Tuesday, 10 October 2017

Vegas Data17 - Opening Keynote Brief Notes

Adam Selipsky
61,000 customers in 100+ countries
Data Myths - created to replace the unknown and create reasons for what happens
#1 AI will replace the analyst. Actually AI is likely to assist the analyst. Tableau is smart software. Drag and drop of Clusters and Trend. Natural Language Processing will take out some of the technical barriers.
#2 Data is only for the analysts. More data programs in Higher Education. 800 million knowledge workers. Excel used to be taught in university, now school children use it. Tableau aiming for the same.
#3 Data governance means no. Data is valuable so needs to be protected. But that’s the old model - the bottleneck has been removed. Governance should means secure enablement.
Honeywell deals in human safety. It needs to have appropriate data ready at appropriate times. Giving multiple environments with clear transitions between them helps to manage creation at all levels. 20,000+ users within two years as the right mix of options gives governance and flexibility.
#4 There can be one, perfect source of truth. Innovation is so rapid that you can predict the different sources and combinations of these. We live in a world of many sources of truth. We have to embrace that. Tableau invests in all of the flexibility that you need. Go past the hype and try the tools. 

Francois Ajenstat
Myth - BI platforms take power away from the people. Often designed for specialist. Tableau focuses on people. 

100+ features added in Tableau over the last year. 50+ came from the community.

10.5 in beta today
Viz in Tooltips 

Data Engine
Hyper is the new Data Engine. Tableau Data Engine was great. But time to scale up. Hyper instant compared to 25 second load where heavy calculations are used. Extract creation will also be massively improved (3 million rows live SQL) double the speed in Hyper. Hyper doesn’t sort the data like a TDE. 500 million rows isn’t possible with TDE, Hyper is possible. No migration necessary - just use 10.5 and it’s there.
Data prep
Still working with existing data prep partners but not everyone has this - introducing Maestro. Data profiles gives you a sense of your data. Filter outliers with normal exclude functionality that you find in Tableau. Grouping through Fuzzy Clustering to sort poorly entered data. Clear view of the changes made through the transformation. Drag and drop joins and unions. Very simple join and cleaning of joins. Maestro in beta this quarter
Extensions API
Makes additions in to Tableau rather than Tableau in to other applications. Dashboards becoming their own applications. Dashboard Extensions create two-way communication with the data source.

Tuesday, 6 June 2017

What’s Next for Tableau - Andrew Beers

“This next hour isn’t about me, it’s all about you”

Three principles and how Tableau is answering them:

  1. People who know the data should ask the questions
Profile: bring domain knowledge rather than the SQL-coding skills to get to the data to answer the questions

2. Software should be designed for deeper thinking
Profile: Not simplistic products, simple ways to do complex things
After the first five minutes of simplicity there has to be more [complexity]...to let you have the impact you want to have

3. Analytics at Scale can drive change
Profile: Analytics at scale can solve the big problems in the world

Last 12 months in the Tableau world has gone from 10.0 to 10.3
Half of the 130 improvements came from ideas on the Ideas forum
Loving that Tableau is on a ‘quarterly’ cadence

So how does Tableau help us achieve this?

Data Prep: “If you can’t connect to the data then you can’t analyse it”
So need to connect to all data (pdf, cloud files, Eloqua, Service Now etc)
Hybrid engine: Fast Live & In-memory, Cross DB Federation
Data Prep: Self Service, export to csv

PDF - shows all the tables in the pdf when loaded into Tableau. Can pivot
JSON - Tableau detects the structure. When connection to JSON, if you have multiple rows then Tableau will create the LoD measures removing the duplication for you

Answer More Questions
More ways to change lines (regular, stepped or jump lines)
Tooltip selections: Using dimensions in the tooltip to enable highlighting other other marks that allow the common elements to be highlighted.
Dashboard element sizing - ‘Distribute Evenly’
Python / R integration - use SCRIPT_REAL function

Collaboration at Scale
Sharing / Governance
Anywhere Web & Mobile
Tableau is now watching how the whole community is using the common joins done to ‘recommend’ what tables to join on
Alerting - can schedule regularity of alerts

Big changes coming soon:

Linux: cost saving / ease of management / technical skills of IT / more security control
Hyper: support for very large data sizes (billions of rows), faster queries, much faster extract creation, scalable (mid summer public beta)
Maestro: Complete visibility for immediate understanding (coordinated views aid understanding)

Saturday, 25 March 2017

Iron Viz - We can't breathe

It’s that time again with IronViz feeder competitions starting up again. Every year, I take this opportunity to explore a technique I haven’t used before, or alternatively, a subject that I care about.

As a keen cyclist in London, Air Quality is a really important subject to me. With increased cycling infrastructure, the move to electronic cars and banning older vehicles from the city centre, I was expecting to download a set of data that showed dramatic falls in the levels of pollutants that are all around us Londoners. Nothing could be further from the truth.

Using data from londonair.org.uk, I looked at the Borough level of detail as there are only three or four monitoring points per Borough. The data proved a challenge with sporadic measurement across an inconstant set of pollutants. This meant that finding trends and understanding geographical patterns using the new Spatial file connector in Tableau was a good challenge.

The biggest break-through I had was whilst researching the subject, was coming across the European legal limits that have come in to force across different pollutants, at various points since 2005. This is shown as the reference line on the trend data to show how often the limits are broken across London.

The answer in many cases is the daily average often doesn’t fall below some of the limits. Worrying stuff!

The spatial mapping allows the reader to see the impact on the city centre. Although Lambeth, outside of the true centre of the City, seems to be hit particularly hard by pollution levels.

As with any IronViz entry, it’s a chance to use a little imagination and have some fun with charting. When I played with some charts, it looked like exhaust fumes so, I kept it and features it at the top of my visualisation.

Overall, the visualisation has led me to want to dig in further in to this subject and what I can do to start making a difference as currently there isn’t any improvement of any note.