Timo Elliott's demonstration dashboard

Neat little demo put together by Timo Elliott (@timoelliott) from Business Objects using their Xcelcius product as a - think I'm inventing words here - mashupable object. He plonked the dashboard he made, on his blog with a number of controls that allow you - YouTube style - to share via email, twitter or cut and past as an object in html. This kind of functionality will get even easier when HTML 5 gains traction, but as Timo shows its do-able right now. Doesn't matter that it's a dashboard, the idea could work with any report or report part. I really like the ability to click to expand to full-screen view - just link clicking on a web page section in iPhone's Safari, a visual drill-down (or up).

So with a click on Timo's blog and a paste, here is Timo's demo Dashboard (his blog has some context that's fun to read too). To start with our blogger hosted blog isn't coping with the width of the element so well, but Timo kindly set a new code block that does fit - Thanks Timo.

An experiment on Twitter

For a long time a few of us, and not just at Monash, have wondered how a real time text feed - perhaps in RSS format - might be applied to BI systems. Now that social networking sites like Facebook/Twitter/Friendfeed exist and are becoming widely used the idea has a bit more traction with people we talk too - nothing like concrete experience to help people understand what you are talking about.

I've just set up a Twitter account that I'm going to use to develop a prototype system to demonstrate how a "feed" of text updates might be useful in a BI context. This feed (@monashbiindex) will contain updates and observations on data collected as part of out BI Index project.

Right now, all it will report on is a single number - the number of on-line job ads placed in Australia for positions related to business intelligence and data warehousing. I've been collecting this number (off and on) since 2005. It's quite interesting, there are weekly wobbles (up on Thursday and Friday) and significant seasonal variations too (up before and after the end of the financial year - way down in January). The analysis we are doing will soon expand (just like any dimensional data set) to include more detail like locations, industry sectors, job categories and the like. Later we might extend it to other countries but right now we'll stick to Australia.

So, I've got a nice little automated app, that will at 9:00 Am each morning go to seek.com.au, do a search and grab from the resulting page the total number of jobs. It records that number in a database - along with the data and time. Then a 'reporting' app fires up and does a simple day by day comparison of the number to the previous days and posts a tweet. Much better than the Excel macro's I've been messing with since 2005! Once I'm happy with how that's working, I'll extend the range of topics tweeted on to include a wider range of temporal tweets (end of week, end of month, end of season summaries), link the tweet to reports (no 3D donut charts I promise) and start to build agent style data monitors that look for exceptions, to demonstrate how a twitter style feed might be used for reporting the results of data mining algorithms.

POD

P.S. Oh, later the "Index" will include more measures of health than job ads. We are planning a regular survey of the "industry" and also a stock market index - something again I started a long time ago.

P.P.S The BI Job index is based on the number of jobs that match the search terms "Business Intelligence" or "Data Warehous" that are listed on www.seek.com.au. The index is expressed on points based on a ratio of the number of jobs to the number on the day the index started 23/10/2005. On that day there were 349 jobs - 100 index points.

Light at the end of the tunnel

Rob and I and co. bloggers have been more than a little quiet as - despite our best intentions - we got run over by our teaching work load in semester 1. Semester 1 is about to end so we'll get back to some BI blogging very soon.

Worth mentioning that we'll also be starting a BI podcast (not the same as the ones we have for our units). This will feature interviews with BI "thinkers" from Melbourne and around the world, as well as the occasional talk from members of the Centre. We will start with a couple of talks from me - the first will be a "briefing" on the decision support industry I gave to Prof. Arnott's FIT5094 class a fortnight ago, and my closing keynote at the Mastering Business Objects conference in Sydney last week. We hope to have a new posting in the podcast stream every two weeks. If you have any ideas about people you'd like to interview, or topics you'd like us to cover, please let us know.

POD

Cranky Geek - John Dvorak - takes huge swipe at spreadsheets, BI and accountants.

Lots of other things I want to and should post at the moment, but I couldn't let this slip. An article by John Dvorak that is kicking up quite a storm. Love John Dvorak. Always worth reading and listening. He's often wrong (and very wrong), but always there is something to what he says. Anyway, he is an article about the 30th anniversary of the spreadsheet.

He's nuts of course (in a good way, and that's what I like about him), but he makes a point. We have all this wonderful what-if analysis, information at our figure-tips, enterprise Bi all over the place - so how come we aren't making better decisions? Of course, the spreadsheet isn't to blame but its a fun read.

The 30th Anniversary of the (No Good) Spreadsheet App

POD

Scoble on BI Panorama/Google style

Thought it was worth drawing your attention to a recent video post by Robert Scoble.

The story of 2009? Enterprise disruption?

It's not particularly critical - really just a PR puff piece - it covers the tool's Panorama have been creating in partnership with Google. Panorama are a company to watch, Novaview - their core offering - is excellent, and they are the company that sold Microsoft the technology that became Analysis Services.

However, I'm underwhelmed by the tools they have created with Google so far, but its a start I guess, into a potentially interesting shift of BI services in the 'cloud'.

Have a watch, I'd be interested in your thoughts. I'll post a little later on why I think this in general is a big deal, but this particular tool isn't.

POD

Simple Designs are Hard

Reading Peter's last couple of posts got me thinking about a great TED conference presentation made by former Broadway pianist and NY Times tech columnist David Pogue. A couple of years ago he talked at TED about the design of technology and the importance of simplicity in design.

A lot of what we do as BI developers is design ways for people to mess around with, and learn from, information. One one of the key principles behind a doing this well is to ensure simplicity. A lot of what Tufte and other data visualisation experts talk about can be seen to derive from this principle, and as Peter said, despite the experimental and anecdotal evidence to support it, it's something the vendors often don't do well. One of the reasons for this is that it's just plain hard, and probably something that most software engineers are not very good at doing. Simple, elegant and intuitive interfaces for BI apps are not just aesthetically pleasing, they lead to better understanding on the part of decision makers, and creative uses of the tools that can lead to unexpected insights - which sounds awfully like the vendors' own jargon. I wish they'd listen to people like David Pogue a bit more.

An unexpected finding ...

It's a good time of year to be an academic, nearly all the marking and teaching related administration is done for the year, though next year is approaching fast - we do have some time to fully devote our attention to research. We have a lot of projects that are finishing up, which means it time for us to get out and start collecting data for the next round of case studies and investigations. We are also doing some tiding up of our infrastructure. We have had to move out of a room we had devoted to project related activities but that has given us an chance to throw some stuff out and generally get our "house" in order. For example we been have updating and sorting out our files hosted on various servers. None of that has any direct impact on this blog, except we have run out of excuses not to extend our blog related activities a little. Shortly, we'll have a podcast featuring presentations and interviews by and with staff from the Centre. Another thing we will start to do is talk a bit more here on the blog about our published research. So lets start that right now ...

Here is a link to a paper we published earlier this year.

A note on an experimental study of DSS and forecasting exponential growth

(The file is hosted on Science Direct and they own the copyright, so sorry if you can't access it. If you are on the Monash network, you'll be able to view it, or if you have a Monash authcate, try using the VPN. If you are at another Uni. you'll probably have a subscription)

I know that sounds a bit technical, and that its not of interest if you aren't into forecasting or that worried about exponential growth, but actually, its interesting beyond those areas. The paper presents and experiment we conducted where we asked subjects to forecast growth in iPod sales - which have been exponential. We conducted a similar study years ago, but used made up data, we thought it would be better to use a real exponential data series, so we re-did the study this time using iPod sales as the data series to forecast. Now, it turns out humans are poor at forecasting exponential growth - there is a cognitive bias at work related to the anchoring and adjustment heuristic - which means we just don't pick up on the exponential nature of a data series and forecast growth as a straight line and as a result under estimate growth of exponential data.

In our experiment, we gave the subjects some historical quarterly data, and asked them to forecast 2 quarters out (we knew that "actuals" for the periods we were asking them to forecast).

The idea we designed the experiment to test is a simple one. If you take a log of exponential data, you get a straight line. Humans are good at doing straight line forecasting so we reasoned that if you take a log of exponential data, forecast based on that, you'll get a better forecast than if you just have the data in its 'raw' state. The conversion to log data and back is something a computer system - a DSS - can do nicely, so that's the basic shape of the experiment. All the detail is in the paper - as you'd expect there is a control group using a paper based version of the data, but we built a nice little tool to perform the forecast. You can click on a chart to make a forecast - and it shows you the number, or type in a number and it shows where that number is on the chart. One version of the tool had just the raw data, the other showed both the raw data and the log data.

So, to the results ... the computer supported forecasts were better. Phew, often in these types of studies the DSS is of no help. In our case it was. However, the simpler version of the system, did better than the version that had the log data - the opposite of what we expected. Our explanation is that the simple version encouraged experimentation, letting the users think a bit more about their forecast - exactly what you want a DSS to to. However, rather than helping, the more complex system with the log data, intimidated the users, stopping them from experimenting and as a result they made poorer forecasts.

So forget about forecasting and exponential growth, the main lesson from this study is keep the interface simple.

POD