Business Intelligence Barista: What DataViz Tool to Use, and When?
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Choosing a data visualization tool is like being a barista serving coffee: everyone wants their data, their way, personalized, fast, and perfect. Many organizations have a cottage industry of data visualization tools, and it's difficult to know what tool to use, and when. Different tools exist in different departments, and if it doesn't meet the user requirements, the default position is to go back to Excel and move the data around there. This session will examine data visualization tools such as SSRS Excel, Tableau, QlikView, Datazen, Kibana and PowerBI, in order to craft and blend your data visualization tools to serve your data customers better.
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Business Intelligence Barista: What DataViz Tool to Use, and When?
- 1. Datavis Barista: How to choose what dataviz tool, and when Jen Stirrup Founder, Data Relish Level: Intermediate
- 2. Who am I? Jen Stirrup
- 3. What dataviz tool to choose, and when? • SSRS • Excel • Tableau • Power BI • Datazen • Kibana
- 4. What makes a good Visualisation? • Effective • Accurate • Efficient • Aesthetics • Adaptable
- 14. What makes a good Visualisation? • Effective • Accurate • Efficient • Aesthetics • Adaptable
- 15. Excel, Datazen, SSRS, Power BI
- 16. SQL Server 2016
- 17. SQL Server 2016
- 18. SQL Server 2016
- 19. Datazen
- 20. Treemaps in Power BI
- 21. Waterfall Charts in Power BI
- 22. Example in Power BI, SSRS • Demo
- 23. Why R? • most widely used data analysis software - used by 2M + data scientist, statisticians and analysts • Most powerful statistical programming language • flexible, extensible and comprehensive for productivity • Create beautiful and unique data visualisations - as seen in New York Times, Twitter and Flowing Data • Thriving open-source community - leading edge of analytics research • Fills the talent gap - new graduates prefer R.
- 24. Growth in Demand • Rexer Data Mining survey, 2007 - 2013 • R is the highest paid IT skill Dice.com, Jan 2014 • R most used-data science language after SQL - O'Reilly, Jan 2014 • R is used by 70% of data miners. Rexer, Sept 2013
- 25. Growth in Demand • R is #15 of all programming languages. REdMonk, Jan 2014 • R growing faster than any other data science language. KDNuggs. • R is in-memory and limited in the size of data that you can process.
- 26. What do I need to install? • Install R – www.r-project.org • Install Rstudio – www.rstudio.com • Handy Shortcuts • Tab – autocomplete of available functions • Control and Up Arrow – History • Control and enter – executes the line of code
- 27. What tools do we have in R? • 80% of your time will be spent preparing and wrangling data • The remainder of your time will be spent complaining about it. • dplyr: the essential data manipulation toolset • In data wrangling, what are the main tasks? • – Filtering rows – Selecting columns of data – Adding new variables – Sorting – Aggregating
- 28. Example in R • Demo
- 29. The Big Data problem • Reaction Time • Enrichment • Insights • Optimize for query, not for storage.
- 30. Can you check the errors between 12.02 and 12.04 yesterday?
- 31. Can you check the errors between 12.02 and 12.04 last Friday? …. Are you kidding me?
- 34. What is Kibana?
- 36. Kibana • It is highly customizable dashboarding • It is constituted of panels: – Time picker / Query / Filtering – Charts / Table / Text
- 37. Flexible analytics and visualization platform Real-time summary and charting of streaming data Intuitive interface for a variety of users Instant sharing and embedding of dashboards
- 38. To better understand large volumes of data.. • easily create bar charts • line and scatter plots • Histograms • pie charts • maps.
- 39. To better understand large volumes of data.. • easily create bar charts • line and scatter plots • Histograms • pie charts • maps.
- 41. Default Chart Types Chart Type Basis Values Types Purpose Histogram Timestamp based Count, Mean, Total Barlines, stacks, percentages Queries Table Paging Fields list Highlighting, sorting Fine grained analysis Pie Charts Terms Missing terms, other Doughnut, legends, tables Proportion
- 44. Summary • SSRS • Excel • Tableau • Power BI • Datazen • Kibana
Notas do Editor
- We will look at: introductory R and why it's useful, and where to go for more information. We will learn statistics and R by looking at: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. We will loosely base the curriculum on the Khan Academy statistics course, but we aim to help the curious, the scared, and the rookie.
- Effective: the viewer gets it (ease of interpretation) Accurate: sufficient for correct quantitative evaluation. Lie factor = size of visual effect/size of data effect Efficient: minimize data-ink ratio and chart-junk, show data, maximize data-ink ratio, brase non-data-ink, brase redundant data-ink Aesthetics: must not offend viewer's senses (e.g. moire patterns) Adaptable: can adjust to serve multiple needs
- Effective: the viewer gets it (ease of interpretation) Accurate: sufficient for correct quantitative evaluation. Lie factor = size of visual effect/size of data effect Efficient: minimize data-ink ratio and chart-junk, show data, maximize data-ink ratio, brase non-data-ink, brase redundant data-ink Aesthetics: must not offend viewer's senses (e.g. moire patterns) Adaptable: can adjust to serve multiple needs
- https://powerbi.uservoice.com/assets/82213473/treemapNoOwl.gif
- Whether measured by more than 6,100 add-on packages, the 41,000+ members of LinkedIn’s R group or the 170+ R Meetup groups
- Filtering rows creates a subset Columns are regarded as variables
- Frustrating, huh?
- Sad
- Web front end to search / graph and more Advantages: A better UI Better than the old frontend Ruby / framework sinatra