Everything is Data... and What You Can Learn from It

We focus too much on fixing the symptoms instead of solving the problems themselves. According to Pete Clancy we can find a solution in the collection and analyzes of data.

30-04-2018 -  by Kevin Van der Straeten

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Transcript

We focus too much on fixing the symptoms instead of solving the problems themselves. According to Pete Clancy we can find a solution in the collection and analyzes of data.

 

Hi Pete.

 

Hi Kevin.

 

Welcome to the studio.

 

Thank you.

 

In our preparation talk you mentioned that we focus too much on fixing symptoms instead of fixing the problems themselves.

 

Well, the main problem that people have is thatwhen they suffer from anything, any pain, anywhere, they do tend to focus on what the symptoms of the pain are. So what they do is they fix the symptoms without actually fixing the problem. What you need to do, is you need to go and you need to look for all the symptoms you can find, and trying to find the source of those symptoms. Using that information, then you try to work out what the problem is and then you try to fix the problem. Because at the end of the day, fixing the problem is going to solve it.

 

And therefore you need data.

 

And therefore you need data. And when we talk about data, of course we’re not talking just about something in the database or something like that. You need to actually go and look at your Excel spreadsheet, price offers, invoices. Information that you might have stuck on stickies,whatever it might be.

 

Do you have a real world example, you can share with us?

 

So one of the companies I worked with is an interior design company. And they were a bit worried in the beginning working with me, because they did not have a database of data. So after a discussion with them, I realized that what they do haveis a list of all their price offers, they also had all the price offers in Excel spreadsheet format, always consistent. And then they had their invoices, also in Excel spreadsheet format, and also in a consistent format. So I was able to take an awful lot of data from there. Then we were able to look at work hours. Take an awful lot of data from systems, where they had the number of hours people have worked, the areas in which they had worked. And you can make the links between the work hours and the projects. You can make all kinds of links with other information: transport data, distances between customers and the factory. And putting all that together, you can then use that information to find efficiencies, and find where they both winning money on projects, and losing money on projects. So the idea was they were losing quite a significant amount of money on projects. In which case they knew they were losing the money but they didn’t know why, they didn’t know where, and they didn’t know on what types of projects. So through looking at the data, looking at the various different interactions of the different data sets with each other, and also trying to work out where exactly the inefficiencies were. I was able to identify particular types of projects that were profitable, and other projects that were not so profitable. In doing that, it allowed the business then to make decisions based on the profitable projects, and say, okay, we wanted to do more profitable projects and fewer unprofitable projects.

 

You told me to story before, I remember there was a particular part that was an indicator for…

 

Yes. In different types of furniture you have of course the wood side of things, you have the square meters of wood, most people will of think of that. So one of the interesting things we found in this project was that there is a link between, the number of hinges that are used in a particular piece of furniture. In comparison with other pieces of information we were able to make a prediction of the profitability of the project. That doesn’t mean, you can turn around and say, so many hinges means so much profit, that is not the case. But you can turn around and make a prediction in an order of magnitude that says, this project is likely to be, profitable on the basis of a number of parameters compared with each other.

 

And that’s because it’s an indicator for the complexity of the project.

 

It’s an indicator indeed for the complexity of the project.

 

Can you use this kind of approach in the events industry?

 

You can use it in the events industry, you can use it in any industry. But in the events industry you would just have different sets of parameters. You would have different numbers to different pieces of data. And then basically do the math.

 

But will that also mean that you need an expert like you who can work with the numbers, see the insights?

 

Correct. One of the important things is to help the customer on the other side also to identify the types of data that they have. Because in most cases the customers, my clients, don’t actually realize what data they have.

 

Yeah, I wouldn’t even think about for example price offers as data.

 

Well, price offers are part of the most important pieces of data. Because they give a snapshot of what the company thought, the project would cost at the very beginning before they actually did any work. They say, building this piece of furniture is going to cost this much, organizing this type of concert is going to cost that much. And then on the basis of that they make their pricing. But later on they do the work and then they start finding out the different problems that they have. And then they start going into the details. But once they go into the details, once they work on the project, they find that maybe some areas cost a bit more, some areas cost a bit less. So at the end of the day they have a result of what they actually did. And what you do is, you compare the two. You bring the offer up, you bring the actual work information up, you compare them, and then you look for reasons why there is a difference. And this is where, as I mentioned previously, with regards to hinges, this is one of the areas that indicate there was a difference in complexity. Of course you also need to have a number of projects. You can’t do this with one, two or three projects. You need to have twenty, thirty, hundreds preferably of projects.

 

What’s the minimum?

 

You can start with nothing. And you can look for indirect information, from the industry or whatever.

But of course if you start with nothing, you have no information, then it’s going to be very different to make a real estimate for your company. Preferably you should have at least an idea of what a good project is, a bad project is, and a normal project is. At least you have those three, you can start doing something. But you can always start. You can always make a start, you should always make start. Because at the end of the day is the trends. You have to trend positive.

 

Yeah, and the more data you have, the more correlations you can make.

 

Yeah.

 

Okay Pete, thank you very much for coming over.

 

Thank you.

 

And you at home, thank you for watching our show, I hope to see you next week.

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