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5 Steps To Measure ROI On Your Data Science Initiatives - Webinar

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5 Steps To Measure ROI On Your Data Science Initiatives - Webinar

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Gramener's Chief Decision Scientist & Co-Founder Ganes Kesari conducted an exciting webinar on how to measure ROI on your data science initiatives.

In this webinar people from the C-suite level CEO, COO, Directors, Managers across various industries joined.

Ganes Kesari covered the following points with industry examples:
-Identifying business use cases with a high impact
-Choosing effective success indicators
-Ascertaining that the consequences may be traced back to your data project

The attendees had a good time. Learnings from the webinar:
-Why do businesses struggle to get a return on their data investments?
-A straightforward framework for calculating the return on investment from your data projects
-Benchmarking of typical payback from data initiatives in the industry

To check out the complete recording of the webinar please visit:
https://info.gramener.com/5-steps-to-measure-roi-on-your-data-science-initiatives

To know more about data advisory check out:
https://gramener.com/advisory-consulting/

Gramener's Chief Decision Scientist & Co-Founder Ganes Kesari conducted an exciting webinar on how to measure ROI on your data science initiatives.

In this webinar people from the C-suite level CEO, COO, Directors, Managers across various industries joined.

Ganes Kesari covered the following points with industry examples:
-Identifying business use cases with a high impact
-Choosing effective success indicators
-Ascertaining that the consequences may be traced back to your data project

The attendees had a good time. Learnings from the webinar:
-Why do businesses struggle to get a return on their data investments?
-A straightforward framework for calculating the return on investment from your data projects
-Benchmarking of typical payback from data initiatives in the industry

To check out the complete recording of the webinar please visit:
https://info.gramener.com/5-steps-to-measure-roi-on-your-data-science-initiatives

To know more about data advisory check out:
https://gramener.com/advisory-consulting/

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5 Steps To Measure ROI On Your Data Science Initiatives - Webinar

  1. 1. How to Measure ROI from Your Data Science Initiatives Ganes Kesari Webinar, July 2021
  2. 2. Was success declared a bit too early?
  3. 3. Why is ROI so difficult? How to compute returns? How can you do it right?
  4. 4. 4 INTRODUCTION Ganes Kesari Co-founder & Chief Decision Scientist “Simplify Data Science for all” 100+ Clients Help start, apply and adopt Data Science @kesaritweets /gkesari
  5. 5. 5 Why is ROI so difficult?
  6. 6. State of the Industry: Data & Analytics investments vs returns Reference: ESIThoughtLab report – Driving ROI from AI We hear more about the money invested.. ..than about the returns generated On average, firms invested over $38 M in data & analytics Over 79% of these firms report negative or no ROI
  7. 7. Reason #1: Data often doesn’t lead to outcomes Reference: ESIThoughtLab report – Driving ROI from AI Data Insights Recommendations Actions Outcomes This chain is very often broken… Model stats are a poor cousin of insights Most dashboards don’t prescribe actions The actions don’t connect to a workflow Data sits idle and untapped “ 80% of projects don’t deliver outcomes in the industry Outcomes often go unmeasured
  8. 8. Reason #2: The outcomes are often not quantified as benefits Higher Efficiency Better decisions Improved branding “ One measurement is worth a thousand expert opinions. - Grace Hopper
  9. 9. Reason #3: The benefits may not have been caused by your project Disney+ launched in November 2019 had a stretch goal to acquire 90 million subscribers by 2024. It beat this goal in 14 months. Was this due to a brilliant marketing strategy, or was it just a pandemic windfall?
  10. 10. The 5 steps to compute ROI
  11. 11. What’s your data really worth? Data has a lot of intrinsic value… …or indirectly, the value will be wasted …but unless you monetize it directly..
  12. 12. 12 Define Success What are your target outcomes? Tally Results What’s your benchmark performance? Reckon Breakeven What are your net costs & benefits? Attribute Outcomes What led to the results observed? Measure Value How will you measure your outcomes? Success S Measure M Attribution A Reckon R Tally T The SMART framework to quantify value from data & analytics initiatives S M A R T
  13. 13. 13 Beverage manufacturer uses analytics to optimize plant cost Challenge “Drink It” is a leading global manufacturer of soft drinks. Over the past year, the company has observed a rise in bottling costs across regions. Approach • Statistical diagnostic analytics to identify recommendations for improvement • Command centre with predictive capabilities to run scenario-based simulations
  14. 14. 14 1. Define Success: What are your target outcomes? Organizational Financial Innovation Stakeholder Customer Pick the right mix of outcome categories…
  15. 15. 15 1. Define Success: What are your target outcomes? Finance Innovation Customer Organization Portfolio mix Stakeholder Revenue Margin Cash Flow Employees Investors Customer Exp. Business value Brand Equity Ops. Perf. ESG Organization Financial Innovation Stakeholder Customer Innov. culture ..and identify the attributes that your initiative must influence
  16. 16. 16 DRINK IT: Definition of success outcomes Strategic Objectives Users Outcomes • Optimize the global manufacturing cost • Improve resource and employee utilization • Streamline manufacturing process • Plant managers • Regional Leadership • Financial • Cost saving • Employee • Enablement • Utilization • Organization • Ops. Performance • ESG “Drink it”
  17. 17. 17 2. Measure Value: How will you quantify your outcomes? Finance Innovation Customer Organization Stakeholder Organization Financial Innovation Stakeholder Customer • Revenue growth • Gross margin • Operating expense • Receivable​s/ payables • New products launched • Revenue from new products • Employee innovation index • Attrition rate​ • Employee satisfaction​ • Stock Price​ • Return on equity • CSAT score,​ • Customer Lifetime Value​ • Acquisition Cost​ • Share of wallet​ • Time to Market​ • Process cycle time​ • Brand Salience​ • Reduction of carbon footprint
  18. 18. 18 DRINK IT : Measuring the success metrics through FISCO Finance Employee Organization Cost saving Enablement Utilization Ops. Perf. ESG • Manufacturing cost per case • Production line cost per plant • People empowerment • Employee productivity • Manufacturing process cycle time • Asset utilization • Reduction of carbon footprint Outcome Categories Success attributes Success metrics “Drink it”
  19. 19. 19 3. Attribute Outcomes: What led to the results observed? Causal factor 1 Causal factor 2 Causal factor 3 Causal factor 4 Outcomes Identify all factors that could influence your target outcomes
  20. 20. 20 3. Attribute Outcomes: What led to the results observed? A/B Testing DoWhy Causal Testing Reference: Microsoft GitHub report – DoWhy | An end-to-end library for causal inference A B 10% attrition 7% attrition
  21. 21. 21 DRINK IT: Causal diagram representation Manufacturing process Market factors Material cost Employee efficiency Manufacturing plant cost Process efficiency Transportation cost Labour cost Command Tower insights Employee productivity Seasonality Competitors Staffing levels Demand Material cost “Drink it” A/B Testing helped isolate benefits delivered by analytics
  22. 22. 22 4. Reckon Breakeven: What are your net costs and benefits? Gain Heads Cost Heads • Revenue, • Margin, • Cash flow • Building capabilities (Software & Hardware) • Effort • Time • Opportunity cost • Innovation • Employees, Investors • Customer experience, Business Value • Operations, Brand Equity, ESG Balance your qualitative benefits with quantitative measures
  23. 23. 23 Cost Heads: Account for obvious and non-intuitive expenditure People Efforts Technology Infrastructure Capability Building Opportunity Cost People Effort Technology Infrastructure Capability Building Opportunity Cost Technology team, business team, change management Hardware, software, infrastructure costs Data initiatives, Technology capabilities Return on best foregone option - Return on chosen option
  24. 24. 24 DRINK IT: Cost & Gain heads analysis Cost Heads People Effort Implementation + Training + Support + Change mgmt Technology Infrastructure Software + Hardware + Maintenance + Other infrastructure Capability Building Analytics delivery process + Workflow integration Opportunity Cost Projected benefits from lean process improvement Total cost: $4.25 M Gain Heads Finance Cost saving Asset utilization cost saving + Material cost decrease Employee Enablement 2.1 points Employee satisfaction score increase Utilization 8% increase in employee productivity Organizati on Process Efficiency 6% Improvement in process cycle time ESG 5% Carbon footprint reduction Financial savings: $4 M + Qualitative benefits “Drink it”
  25. 25. 25 5. Tally Results: What’s your benchmark performance? Average payback (years) across industries Reference: ESIThoughtLab report – Driving ROI from AI Compare ROI across the firm, against competitors, and the industry “ The average payback on data investments is ~17 months Average payback (years) by org D&A maturity 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Beginner Implementer Advancer Leader
  26. 26. 26 Payback in 1.2 years (Manufacturing industry average: 1.32 years) ROI: $ 4.00 M Investment: $ 4.25 M Benefits and Cost Summary Breakeven Benchmark DRINK IT: Benchmark ROI across industries and track progress “Drink it”
  27. 27. 27 How can you do it right?
  28. 28. 28 Applying the learnings to your organization For how many of your projects is the outcome crystal clear? Is your breakeven better than your industry average? Is there a KPI or a metric that signifies it? Are you confident that your project improved the metric? Do you have a handle on all your costs? 01 02 03 05 04
  29. 29. We regularly share our thought leadership through articles, talks and webinars 29 We regularly speak in events and conduct webinars to share knowledge on getting value from data Featured Talks Featured Publications When and how to build out your data science team How to beat resistance to AI projects: 3 steps for executives How NGOs can leverage AI without breaking the bank Why data leaders must play offense during COVID-19 We frequently publish articles in leading magazines to share insights with executives and practitioners Webinar: How to Build Successful Data Science Teams Webinar: How to structure your Analytics teams for the Best Outcomes Webinar: The best way to Choose your Data Science Projects Panel: Role of AI Strategy and Culture in Org Transformation The 5 roles that every data science team must hire Whiteboard Series: Executive insights in under 5 minutes
  30. 30. 30 Thank You! @kesaritweets /gkesari www.gramener.com Reach out for a free discovery session: https://gramener.com/data-advisory-workshop Ganes Kesari
  31. 31. 31 Planning starts early but realization happens at later stage • Clarity of Vision & Strategy Alignment what the organization intends to achieve and Alignment with the corporate vision and long-term goals. • Initiatives Planning & Project Prioritization How the initiatives are planned and governed to ensure business value. • Data Analytics & Data Consumption Generation & presentation of actionable insights from data(dashboards, visualization, data stories). • Adoption & ROI Adoption of the data initiatives. Quantification, and tracking of the value generated from data science initiatives. • Data Literacy Ability of the users to read, write, and communicate with data P L A N N I N G V I S I O N E X E C U T I O N R E A L I Z E V A L U E D A T A C U L T U R E Insights & Recommendation Action Most of the organizations leave their data initiatives at the action stage which not only makes them suffer in quantifying ROI but also makes their data initiatives less successful Reference: Gramener toolkit Data
  32. 32. Poll #1 32 Which of these is the biggest challenge for your organization? Here’s a short & simple poll to help you reflect.
  33. 33. Poll #2 33 Have you ever quantified ROI for your data initiatives? Here’s a short & simple poll to help you reflect.
  34. 34. Poll #3 34 Which of these 5 steps do you need help with? Here’s a short & simple poll to help you reflect.

Notes de l'éditeur

  • Photo by Robert Wiedemann on Unsplash
  • Photo by Waldemar Brandt on Unsplash
    Photo by Matt Duncan on Unsplash
    Photo by Perry Grone on Unsplash
  • Source: ESI ThoughtLab
    On an average, firms invested more than $38 Million each in data initiatives over the past year. This was 0.75% of their revenue.
    Leaders invested 2.6 times the average—more than $99 Million.
  • Data can be monetized in endless ways. But only one-third of companies are generating external benefits from available data. – Doug Laney, Forbes Photo by Nick Hillier, Fang-Wei Lin & Luke Chesser on Unsplash



    Direct monetization
    Licensing Data or Insights to Others
    Bartering or Trading with Data
    Enhancing Existing Products or Services with Data
    Digitalizing Existing Products or Services

    Indirect monetization
    Reducing risks and improving safety;
    Improving customer service;
    Identifying new prospective customers or markets; and 
    Solidifying business partnerships or customer loyalty. 
  • Projecting ROI : Ask "Why does this happen?“ at each node

    Visually depict the factors that could contribute to the observed effect.
    Ask why does this happen and brainstorm to determine the major causes
  • Projecting ROI : Ask "Why does this happen?“ at each node

    Visually depict the factors that could contribute to the observed effect.
    Ask why does this happen and brainstorm to determine the major causes
  • Give relevant example
  • Taking point: Less scores in Realize value and data culture because of low adoption rate
  • How can I derive business value from my data science initiatives?
    Which business problems should I solve first?
    What's the best way to quantify ROI from my data investments?
    How should I build a data-driven organization?
    How can I help my business teams make actionable decisions from data?
  • Yes and I succeeded at it
    Yes, but I couldn’t compute it fully
    No, I do not know how to do it
    No, I do not want to do it
  • Change the Question

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