1) Data blending is the process of joining and transforming data from multiple different data sets and sources to provide a more complete picture for making critical business decisions. 2) It is important because companies have dozens to hundreds of different data sources that are rarely analyzed together, leaving decisions to be made without a full picture. 3) Skills needed for data blending include using tools like Google Sheets to perform joins, groups, and transforms on data from databases and other sources to produce merged data sets for analysis.

1. Data Blending for critical
business decisions
ITEM, Kyiv, Mar 2018

2. About Me
● Yevgen Tsvetukhin
● Product manager in IT Consultancy company Railsware.
● Managing IT projects for 10+ years with businesses(mostly startups) from USA
and EU using latest best practices Agile, Scrum, Kanban, Lean Startup.
● Also taking part in managing company, Operations, HR, Finances.
● Product manager of mailtrap.io

3. Railsware Products
● mailtrap.io - fake smtp server that isolates emails from your
dev, qa, staging envs, from real production customers.
● Smart Checklist for Jira - Create Acceptance Criteria and
Definition of Done Jira checklists. Add other ToDo lists from
issue view or using Markdown editor
● +3 other products in MVP stage
● +3 products in Proof of concept stage

4. What is Data Blending?
The simplest:
Join data from different data sets
+ Transform
+ Map&Reduce
Ideally:
+ Real-time
+ Fully automated

5. Why Data Blending is so critical?
- Companies have from 50 to 1000 data sources.
- 1 data source very rarely usefull
- Many data sources are managed by different people/services in different
formats, so not analyzed together.
- Decisions without data are blind/gut feeling guessing.
- When good data is easily available, it’s used much more often for decisions
- Product Manager can blend data with deep product
understanding and find awesome cases

6. What skills are needed?
- Google Spreadsheets (MS Excel) formulas
- Start with basic, but go to advanced level
- Understanding of databases
- Simple SQL like join, group by
- Write scripts to auto-upload external data regularly
- Can be delegated as simple task to engineer

7. Super simple example of Data Blending

8. Analytics events log

9. Transactions of Payment provider

10. Overview result with $

11. Real example of Data Blending
(partial data)

12. Transactions from Payment provider

13. Users usage analytics

14. Few simple formulas :)

15. Merged: Usage with payment data

16. Last 13 weeks usage heat map

17. Segmented Weekly Usage

18. Filter & Find Patterns: Week#1

19. Filter & Find Patterns: Week#1-2

20. Build nice chart

21. Key decisions & findings
- Week#1 has most drop-offs by 1-2 people testing
- 3+ people testing drop-off very rarely
- Focus customers teaching & emails on Week#1
- Most of cancellations didn’t pass 8 actions/week
- Product is growing usage wise :)

22. Thank you!
Questions?