s clearly a budget! It has a lot of numbers in it! Peter F Drucker George W Bush
- 9. 9 CDOs Must AddressHearts & Minds to Drive Data Value Data-driven culture Business valueCDO Credit: Gartner
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- 11. Why Stories? Stories are| emotional Stories are | memorable Stories are | impactful
- 12. 100+ Clients @naveengattu Naveen Gattu Co-founder& COO We bridge the DATA CONSUMPTION gap Storytelling for Analytics INSIGHT STORY DATA GRAMENER COMBINES
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- 14. 14 Just EXPOSE the data tome EXHIBIT to me what is happening with the data EXPLAIN to me why it’s happening Allow me to EXPLORE and figure it out Low effort High effort High effort Low effort Creator Consumer There are Four Ways of Telling Data Stories
- 15. Let’s See Examplesof Each My aim is to plant ideas of what’s possible
- 16. 16 Just EXPOSE the data tome EXHIBIT to me what is happening with the data EXPLAIN to me why it’s happening Allow me to EXPLORE and figure it out Low effort High effort High effort Low effort Creator Consumer
- 17. 17 INFORMATION IN ROWS& COLUMNS
- 18. 18 GOOGLE SUGGEST: INDIA’SRELIGIONS LIN K
- 19. 19 GOOGLE SUGGEST: AUSTRALIA’SRELIGIONS LIN K
- 20. 20 Just EXPOSE the data tome EXHIBIT to me what is happening with the data EXPLAIN to me why it’s happening Allow me to EXPLORE and figure it out Low effort High effort High effort Low effort Creator Consumer
- 21. 21 This is adataset (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. 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? Very high births in September. But this is fairly well known. Most conceptions happen during the winter holiday season. Relatively few births during the Christmas & Thanksgiving holidays, as well as New Year and Independence Day. Most people prefer not to have children on the 13th of any month, given that it’s an unlucky day. Some special days like April Fool’s day are avoided, but Valentine’s Day is quite popular. More births Fewer births … on average, for each day of the year (from 1975 to 1990) Let’s Look at 15 Years of US Birth Data Education LINK Fraud
- 22. 22 The Pattern inIndia is Quite Different 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 13th or 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? Very few children are born in the month of August, and thereafter. Most births are concentrated in the first half of the year. We see a large number of children born on the 5th , 10th , 15th , 20th and 25th of each month – that is, round numbered dates. Such round numbered patterns a typical indication of fraud. Here, birthdates are brought forward to aid early school admission. More births Fewer births … on average, for each day of the year (from 2007 to 2013) Education LINK Fraud
- 23. 23 How should youhedge your Portfolio? 68% correlation between AUD & EUR Plot of 6-month daily AUD - EUR values Block of correlated currencies … clustered hierarchically using “Hierarchical Agglomerative Clustering” Algorithm LINK
- 24. 24 Just EXPOSE the data tome EXHIBIT to me what is happening with the data EXPLAIN to me why it’s happening Allow me to EXPLORE and figure it out Low effort High effort High effort Low effort Creator Consumer
- 25. 25 Financial Reporting NarrativesLINK Financial ServicesNarrativesFinancePlatform A key problem in financial reporting is annotating drivers of variance. For e.g.: • Which account caused the largest increase in assets? • Was this the primary cause, or one among many? • Were there other accounts that mitigated its effect? These are what a financial analyst manually analyzes, adding annotations to the report. But this is automatable. This natural language generator by Gramener applies these simple rules: • If there's more than one driver, mention the top driver. • If the second driver counteracts the first driver's effect, mention it. • Or, if the second driver has 78% of the influence on the first, mention it The annotations are similar to a human’s, but without human error. It sets a starting point for exploration, letting people focus on review rather than execution.
- 26. 26 EUROPEAN BREWERY IDENTIFIED€15 M COST SAVINGS AFTER CONSOLIDATING VENDORS WATCH A 4-MINUTE VIDEOSEE LIVE DEMO A leading European brewery’s plants purchased commodity raw materials from several vendors each – and had low volume discounts. Plants also placed multiple orders placed every week, leading to higher logistics cost. When plant managers were shown the data, they objected, saying “That’s not always the case.” Or, “That’s the only way– no one else does better.” Gramener built a custom analytics solution that sourced their SAP order data, automatically identified which plants ordered which commodities the most from multiple vendors – and when. It showed how each plant performed compared to peers – shaming those with poor performance. With this, they identified savings of €15 m — which the plant managers couldn’t refute. €15 m 40% savings potential identified annually vendor based reduction identified
- 27. 27 Challenges Women Face– An Interactive Narrative Best of the Visualization Web, Sep 2018 LINK
- 28. 28 Data-Driven Comics Canbe Embedded in BI Tools LINK
- 29. 29 These were automated using Comicgen Adata-comic library we developed. GRAMENER.COM / COMICGEN /
- 30. 30 Just EXPOSE the data tome EXHIBIT to me what is happening with the data EXPLAIN to me why it’s happening Allow me to EXPLORE and figure it out Low effort High effort High effort Low effort Creator Consumer
- 31. 31 Process Optimization, Supportedby Augmented Narratives Navigation filters Process flow diagram indicating bottlenecks & volume of requests Automated analysis to identify areas which need work and which can create maximum impact LINK
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- 33. 33 Just EXPOSE the data tome EXHIBIT to me what is happening with the data EXPLAIN to me why it’s happening Allow me to EXPLORE and figure it out Low effort High effort High effort Low effort Creator Consumer There are Four Ways of Telling Data Stories
- 34. 34 By 2025, datastories will be the most widespread way of consuming analytics & 75% of stories will be automatically generated using augmented analytics techniques. Reference: Gartner report , Augmented Analytics: Teaching Machines to Tell Data Stories to Humans
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- 37. 37 EMBRACE THE PROCESS 1 Data Storytelling= Insights + Narratives
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- 39. 39 AUTOMATE STORYTELLING 3 Reports in plain Englishwith visuals Ø Wealth reports Ø Patient reports Ø Loyalty point usage Ø School report cards NARRATIVES Visual Insights delivered to Inbox Ø Customer segments Ø Viewership shifts Ø Geo-demographics for geographic zones. INFOGRAPHIC ALERTS Engage through emotions from Comics Ø Price forecast Ø Revenue forecasts Ø Capacity utilization Ø Viewership forecast COMICGEN Insights delivered as Automated Videos Ø Type detection Ø Root cause drivers Ø Factor correlation Ø Cross-tabulation DATA VIDEOS
- 40. 40 AT GRAMENER, OURFOCUS IS ON NARRATES INSIGHTS FROM DATA AS STORIES Stories are memorable, viral NUMBERS ARE NOT ENOUGH STORIES EXPLAIN THEM Delays are due to fragile cargo. Trained staff and forklifts reduce risk of breakage, and hence reduce delay. Insights are useful, non- obvious, Big FACTS ARE NOT USEFUL E.g. Delay in cargo delivery grew 8% last quarter. INSIGHTS ENABLE ACTION Lack of forklifts and fewer trained staff led to the delay. Improving these can reduce cargo delay by 15%. 40 INSIGHT STORY DATA GRAMENER COMBINES These are memorable. People act on them. They go viral. This enables collective action.
- 41. 41 WE HELP PEOPLEREALLY UNDERSTAND DATA – LOGICALLY, AND INTUITIVELY 41 We use technology to automate Analysis, Visuals and Narration INSIGHTS Extract meaning using automated patterns AI & MACHINE LEARNING SERVICES VISUAL NARRATIVES STORYTELLING Creative ThinkingCritical Reasoning SOFTWARE THROUGH SERVWARE: augmenting human intelligence with technology STORYTELLING Binding visuals together into a logical story
- 42. 42 § What arethe most critical skills needed in your data science team? § What roles should you plan to hire and where should you scout for talent? § Tips and tricks for hiring your data science team, presented with real-world examples? § What are the essentials for seeding a culture of data? § How to form ‘data’ habits in your workforce? § Best practices to show when and how you can get started on this journey § Key reasons why data science projects fail § How to identify your projects and prioritize them § A standard 3-step framework for building your data science roadmap Get Business ROI from Data Science ADVISORY WORKSHOPS Create your custom Data Science Roadmap Build a Data Science Team to deliver Business Value Data Culture to promote Data-Driven decision making How to
- 43. 43 Recap : DataStorytelling • Industry Case studies • 4E Patterns Storytelling Patterns • Build Data Science Teams • Data Science Roadmap • Data Driven Culture Data Advisory workshop Why Stories • Aids Decision Making • Insights as Data Stories Recommendations • Build Storytelling skills • Process • Automate Storytelling
- 44. 44 What Next? • Readthese • Storytelling with data • Resonate • Show & Tell • Data visualization society Feel free to contact me at Naveen.gattu@gramener.com • Practice storytelling • Understand the context systematically • Review chart annotations with colleagues • Interact with experts outside your circle • Automate this in your dashboards Reach out for inspiration or help
- 45. 45 @naveengattu Thank You! gramener.com /naveengattu gramener.com/solutions Feelfree to contact me at naveen.gattu@gramener.com