The Progress of Mobile Business Intelligence

This Infographic from Domo in 2011 tells a story about the ROI of mobile business intelligence. I’ve enclosed it as-is.  It reflects the fact that I’m always being asked, at the start of any Business Intelligence project, whether the data can be mobilised.

The optimism of the infographic is interesting, since the figures shown below should be borne out by the time of writing this blog, which is March 2013.

infographic 

In order to get a better idea of mobile Business Intelligence adoption, I suggest that you look at Howard Dresner‘s Mobile Business Intelligence Survey, which he conducts on a yearly basis.  To summarise, despite the earlier optimism of the original infographic, penetration of mobile BI today is modest, with majority of organizations report that fewer than 10 percent of users have access. As you might expect, smaller organisations have higher adoption of the new technology, with 20 percent of small business participants report that their mobile BI penetration is 81 percent or higher. So penetration in the small organizations is significant today. The take-away point is that there is a disparity between ambition and reality, where businesses are concerned.
Dresner’s research makes a calmer estimate: half of even the largest of organizations will be in the 11–20 percent band of using mobile Business Intelligence by 2015. There is a great deal of enthusiasm for mobile projects. In my experience, people can get distracted by the shiny-shiny new devices or applications, but forget that they still require clean, tidy data. Putting bad data on a mobile device doesn’t make it any better, and this is a key point which the whole mobile Business Intelligence discussion seems to miss.
In other words, on twitter, I’d like to see the hashtags #CleanData and #RightData used just as much, if not more, than the hashtags #BigData and #MobileBI, which are fairly ubiquitous where Twitter is concerned. These problems are less fun, and are often hard to do, which explains why they are much less popular than projects which involve shiny gadgetry.

 

SQLUniversity: PowerPivot, Tableau and Jedi Knights

This blog will show an overview of how I mobilised PowerPivot using Tableau. I’ve previously given this session at SQLBits, NEBytes Microsoft Technology User Group, and SQLServerDays in Belgium but thought it would also be useful to supply the files for you. The steps are very simple since I intended to show the end-to-end solution simply as a proof of concept, as follows:

  • creation of a PowerPivot which mashed up UK Census data with geographical data
  • creation of the report in Tableau
  • deployed to Tableau Public for consumption by mobile devices such as the iPad
The example was deliberately kept simple in order to prove the concept of PowerPivot being mobilised. 
The data sample involved mashing up two sources:
  • Jedi Knight census, data, which can be downloaded from here This is a basic file but the final PowerPivot can be downloaded from a link later on in this article
  • Geonames offer an excellent free download service, which you can access here
The Jedi Knight data, along with the geographical data, were joined using the outcode of the postcode data. If you need more definitions of the UK postcode system, I’ve previously blogged about this here.  
Essentially, a very simple RELATED formula was used in order to look up the latitude and longitude from the UKGeography table, and put it into the Jedi Knights data, and produce the necessary data in a simple Excel table. The formula looks like this:
=RELATED(UKGeography[Latitude])
=RELATED(UKGeography[Longitude])
Once these very simple formula were put in place, it was time to load the data into Tableau.
Tableau can take both PowerPivot and Excel data – which driver to use?  I used version 6 of Tableau. Whilst this version of Tableau does see the PowerPivot correctly as an Analysis Services cube, it does not always read the date as a ‘date’ type, but instead as an attribute. There is a forum posting on the Tableau website which tells you how to fix this issue, which involves changing the date so it appears as a measure, which means it can then be used for trends and so on. 
However, I wasn’t comfortable with this solution because I like dates to be in date format. I’ve also run into this issue at customer site, where the customer wanted to use SSAS as a source and Tableau as the presentation layer. They were data-warehouse savvy and didn’t like the ‘measures approach’ fix. 
On customer site, I got around it instead by using the Excel data source, and importing all of the PowerPivot columns into an Excel 2010 sheet. By doing it in this way, date formats were preserved. In this example, I didn’t have date format so it didn’t matter – but this is a useful tip for the future if you are using PowerPivot with Tableau. The final data, in an Excel PowerPivot, can be obtained in zip format here or if you can’t access it, please email me at jenstirrup [at] jenstirrup [dot] com.
Once the data was accessible by Tableau, I used the Tableau Desktop version to upload the data into Tableau’s memory. I did this so that I could eventually upload the Tableau workbook to Tableau Public. The instructions to save to Tableau Public are given here
Once the data was in Tableau Public, I just needed to access the data using the Safari browser on the iPad. In case you are interested, the demos are publically accessible and you can access the final result by clicking on the hyperlinks below.
I hope that’s been a useful overview of PowerPivot, and the ease of which it was mobilised. This blog forms a use case of how it might be useful to use PowerPivot, since I think that people sometimes need examples of how PowerPivot can benefit them. In this case, the clear benefit of PowerPivot is to provide an easy way of mashing up different data sources.
I look forward to your comments and thank you for sticking with me for the PowerPivot SQLUniversity discussions!

Mobile Business Intelligence – Try it out!

Thank you to everyone who attended my SQLBits ‘Mobile Business Intelligence in Action’ session recently. If you are interested to try out Mobile Business Intelligence on your iPad or mobile device, here are the links below:

Jedi Knight Actuals of UK Census 2001 Dashboard 
Jedi Knights Percentage of UK Census 2001 Dashboard
AdventureWorks Sales by Geography Dashboard
AdventureWorks Actuals Sales
AdventureWorks Analysis Dashboard

I haven’t tried this on every browser and every device, so I would be very interested in your feedback.
I look forward to hearing from you. Please leave a comment below, or email me at jenstirrup [at] jenstirrup.com

Representing data about the iPad

The current blog will take three different ways of representing the same data set, in order to see how it can be done simply and clearly – or not so clearly. I have taken some samples, and reworked them as a progression throughout this blog.

Although I am discussing the iPad here, this is not a preview about my iPad and Mobile Business intelligence sessions which I’m delivering at SQLBits session in October, or my User Group sessions in Leeds and Surrey this year; however, obviously the iPad is very much in my mind, hence the perpendicular topic of this blog!

The dataset is interesting because it aims to show the impact of the iPad announcement on notebook sales. This study was conducted by NPD, Morgan Stanley Research. CNN Money has written a short article on the impact of the iPad on netbook sales, which proposes that the iPad is at least ‘partially’ responsible for the decline in netbook sales. The rather dramatic bar chart, which underlines this point, is given here:
US-notebook1
There are a few issues with the bar chart:

 – The axis doesn’t go from 0 – 100%, which I would expect, given that it is supposed to show percentages. This skews the results slightly; for example, the 70% seems higher.
– 3D gradient issues don’t add anything. Sometimes 3D can make an image look more ‘pretty’. Here, the 3D does not add anything ‘pretty’ or enhance anything about the message of the data
– it’s not clear why the data has been represented as distinct categories when time is continuous rather than discrete
– the big pink arrow shouldn’t have been necessary; the graphic should have been enough.
– there is nothing to make the negative value stand out, or to distinguish it in any way.

There have been other examples of the same data, re-visualised. Here is an example from a wonderful infographic, which has been completed by the Focus Group. I have taken an excerpt of it here since the whole infographic is not the focus of this blog:

iPad and Notebook sales by the Focus Group

The above infographic solves some of the issues of the earlier version, which was reproduced by CNN money.

– There is no 3D
– The big arrows have gone

However, although it is visually appealing, it does repeat some of the earlier issues found in the CNN money chart, since the scale still does not reach 100% on the Y axis. Further, it also introduces some new issues:

– The black background might be visually appealing, but as a ‘best practice’, a white background is better. This allows the representation of the data to dominate the scene, not the background or other non-necessary items.
– hatched lines replace the arrows, to denote the time of the announcement of the iPad and the actual release of the iPad. This is an issue because it is slightly jarring to the eye.
– the month timeline isn’t evenly marked in terms of months; it is therefore difficult to ascertain if the data is skewed horizontally in any way.

In order to improve these representations of the data, I have used Tableau in order to create a simple line graph. This was all that was needed in order to get the message across, without skewing it or obscuring it in any way. Here is my example below, which can also be found on the Tableau Public website:

iPad and Notebook Sales

I have removed the issues found in the earlier visualisations and added some further enhancements:

The negative growth percentage has been highlighted with red colouring
added in clean annotations which do not obscure other parts of the data visualisation
ensured that the Y-axis shows 100% so that the data is not skewed
used a line graph since the X-axis is continuous, not discrete
removed the black background to emphasise the components of the data that provide the message of the data

Although the data visualisation has been improved, there are still contextual answers which the graph cannot answer:

– what about the impact of the iPhone, or other tablets?
– what about the impact of the time of year e.g. post-Christmas sales?
– what about the impact of the impending recession?

Therefore, the initial analysis as described by CNN money simply provided a ‘headline’ message, and further analysis would need to be conducted in order to answer the question more fully. That said, a proper visualisation of the data is a useful tool towards getting the ‘bigger picture’ right, as well as the ‘smaller picture’.

I hope that this was interesting, and look forward to your comments.
Jen x