Data is about your business, not just technology and algorithms
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!
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 , 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.
Delft, 29 June 2021 - The Rabobank Startup & Scale-Up team has provided Cuurios with substantial growth funding to further scale the company. Cuurios is a fast growing software technology provider transforming the industry and redefining leadership in a digitized world. The combination of an investment round closed earlier this year and the growth capital raised today, Cuurios is in the position to gear up for an exciting journey!
Our objective is to provide the tools that will digitalize and streamline industrial operations, foster collaboration and ultimately unleash an industry data revolution. For one of our clients the automated and data driven production advises in order to produce to the optimum and avoid asset failures contributed to a 3% production increase.
I didn't choose software engineering. It chose me. My first job was site content management using a html redactor. The manipulation of the content was pretty cool and exciting for me and had a permanent impact: I decided that I wanted to further develop myself as a software engineer. After a couple of years I became a full stack developer, and nowadays I mostly work with back-end technologies and DevOps engineering.
One of the most exciting aspects of programming for me is to see that the code you developed is used by actual people, affects, and improves their everyday lives in so many ways. You release a product and see people interact with it, use it, getting work done with it.
It is motivating to see something I built come to life. It gives me energy to create something that didn't exist before. It also gives me joy to implement interaction between structure and dependencies within a complex application. Playing with algorithms, frameworks, methodology is like fixing a puzzle or making a puzzle of my own.
The time management process helps me organize my work more efficiently, because being busy isn’t the same as being effective. Every morning or the evening before I make a plan on how to divide my time between activities. I sort activities by priority, complexity and time consumptions to make my day as productive as possible.
It is my priority to have a healthy work-life balance. Beside my main job I am an aerial hoop instructor and a competing athlete. This is a huge part of my life and brings me a lot of energy.
This year was difficult for all of us, I miss my group classes and students, but also it gave me the opportunity to give online and private classes. This summer I am going participate in the national IPSF competition in professional senior category. This is a big event in my life, I spend almost all my free time preparing for this competition and I hope everything will go smooth.
After one and a half years living in the Netherlands and feeling more confident as a professional as well as in my social life, I decided to change my job: that is when I applied to Cuurios. Cuurios got my attention as it is a fast-growing start-up and they need the right people to grow.
Working at a start-up allows me to try a lot of different opportunities and responsibilities, even that weird one that I didn’t think I would ever like but find out that I did. I came into Cuurios as a full stack developer, but now I feel comfortable in a lot of different areas such as DevOps engineering or as a back-end guild speaker, just to mention a few.
The Cuurios team when sees a problem, they think of an innovative and original way of solving it. They are the best people to learn from. They continue to challenge me when I present a solution. They have a different approach than I do, that gives me a broader understanding of the different ways to find solutions.
We started Cuurios because we are convinced that data and information in leading organizations will be used to optimize the processes. We successfully verified our vision running many successful projects at various customers in different industries. We enabled our enterprise customers to automatically translate their data into concrete actions. Replacing an entire chain of Excel sheets, e-mail messages and text messages. The resulting efficiency gain and positive performance impact surprises our clients again and again.
“The digital-tool developed by Cuurios allows us to further streamline our daily operational tasks. I very much appreciate the flexibility, fast response and easy going cooperation with Cuurios.”
The positive impact of these solutions made us realize we had to push through. The lessons learnt at our launching customers inspired us to design and develop the leading SaaS platform in the market of asset intensive enterprises. The sophisticated data to action solution accelerates customers’ performance by an order of magnitude. We enable you to learn from yesterday, know what's happening today and predict tomorrow.
So, how do we do that?
Engineers are enabled to develop their own algorithms and workflows and configure standard elements as they see fit. The analytics technology used to get insights from your data does not per se need to be complex (i.e. AI or learning algorithms), the trick is to select the right technology for your particular use case! We’ve seen that analytics initiatives resulting in meaningful actions, well embedded in the regular daily work process has a much bigger chance of success compared to yet another fancy dashboard. This has already allowed our customers to avoid multiple production stops, by making smarter use of existing assets and production data.
We know the feeling: trying to fit a square peg in a round hole – the asset structure and hierarchy of companies cannot be simplified and generalized without compromises. Instead of forcing engineers to somehow adjust their already existing domain representation to a prefixed system, our tool allows complete control over hierarchy, connections and structures, which allows users to eventually have the perfect representation of a domain with no compromises.
All that with a graphic display, to be able to overview and edit in a fast and convenient manner.
I grew up in a small village in the Dutch province of Zeeland. During my childhood I spent a lot of my free time outdoors playing with my friends, climbing trees, building tree houses, playing football and swimming in the sea. My father was (and is) a watchmaker and as a small boy I was always intrigued seeing him repair watches and clocks. Looking at all those small parts ultimately forming one single system that tells time. I guess that during those days my passion for technology started, although at that time I excelled taking clocks apart, not repairing them…
Later I got intrigued by the possibilities of computers as we got at home in the late 1980’s: a Commodore Amiga 500. Initially we used it to play games, but soon after that I tried coding new games myself.
At relatively young age I already knew I wanted to be an engineer and I ultimately decided to study Mechanical Engineering at the Delft University of Technology. During the Master phase I specialized in the field of human machine interaction. This is all about making sure you provide people with the right information at the right time to make sure they will be able to make the best possible decisions. This is the red line in my career so far: utilizing (software) technology to help people make timely and well-founded decisions.
Gaëtan and I started Cuurios in 2018 because we’ve noticed that many industrial big data analytics projects face difficulties to make it into daily reality. In our vision you need to add business value in the early stage of digitization and transform data into prioritized (and assigned) actions for it to be taken seriously. The underlying technology to transform the data does not need to be complex (i.e. AI or learning algorithms), the trick is to select the right mechanisms for your particular use case! Most importantly, make sure you well manage the actions resulting from these data analytics: your average dashboard does not do any of this!
In my current role I get a lot of energy bridging the gap between the customer’s daily reality, new technology and software development. I’m enthusiastic, curious and people oriented. We typically like to listen very carefully to our clients ideas and requirements and ask the right questions in order for us to be able to deliver software solutions with a WOW factor: always go one step further!
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!
My very first program was written in Turbo Pascal. It was a very simple little application, where you could give your birth date, and the app was telling you your astrological sign and all the main characteristics. That was over 20 years ago, when computer science classes for the students in my country meant learning how to turn on and off a computer and change wallpapers.
I was always intrigued by the possibilities of this technology, and my primary school teacher was really happy that someone showed interest, so we started learning the real things, hardware and software as well. After primary school, I never again had the same opportunities or means to keep on learning.
Fast forward almost two decades, in my late twenties, still trying to find my passion, becoming tired and burnt out from marketing and copywriting that I was doing for over a decade, and not finding joy in being in law school either – mainly because I saw my future as lawyer, and suffice to say that it wasn’t as „romantic” as I imagined it.
From customer relations officer to HR assistant, from journalist to head of marketing I tried myself in many 9 to 5 jobs, until one day I decided, I wanted to do something completely different. At age 27 I decided it was time for a real change – and I started learning again. In just 3 years I became a web designer, and after rediscovering my love for coding, kept on learning to become a full stack developer.
Today, for the very first time in my life, I can finally combine my need for creativity and challenge, I can connect the missing dots. Before coding, when I had an idea, I always thought: „how cool it would be”. But today, I know how to make it happen, I have control over what happens and how, and I can bring all those ideas to life.
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, 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-relatedproblems (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!
Now this sounds great, isn’t it? Just add in a couple of variables about your new candidates, and a software will spit out the possibility of them becoming a loyal, long-term employee.
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.
And beware of the self-fulfilling prophecy! A simple notification to a manager that a certain employee is most likely to leave the company in the near future based on these parameters will itself affect the workplace dynamics – and might bring upon what one wanted to prevent.
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.
Machine learning and its limits
Last but not least, even if you get every single part of your use case right, although we mentioned it in our previous blog post, but it can never be emphasized enough: machine learning can be a goal, but it is very rare, that it can be your first step of data analytics. The reason for that is simple: you need huge, and I mean really huge amount of data for your software to be able to actually find working relations, rule out the insignificant factors, be aware of unique cases and extremes and create predictions that will actually give you useful and trusted information.
Having a machine learn relations, connections, create predictions is not so much different than having economists and researchers conducting statistical analysis. You need an unbiased representative sample to get to a conclusion, and to avoid biased sample you need to know your population. In our example case even if we ignore any other issues, simply reviewing 500 employees actions are just not enough to be able to get clean data that can be used as a base of any sort of prediction.
For a kickstart, most companies don’t need machine learning right away: synchronizing, centralizing your data, and automating just some of your daily tasks can already be a huge step towards the right direction.
This is how sometimes even small but fast companies on the market are able to steal market share from well-known giants: they already know that there is no need to reinvent wheel to gain momentum.