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Big Data: selling the Business Case to the business

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Big Data: selling the Business Case to the business by Eline Brandt & Javier de la Torre Medina

Big Data, every company loves the idea of it, but often, selling the Business Case is a challenge. So how to build a successful Business Case for your Big Data initiative for the Business Users? This presentation is based on the most common objections one gets, and how to deal with them. We'll go through one of my most surprising projects, look at the lessons learned and how can we optimize the Business Case?


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Big Data: selling the Business Case to the business

  1. 1. Selling the Business Case So you can get the budget you need to keep doing what you do best: creating value with data 1
  2. 2. Skills: Skills: Meet the team 2 Business focused pre-sales. Helping customers build their business cases, doing the right things right and building bridges between business and IT Eline Brandt Consultant @OpenClosedBook Certified Cloudera Administrator for Hadoop, delivering POCs for EMEA customers and designing architectures with customers. Javier de la Torre Consultant @JaviTorreMedina Bridge builder Pitching 80% 95% Endless curiosity 70% Big Data Data Architectures 80% 95% Tech Hands-on 90%
  3. 3. 1 Agenda 3 --- 1 --- --- 2 --- --- 3 --- --- 4 --- --- 5 --- Understand the Business problem Know your Ecosystem How to Build a Big Data architecture? Show the Value (with a POC) Q&A
  4. 4. Introducing: University use case What were their problems? Difficulty retaining students Lack of student satisfaction and progression Scattered Data Inconclusive results from research
  5. 5. Introducing: University use case 5 Tech approach Business approach Tech approach - Understand what is going on right now - Have a unique source of truth - Eliminate the data silos - Use all the data, to improve the business approach Business approach - Improve curriculum to improve student progression - Understand student needs for better student retention Mutual success
  6. 6. Big Data is the tool! 6
  7. 7. Big Data is not easy 7 Current solution Business expectations
  8. 8. 8 Doing Big Data Successfully is Hard “Only 27% of respondents described their Big Data initiatives as ‘successful’ and only 8% of respondents described them as ‘very successful.’ In fact, organizations were found to be struggling even with their Proof-of- Concepts (PoCs), with an average success rate of only 38%.” Capgemini Consulting, Cracking the Data Conundrum. 2015
  9. 9. Big Data from Challenge to Success 9 No convincing business case for moving further Agile & Elastic cloud environment Ineffective alignment of Big Data and analytics teams across the organization Unified Platform for all users Lack of Big Data and analytical skills Managed service & analytical tools Most data locked up in difficult to access legacy systems Smart Data movement Data in Silos, scattered across the enterprise Unified Data Lake
  10. 10. Pitching the Business Case 10 Tell the story - Identify the problem you will solve - Spell out the Business needs - Make the benefits quantifiable and measurable Know - Know what they (only) know - What influences them - What they need to know to make a decision - Their roadmap & strategy Purpose Showing the value: the key to approval - Inform - Convince
  11. 11. • Identify the problem you will solve • “Through the proper use of data analysis you will be able to gain insights into possible reasons why students or which students are struggling” • Spell out the Business needs • “With these insights you will be able to fine tune the policies focused on student retention and progression” • Make the benefits quantifiable and measurable • “By using the insights the data analysis gives you, you could bring up your student retention by 10% within 1 year” Tell the Story 11
  12. 12. Know 12 What influences them Roadmap & strategyWhat they know Need to know
  13. 13. Know 13 What influences them Roadmap & strategyWhat they know Need to know What is their background? Are they experienced?
  14. 14. Conceptual architecture 14 Actionable Events Streaming Engine Data Lake Enterprise Data & Reporting Discovery Lab Actionable Metrics Actionable Data Sets Input Events Execution Innovation Discovery Output Data
  15. 15. Practical Architecture 15 Actionable Events Streaming Engine Data Lake Enterprise Data & Reporting Discovery Lab Actionable Metrics Actionable Data Sets Input Events Execution Innovation Discovery Output Data Structured Enterprise Data Notebooks/Analytic Services Object Store Hadoop/HDFS
  16. 16. Data Reservoir (t = 0) 16 Data Warehouse Reporting Data
  17. 17. Data Reservoir (t = 1) 17 Data Warehouse Reporting Big Store Data Batch processing of data moves to Hadoop and enables more cycles for analytics in the DW 2 Other diverse streams of data enter the Hadoop reservoir for processing and correlation or exploration 1
  18. 18. Data Reservoir (t = 1) 18 Data Warehouse Reporting Data New data sets are continuously generated and moved for both mass-comsumption and further analysis in the DW 3
  19. 19. Data Reservoir (t = 2) 19 Data Warehouse Reporting Data Cache Data Reservoir
  20. 20. Data Reservoir (t = 3) 20 Data Warehouse Reporting Data Real Time Cache Data Reservoir Data is transported and streamed 1 2 Without moving it, data is exposed (sometimes aggregated) to the real-time DW 3 Without moving it, data is exposed to Hadoop processing enabling an up-to-date view 4
  21. 21. Data Reservoir (t = 4) 21 Data Raw data is flushed into the Hadoop store for long term storage and analytics on raw data (plus aggregation) 2 New data sets (aggregations and more) are continuously created in the DW for mass-consumption and consolidated analysis 3 As data is flushed, aggregates are materialized in the DW to avoid double work 1
  22. 22. Know 22 What influences them Roadmap & strategyWhat they know Need to know What is their background? Are they experienced? Stakeholders Newspapers Gartner, Accenture Success stories of competitors
  23. 23. Know 23 What influences them Roadmap & strategyWhat they know Need to know What is their background? Are they experienced? Stakeholders Newspapers Gartner, Accenture Success stories of competitors Costs Usability Budget Legislation Policies
  24. 24. Know 24 What influences them Roadmap & strategyWhat they know Need to know What is their background? Are they experienced? Stakeholders Newspapers Gartner, Accenture Success stories of competitors Costs Usability Budget Legislation Policies Vendor preference Cloud first Make or buy
  25. 25. Purpose 25 INFORM CONVINCE Give them enough information so they can make a decision Outline the benefits Focus on data Highlight the problem, offer the solution Call to action Subjective Showing the value
  26. 26. Example: Pitching to Inform 26
  27. 27. • Initial issue: University was disappointed with its 67% one-year student retention rate, costing 6.5$ millions in lost revenue each year • Discovered: students who also works at campus, has a 85% retention rate. A 200.000$ investment in jobs saved 2$ million in retention costs over the four year course POC Results 27
  28. 28. Summary 28 Analyze the Business Problem Customize your pitch Know your team Build Smartly Business Problem
  29. 29. ¿Questions? 29
  30. 30. Thank you! 30

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