Wednesday, 29 April 2009
Tuesday, 28 April 2009
I, for one, would like to see more data.
Let’s not forget that nuclear power is also a threat to the planet. Whilst melting ice is not a popular move with that most potent symbol of our planet – the polar bear - neither is waste that stays radioactive for hundreds of thousands of years. The choices may not be palatable, but surely all the more reason to have good data, presented clearly.
Renewable energy, he said should not be dismissed: wind is producing enough power for 2 million homes. 2,000,000 is indeed a large number – an impressive number even. But it is a meaningless number in the debate about energy and climate change. How many homes and businesses need power? And at what cost?
Milliband rightly pointed to the future with ideas such as “clean coal” which as soon as the technology is ready the government will ensure will be 100% committed to. Whilst he was quick to quantify the potential jobs that will be created through this R&D, he was less keen to share the costs even though they are equally quantifiable.
Neither did he talk of other technology which might provide low-carbon alternatives a great deal more palatable for our polar bears than sitting on radioactive waste for a million years. Yet they do exist.
This whole issue is quantifiable: cost of power, amounts of subsidies, carbon emissions, temperature changes, sea levels, thickness of ice, number of species. I could go on – they are many and varied and all absolutely quantifiable. Yet for some reason we prefer pictures of polar bears to clear data which would help us make good decisions about what mix of power to rely on.
Monday, 27 April 2009
Wednesday, 22 April 2009
- It should be easier than this. It should, shouldn’t it? But it isn’t. Malcolm Gladwell in Outliers writes some fascinating things about why some people are successful and others are not.
- Other people don’t struggle like I do. Paul Simon said some people’s lives roll easy – and maybe he knew. My suspicion is that they had to work pretty hard too.
- We have enough technology. We are too curious to be ever satisfied with the technology we have. There will always be something faster, smaller and whizzier. We won’t necessarily need it, but we will buy it anyway - particularly if it comes with a cool case.
- Children grow up faster than they did in my day. Maybe, but children are still children – even though they sound scarily sophisticated. Love, nurturing and listening are needed as much today as ever.
- You can build loyalty. Maybe you can buy it for a short term – but the battle starts over again every day. Business intelligence and performance management don’t top the IT Director’s shopping list for fun.
- We can save enough energy to make a difference. After getting a new electricity meter counter gadget it has now become a hanging offence to turn on the spotlights in the hallway in my house. Using the last of the loo paper is a minor offence by comparison. Everyone is going green and saving energy – and quite right too. But it still won’t be enough to let all the world’s children grow up at the speed they are determined to.
Monday, 20 April 2009
I did see some neat status codes over the weekend at a very swish place called Heathrow Terminal 5. It was my first visit to this lovely new terminal which has been in the news for all the wrong reasons.
I was impressed: imposing architecture, systems that appeared to work well, and lots of showers for little people. Actually I think they were fountains but I suspect they will be very popular as showers as the weather gets hotter. It’s the first thing you want to do when you get off a plane, isn’t it?
Back to status codes - vital at the airport. I was in arrivals – the time shown on the board could be: estimated or actual. An additional status code showed when bags were being unloaded – now that's useful.
What really impressed me about these status codes however, was the board next to it. It had helpful information like the amount of time you should expect to wait for someone once the aircraft has landed. How long it takes for someone with or without a bag to get to the arrivals hall – now that’s not just useful, it’s actually helpful. It’s not rocket science, but it is thoughtful and genuinely helpful.
Of course it was a sunny Sunday and the incoming flight was on time. The arrivals hall wasn’t too crowded and I suspect the systems weren’t being stressed too much.
It is when it all goes wrong that people and systems come together to make decisions and ensure boards such as these give genuinely helpful information, and T5 has some making up to do in that department.
However, for the 95% of the time that things go well it’s an improvement on what I’ve seen before. And an interesting way to think about status codes – maybe they are a little too pithy sometimes?
Friday, 17 April 2009
But a visit to the Royal Opera House to see Tamara Rojo dancing Giselle put pay to any discussion about Dyrham Park. You can’t compare soup to arabesque, so I shan't even try.
Little alters my view that the Royal Opera House is a fascinating case study in excellence. I just need a few more visits to be sure ….
Thursday, 16 April 2009
Data mining is the applications of statistical techniques and artificial intelligence to find patterns in data that are not apparent using queries or other database techniques. Data patterns can provide insights into behaviours and trends that would otherwise remain hidden. Data mining is perhaps more descriptively known as knowledge discovery in data.
The statistical techniques are run as software programmes which allow parameters to define how the algorithms are applied. The pattern-finding process can be run on different data sets and with different parameter settings. Models can be refined to improve the accuracy of the results.
Although data mining algorithms can be run on any data file, they are often applied to files where data has been brought together from a number of different sources. Different statistical techniques, or algorithms, are suited to different types of data, and different problems.
The basic premise of data mining is that predictions can be made about the future from a sample of past behaviour, ie the existing data files. For example, theatre bookings together with other information about those who made the bookings can be used to find patterns, and predict what type of productions they might book in the future. Segments can be found and different marketing messages sent to them according to their profile.
Data mining is the automatic or semi automatic means of finding patterns and making predictions.
Data mining has now been built into Microsoft’s SQL Server database: starting with two algorithms in SQL Server 2000, extended to 7 algorithms in SQL Server 2005, and with some further enhancements in SQL Server 2008.
Get in touch if you would like to find out whether your data files are suitable for data mining.
Tuesday, 14 April 2009
One of my favourite software writers observes that the quickest way to deliver a project is not to make mistakes. It is good advice (although not that easy to follow), and risk management is part of project and performance management. As many organisations have too graphically illustrated, there is little point in aiming for peak performance if you destroy the very organisation you are trying to manage.
A friend recently sent me Dr Aswath Damodaran’s Six Rules of Risk Management, which I liked a lot. At the considerable risk of getting a reputation for loving lists, here it is:
- Where there is an upside there is also a downside
- There are no free lunches
- There is no risk in the past - study the past but remember risk is in the future
- Risk management is everyone’s problem
- Plan to be a risk taker - hire the right people
- Do everything you want to manage risk, but at the end of the day you also need luck.
An interesting list – I would quibble slightly with 2 and 6 – but no more than quibble. A lunch, at the end of the day is a lunch; losing Barings Bank (or whatever) is a little more serious. And luck is a highly debatable entity, don’t you think?
Thursday, 9 April 2009
Business intelligence means different things to different people. But underlying any business intelligence project two things are normally found:
- Desire to improve performance
- Quantitative analysis
Celsentri is a new drug that can be taken orally to slow down the HIV virus in some patients. Trials have shown the drug to be both effective and well tolerated within certain groups. However, a test was needed to identify those who would benefit. In an increasingly commercialised medical world, the cost of the test was seen as a barrier. However – despite the test being made available free of charge, there was still resistance to prescribing the drug. Why? It didn’t make sense – it was better for the patient, and the barrier had been removed from the doctors.
The business intelligence team already realised there was a hidden barrier to prescribing the new drug. Research was commissioned which used a three-pronged approach:
- The research focused on before, during, and after the decision to switch treatment.
- In-home interviews were conducted to allow in-depth and open discussions. Patients were encouraged to talk about problems with the decision to change drugs, and how they were advised.
- Doctors were asked to map what they did, and when, onto a timeline. This removed ambiguity in discussions and allowed patterns to be seen in their decision making.
Pfizer called their approach the Pfizer 6-D’s:
- Define the question
- Dig to address the question
- Discover customer perspectives
- Distill responses to build insights
- Develop marketing programs based on customer insights
- Deliver based on evidence
His approach is instructive - not only within the healthcare market, but for anyone involved with customers. Whilst Business Intelligence is a quantitative discipline, people are still people, and remembering the emotional angle is important.
Wednesday, 8 April 2009
Of course, he does cherry-pick his examples: the Beatles, Bill Gates, and Steve Jobs are all lives that have been well documented. But equally he challenges some deeply held treasured myths: that it’s not just intrinsic talent, but also hard work that sorts the millionaires from the benefit recipients.
His description of KIPP made me late for work this morning. “Work hard. Be nice.” it says on their web site – and hard work is exactly what is turning around the lives of disadvantaged children. Through their own grit and determination, led by an inspiring and visionary program, over 16,000 US students now have a chance of higher education and a better life. The secret? Start school earlier, finish later, do more homework and take less holidays. Genius!
Gladwell also points to Ericsson’ research on Expertise and Expert performance – another great interest of mine. It turns out our great violinists, pianists, chess players and others are in fact those who have practiced the most. They also happen to be very talented, but they have to put the hours in like everyone else.
I have a perverse interest in statistics, and expertise, despite its apparent dryness. This book brings both subjects to life in a way that I can only envy. Data visualization using words, beautifully done.
Tuesday, 7 April 2009
Monday, 6 April 2009
- Thomas John Watson, Sr.
Thomas Watson was the son of a lumber merchant who worked first for NCR and then the Computing Tabulating Recording Corporation (CTR) which he soon renamed to International Business Machines. Under his leadership the company went from 400 employees to a world power-house of computing.
This quotation reminds me of Leonardo da Vinci's thoughts:
Thursday, 2 April 2009
Reserves which may contain gold for their owners.
Before you put on your hard hat with the lamp on the front to break open the server, I’m talking metaphorical gold - metaphorical gold which could be worth a great deal more to your business than the real thing.
First – let’s consider the reserves, which unless you are actually a mining company will be the data stored in databases within your company. Then, let’s look at what the gold might be that’s hidden in the data.
There are few businesses which have not installed a database, whether for managing customers, accounts or stock. As more enterprise-wide systems are installed the amount of data being generated is phenomenal. Some of that data will be immediately accessible through reporting tools. But what could happen if those databases were joined together? What if you could see the sales information together with the customer management information? Or the training data together with sales data? At the risk of mixing enough metaphors to make soup, that would really be cooking with gas ….
But whether it’s one database, or a number joined together, how do you go about looking for gold? Indeed what does gold look like in data terms?
How you find gold is by using a technique called data mining, and what it looks like all depends on your business. It may be customers who are more likely to book a particular type of show in your theatre, or finding which products to bundle together to maximise sales and profit. Or it could be something completely different – depending on what business you are in. The applications for data mining are many and varied and are limited only by business owners' imagination and ambitions.
Data mining is now more accessible and affordable than ever. Products such as Microsoft SQL Server 2008 and 2005 put data mining within the reach of most companies – large or small.
Get in touch if you want to dig for gold in your data. Hard hats with lamps supplied.
Wednesday, 1 April 2009
But I discovered a new bit on this latest visit – Shipping. I’d already seen Samuel Plimsoll’s statute in London a few months before and the
“So did he invent gym shoes as well?” was the inevitable joke. “Nah – that was his brother – they were a talented family…” Yup, the conversation was almost as inspiring as the exhibits.
Of course it wasn’t his brother, but the gym shoes were named after him in a way. When these new canvass shoes became all the rage they had a thin rubber band running around the shoe to stop the top and bottom sections coming apart. It looked like the Plimsoll line that had just been introduced in 1867. So the shoes became plimsolls and Mr Plimsoll was famous twice over.