An Example Tableau Security Model

My experience navigating Tableau security as a novice…

I recently upgraded a Tableau 10.1 estate to Tableau 2018.1.  I used the opportunity to completely rework the security from the ground up.

When starting out, much of the guidance I found on the net was focused on the many individual components that make up a Tableau estate.

While I’m certainly not claiming what I have done is best practice, I hope it will trigger some ideas and serve as a starting point for your own implementations.

This guide doesn’t cover licensing although that is something which is definitely worth understanding if you are implementing a Tableau security model.  If you need to learn about Tableau licensing then this article does a great job of explaining both the old and new models.

Continue reading

Top 10 concepts from Netflix’s culture of ‘Freedom and Responsibility’

Back in 2009 Netflix released a slide deck called ‘Freedom & Responsibility’ that explained some of their strategy and culture.

Facebook COO Sheryl Sandberg said that it “may well be the most important document ever to come out of the Valley”.

I first heard about it a few years back when I read Dave Coplin’s brilliant (and succinct) book Business Reimagined, which you can download here for free.

‘Freedom & Responsibility’ was something that evolved at Netflix over many years.  Here are what I consider to be the 10 most notable ideas from the document.

Continue reading

Software Development & Broken Window Theory

broken_window

Broken Window theory goes something like this:

  1.   Some broken windows are left unrepaired in a neighbourhood…
  2.   People see this state of disrepair and feel like no one cares about their surroundings…
  3.   Because nobody cares, people feel like they can cause further damage without repercussion…
  4.   Further damage is done, perpetuating the cycle.

We see this exact same pattern in all areas of life including software development.

Continue reading

Data Dictionary

Data Dictionary w/ Search Functionality (2016)

At CNA-Hardy, I put together a data dictionary for an Underwriting/Actuarial MI system I looked after.

I created the data dictionary in Excel and put a search facility built in.  With around 250 calculations and attributes it made understanding and troubleshooting a lot easier.

data-dictionary-img

The .xlsx version of the tool can be seen here.

The .xlsm version which also filters the rows can be found here.

 

Technical Debt

Technical debt is a metaphor that equates software development to monetary debt.  In my opinion it is one of the most crucial concepts to be aware of when planning projects or road-maps.

Imagine that you have a project that has two potential options; one is quick and easy but will require modification in the future, the other has a better design but will take more time to implement.

In development, releasing code with a ‘quick-and-dirty’ approach is like incurring debt – it comes with the obligation of interest, which, for technical debt, comes in the form of extra work in the future.

Just like monetary debt, technical debt is interest-bearing and compounds.  You always have the option to pay down the debt (long-term thinking) or to take out additional credit (short-term) but your project can become insolvent where the only option is to write-off the debt (re-write from scratch).

To summarise, it is a debt that you incur every time you avoid doing the right thing like removing duplication or redundancy.  It will add an overhead to everything you do from thereon in, whether that is troubleshooting, maintenance, upgrading or making changes.

[Some parts taken from MartinFowler.com and Techopedia]

Cubes (& PowerPivot) vs Traditional Excel Pivot tables

This is a question I’ve been asked a few times before; “apart from handling larger data-sets, what do you get with cubes that you don’t get with pivot tables?”

This isn’t an exhaustive list but it covers what I think are the most important functionality differences.

#1 – Cubes are organised into dimensions / attributes.

With traditional Excel pivot tables, you are building on top of a flat data-set and as a result you will get your dimensions / attributes / measures in one big list.

You can take a flat data-set straight into PowerPivot as well (and there are definitely use cases for doing this) but if you build a dimensional model (as you are forced to do in SSAS MD) you will logically organise your reporting attributes by the correct dimension and make a much more intuitive model for the user.

dim-vs-flat

#2 – Hierarchies.

Traditional pivot tables will show you the implicit relationships in your data but you will have to create a column for each attribute you wish to show.  You can then tidy this up by adding manual grouping but the whole process is very clunky.

With PowerPivot & Cubes, you explicitly define hierarchies and when exploring the data you can drag this single field in and have instant drill-up and drill-down, plus on large dimensions performance is going to be significantly better.  Displaying natural relationships this way is key in making the data easy to understand at a glance.

ssas_pvt_hier_v2

#3 – MDX / DAX.

In cubes and PowerPivot you can write your own MDX & DAX functions to build more complex calculations.  There really isn’t any direct comparison for MDX/DAX for traditional pivot tables although when creating a measure in a traditional pivot table there are some options available (as shown below) but nothing with the advanced analytics capability of MDX/ DAX.

pivot_measure_opts

Users also sometimes build calculations outside of the traditional excel pivot but this then means the pivot table has to remain static otherwise the calculations are overwritten.

#4 – Client Tools.

Traditional Pivot Tables are themselves the end-product.  PowerPivot provides an upgrade path to SSAS tabular and cubes have a whole host of cutting edge reporting tools that can use them as a source.  Personally I don’t like excel pivot tables for browsing cubes and have tried out some different reporting tools that can connect to SSAS.  My favourite experience was delivered by Pyramid Analytic’s BI Office solution, an example of which can be shown below.

Power View inside Excel is not bad but in my opinion it’s not the easiest thing to use and I can’t see it gaining wide adoption.

bioxl

#5 – Other Considerations

There are plenty of other important considerations such as

security,
pre-aggregation,
partitioning,
formatting of dimensions/measures,
deployment possibilities

…and of course scalability but hopefully the above should demonstrate to people new to BI that SSAS offers key functionality benefits compared with pivot tables.