Author Archives: Menno Koens

Why am I writing this?
I am writing this post, because I think this topic is interesting for people that are reading blog posts at intopreneur.com. It is especially interesting for people that like data, programming and modelling in relation to policy/strategy making. What I will describe is also interesting for start-ups that want to specialize in this area, because it is a real niche.

Some background
I am a Master of Science student at the Delft University of Technology, faculty Technology, Policy & Management. My master is called Engineering and Policy Analysis and focuses on Modelling & Simulation and Policy & Politics. What I will describe in this blog post are things that I have learned during the first year of my master.

A summary of the main idea
The building blocks are Data, Programming, Modelling & Simulation and Results presentation. You can see the building blocks in figure 1.
You start with a large amount of data, by means of programming you clean and prepare the data. The prepared data will be used as input for your mathematical model. You simulate your model and you get results. Now it is important that you present your results nicely, so that decision makers will take over your recommendations. With this process you can turn data into information and influence decision making.

A comprehensive explanation of the main idea
If you are still interested, then please keep on reading. Because in this part I will go more in depth about this topic. The total process is described in figure 2. I will walk you through the figure, so that you understand what is happening. The whole process, which I described above comes down to what is shown in figure 2. This is called “Data Driven Hybrid Modelling & Simulation”.

• System data –> In our world we are generating more and more data. This can be real time data or static data. The problem is that most of this data is not turned into information. With the process I describe here, the data becomes useful.
• Programming 1 –> The generated data is most likely messy and cannot be used directly for modelling. The data first need to be analysed, cleaned and prepared. This can be done by means of programming. In essence you create a new data set, which is called usable data. The analyst then can decide which of the usable data will be used as input for the modelling and simulation part.
• (Exploratory) Modelling & Simulation –> The usable data is prepared and can be used for modelling. However, what do I mean with modelling? With modelling, you recreate a part of a system. For example, the European freight infrastructure or the spread of infectious diseases. With simulation you simulate your model and you try to detect what might happen over time. In essence you can explore the future. The three main modelling branches are: System Dynamics (aggregated modelling), Discrete Event Simulation (dis-aggregated modelling) and Agent Based Modelling (dis-aggregated modelling). Until today these branches are operating individually. However, there is a need for Hybrid Modelling, in essence combining all these techniques. This is something that will emerge in the coming years.
• Programming 2 –> Now you have your results from the models you build. The output data need to be organised and stored in a database.
• Model data –> You went from system data to model data, or actually real information that can be used for decision making.
• Reporting –> This is maybe the most important part, because if you cannot report and present your results nicely to decision makers, they will not be convinced about your recommendations.

In the end your recommendations as policy/strategy analyst should enrich and improve the decision making process. Nowadays, managers and policy makers base a lot of decisions on qualitative reasoning, which is good up until a certain point. By adding the quantitative component, which I described in this blog post, it is likely to assume that decision making becomes better and this leads to sustainable strategies and policies.

Why have I told you this?
The process as described in figure 2 is not yet reality. There is still a lot of work required to make this happen. However, there is a strong need in the business field for informed decision making, based on quantitative methods. I think talented people, with good analytical skills can set-up a profitable start-up in this business area. If you are interested, you can contact me and I can bring you in contact with the department of Technology, Policy and Management from the Delft University of Technology.

Thank you for reading!

Regards,
Menno Koens

Monday the 25th of September Josefien and I attended the start-up event: GDPR & Software Quality. So what is GDPR? And what is the link with start-ups?

GDPR stands for General Data Protection Regulation and is designed by the European Commission in 2016. GDPR aims to harmonize data privacy laws across Europe, protect and empower all EU citizens data privacy and it reshapes the way about how companies use and approach personal data. The GDPR replaces the old Data Protection Directive out of 1995. The GDPR enters into force in May 2018. By then all companies, which process or use data from European citizens for their business should comply to this rule.
So it is about the protection of personal data, what actually is personal data? To name some examples: photos, email addresses, bank details, computer IP addresses, etc… All in all, data that can directly or indirectly identify a person.

Nowadays we live in a world where data becomes more and more important. More and more companies rely on the use of data for their businesses. It is easy to understand that the GDPR rule has a dramatic impact on the business world today. All companies need to adapt to make sure that they comply with the GDPR rule. For example, personal data such as a phone number, cannot be transferred from one company to another without informing the owner. Have you ever wondered how recruiting bureau’s get to know your phone number?

So, this brings me to start-ups. A lot of start-ups, especially technical start-ups which use big data for their business, need to know what is allowed within the GDPR rule. On the one hand, when start-ups know what they can do within this new piece of legislation it can give them a competitive advantage among others. On the other hand, due to this GDPR rule new niche businesses emerge. Start-ups can use this niche to specialize in data protection and provide advice to clients.

I learned that the use of data under the GDPR rule will change. Remarkably their is not much media attention for this topic, but I expect that this will increase as soon as the deadline of May 2018 is coming closer. The majority of companies is simply not ready for the GDPR rule. So start-ups that specialize in data protection should use this opportunity to provide consultancy to boost their business.

Thank for reading,

Menno

Hello everyone!

This morning we (Josephine, Rosita, Rasam, Shiva, Mussa, Vishal, Menno) sold coffee at the KTH campus at the V-building. We first thought that this would not be a great success. However, it turned out to be a real niche in the market, because there is no coffee store nearby in this building! Some people even told us that we were heroes! 😉

Our goal/strategy was to make as much profit as possible. We tried to do this by keeping our overall costs low and selling coffee at a location where we are the only supplier. This worked out perfectly.
We decided to sell instant coffee (pretty cheap) together with a cookie. Additionally we bought milk and cups. In the end the variable costs were 125,- SEK. Our fixed costs were 0,- SEK, because we did not made any capital investment and there were no labor costs. So we managed to keep our overall costs low.
Since the costs were so low, we were able to sell the coffee against a competitive price of 10,- SEK per coffee. Next to the coffee people got a free cookie.
We promoted our “coffee company” by contacting the people directly and by hanging promotion signs around the building.

Lets have a look at some micro economic stuff:
– Total costs: 125,- SEK
– Total revenue: 365,- SEK
– Total profit: 240,- SEK
– Total output: 36 coffees sold
– Average costs: 125/36 = 3.47 SEK
– Price: 10,- SEK
– Profit mark-up per coffee: 10-3.47 = 6,53 SEK

So, if you want to make money on a Monday morning when everybody is desperately looking for coffee, go to the V-building and sell it.

Thanks for reading!

Menno Koens