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Data is about your business, not just technology and algorithms

Author: Gaëtan Giraud
mei 2020

Nowadays data is everywhere. It is the hot topic of the moment, for businesses and in the general public. There are heated debates in politics about “Data”, data wars, and even a new science entirely dedicated to data!

When it comes to data, you might be wondering, should you build an enterprise architecture with data as your starting point (data-centric)? Or build a data structure around the existing landscape (data-driven)? What about data lakes? Do we actually have any data?

Well, tackling data is highly dependent on the specific configuration of your organization history, business, and IT landscape. 

At Cuurios we believe that Data should be CENTRAL to your organization. Gathering, managing, and acting on your data is your core business. But there are no one-size-fit-all solutions. Data is a mindset, the most important is to just do it, however small the first steps might be.

In this blog post I am giving some insights on how we see Data at Cuurios and sketching the first steps towards data proficiency!

DATA

It sometimes feels like data is something new, something very hot, the core of the 21st century technological battlefield. But data has been there for a long time! Measuring, gathering and analyzing data is at the core of the scientific and industrial revolution. Without Galileo gathering data on the moons of Jupiter with his telescope, there would have been no proof that Copernicus was right, and we might still be thinking that the earth is at the center of the universe.

Data is not a thing, a disincarnated entity that exists for and of itself. Data is grounded in reality, it is information that represents assets, people, events in the real world. Data is what makes large-scale organizations possible. Without data, how would you know what the state of you inventory is without having to recount every time? What the state of a critical asset is without having to look at it?

The first form of writing ever discovered, the Sumerian tablets [1], were accounting records of production and exchanges of goods, i.e. data.

What has changed to make data the focus of a new gold rush? 

  • Storage capacity has increased exponentially. When a Sumerian scribe needed hours to imprint a clay table, we can now record terabytes of data for very little cost.
  • The internet (as in the complete networking infrastructure). No need to have people do the measurements and record the data themselves. Everything is be automated.
  • Advanced in computer power has made the application of advanced AI algorithms cheap and rewarding.

This list describes techniques to store, manipulate, and analyze data.

What it does not describe, is a change in the nature of data. People tend to concentrate on the new hype, assimilate data with data science, and equate analysis with machine learning. This is a very narrow view of what data is and limits its usefulness to a few very advanced use cases.

Because first and foremost, data is information, information about your business, its customers, its assets, its financial state. It represents the tangible and is often the only thing you have to steer large complex organizations.

We think that data should play a core role in every organization, be CENTRAL to decision making and action taking. Without data, any decision taken is an educated guess. 

THERE IS MORE DATA AROUND THAN YOU THINK

In practice, we often encounter organizations that claim not to have any data. Because they don’t have a data lake.

The first thing they have on their data roadmap then, is to create one. But really, a data lake is just a big database. It won’t tell you what to do with your data. In our experience, many organizations, after having spent an incredible amount of time and budget on creating a data lake, are stuck. They don’t know what to do with it.

Because your data is about your business, not technology, not algorithms.

What we usually see is that organizations already have data, very often plenty of data, scattered around, in custom made applications, asset databases, excel sheets. Because you can’t function without data.

What they lack is an approach, a concrete process to manage data, to embed it in its day to day operations. The data processing, the algorithms, should come in support of operations.

Making sure data is part of your operations day to day business, that is being data-centric.

In order to do that, you need to reverse the data analysis process, look at your data from a business perspective:

  • What are my most important use cases, processes, assets?
  • Which data do I have about them? Where can it be found, in which format? Do I need more of it?
  • How can we automate this specific use case? Which algorithm can be used? 

At Cuurios, we have extensive experience working in the industrial sector. In most Industrial settings, processes will already be described. They will be backed by data for real-time monitoring, stored in a historian. Optimization and analysis algorithms are known.

The actual running of the algorithms, the analysis of the data and the generation of advices is traditionally a step performed by engineers, in Excel, Matlab or others. Tools great for exploration and scientific inquiry, but not made for automation.

For most companies, there is tremendous value to be added by connecting data sources to each other and automating their analysis. Actions can be defined and set-out quicker, with a better response to issues and a higher efficiency.


[1] https://www.cam.ac.uk/research/news/a-stray-sumerian-tablet-unravelling-the-story-behind-cambridge-university-librarys-oldest-written


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Both professionally and privately I like complex puzzles, varying from large data sets, challenging customer use cases all the way to large LEGO Technic sets. I am confident that this mindset helps me to keep pushing boundaries in search for that missing piece!


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Cuurios is my very first workplace as a full stack developer, but I believe I could not have been any luckier. I am not only a „coder” who translates an idea into code – my ideas are appreciated, and I can work in my own rhythm, try and solve problems alone, but always get support if needed. This is an environment where I believe an entry level programmer can really thrive, learn, and grow as fast as possible, facing challenges, a huge variety of tasks, being responsible but still getting all the support necessary.

Feels like it was yesterday when I created my very first hangman in python as a student. Back then I did not even dream of creating enterprise applications in just a couple of months after school, but with all the tools and support provided at Cuurios, I never felt uncomfortable facing that challenge.

Being a full stack developer helped me grow as a person as well. Learning how to make mistakes a thousand times and keep on trying until you get it right, knowing when you reached your limit and being able to ask for help without feeling uncomfortable about it – it is all part of a developers life as much as life itself.

Big Data or Big Mess?

Big Data, machine learning, AI – the hype words that will pull you into the magic circle of modern technology. Everyone wants it, everyone wants the possibilities, the growth, the kickstart that can be given to you by a good amount of data and a smartly designed software built around it.

But then why 87% of the data analytics, big data and AI projects fails? Aside from organizational and cooperation-related problems (more info here), there is more behind this amazingly high failure rate.

Is your data good data?

You could ask is good data and bad data actually a thing? Well maybe not in itself – but data without context is worthless (read more in our earlier blogpost here). Building the wrong relations, the wrong connections, having the wrong approach and conclusions can make your data bad data.

Like when in The Big Bang Theory Leonard, Howard and Raj found some notebooks of a late physicist, Professor Abbot, filled with hundreds of pages of seemingly random numbers without any notes or explanation. Seems like something important, maybe it is his life’s work, maybe it contains something important, exciting isn’t it? Realizing that it is in fact his daily calorie intake diary such a bummer...

You can’t know if your data is valuable if you don’t have the context. You have to know what this data is about, otherwise you might as well end up building a model to predict some long dead professor’s calory intake….

Seemed like a good idea...

There are a lot of projects that fail because they seemed like a good idea, but in reality were completely worthless.

A company that had a relatively high employee turnover decided to up their „HR game”. They started researching the signs of people resigning, trying to predict which employee will leave the company soon. They tracked measurable data, like the number of years they worked for the company, their commuting distance, their salary, overtime, sick leaves and some hardly quantifiable data like their engagement to the company. They were looking for detectable connections, that will help them make their employees next move predictable.

While in some cases, collecting data and finding connections simply turns out to be not viable at all in production, in our case, that was not the problem. The issue was the wrong understanding of what data is and what data science and statistics can do for you.

The company actually was planning to use the knowledge they earned: they were planning on using it in their evaluation and hiring process. The idea was to evaluate new candidates on their likelihood of leaving, and only hire those applicants whose likelihood of leaving the company in the first 3 year is below a certain rate.

...but it simply does not work that way!

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Is it doable? From a programming point of view, with quantifiable variables, it is no magic to create a program like that. But will it work? The answer is a straight and obvious no.

Besides the fact that collecting data like this is questionable at best, with regards to the privacy of employees, it does not make sense from a business perspective. If we really give some deep thoughts of why anyone would want to quit their job, the factors are multiple, and are neither predictable, nor measurable for start. How someone will respond to workplace dynamics, or personal issues they might encounter, are not quantifiable and cannot be predicted even with the utmost caution.

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The idea completely ignores the single most important thing of why some people stay at a workplace even when underpaid or working extreme hours, and why others with a generous salary or personalized benefits will still leave. And that is the human factor (read more here).

Causes or consequences – did you make the right conclusions?

But let’s assume, that you are able to quantify and take into consideration every human factor – that is probably a mission impossible, but sticking to the above example let’s just assume it for a second, and try and dig deeper of why the idea is inherently wrong.

The software would calculate a vast number of variables, that are not decidedly causes, but most likely consequences of an employee leaving the company. Having less overtime, going on a sick leave, besides of other factors can actually be consequences of an already made decision: our employee already decided to leave, and preparing for the change, is looking for another job and so on.

The algorithm does not predict, it just tells us what is happening. We are too late to do anything about this employee leaving. That is how wrong connections, or even only seemingly existing relations can make your data bad data – and lead to wrong conclusions and wrong business strategy.

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YES!Delft: Investor readiness program – A partial view

After a first month as part of the YES!Delft investor readiness program, it is time to share some thoughts on the process, what we’ve learned so far and how it impacted us.

DISCLAIMER: This light-hearted description is only very loosely based on reality and should definitely not be taken at face value :-)

Like most, we started this journey as enthusiastic entrepreneurs, completely sold on our ideas, focused on how great it is and all the cool stuff we have in petto, the myriad of functionality it will provide and how it will revolutionize the market. We were pretty confident, sure of our game.

Then we had 1 minute to pitch.

Big Fail

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Investors are from Mars

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The first hurdle passed, it was not the end of it. We had some numbers, great, but what was it again that we sold? And, most importantly, sell to whom?

  • Ok, you’ve got 30 seconds.

Hmm, yes, you know, we make software that does stuff with data, and it’s super cool.

At this point we knew we had to do something about that elevator pitch. 

Elevator music pitch

Here comes the WOW method in place. A simple method to get your ideas straight and write an elevator pitch with a true WOW factor (yes, it is in the name).

Suddenly, it all made sense. This is the story we want to tell, these are our customers, and this is what their pain is.

Elevator pitch much improved, still some way to go, but at least most people now understand what we are trying to achieve.

No free lunch

Now that we’ve got a good enough elevator pitch, and some numbers, time for what we’ve all come for: Show me the money!

Again, a good learning experience: there is nothing like a free lunch in this world. Investors are not charities, they’re in for the big bucks! And how to convince them that you are the next Google when you are a fledging start-up?

  • Get some customers on board, build traction, show you’ve got it!

But wait a sec, to build any sort of real traction we first need to finish up our product! And wasn’t raising money supposed to help get us there? It seems a bit like a chicken and egg story, isn’t it?

Welcome into the real-world people!

There are some options fortunately, government grants and loans, to help you get started and get you through this first stage in your development, without of course forgetting the most common investment of all, some good old-fashioned hard work!

Conclusion

So far, a very valuable program, it has helped us to sharpen our message, build up our case, and get us ready for the next stage!

From theory to reality: internship at Cuurios – Meet Bram

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