To master digital transformation in your business and put data-driven business models into practice, a digital mindset and comprehensive empowerment originating with corporate management is required.
By Trond Bjerkvold.
Four gold medals and one silver medal during the 2018 Winter Olympics are proof that Jac Orie is a successful speed skating coach. Why? It all has to do with data!
In the ice skating world, the name of Jac Orie is well established. He is the man behind the biggest successes of many Dutch speed skaters. Gerard van Velde in 2002, Marianne Timmer in 2006, Marc Tuitert in 2010 and Stefan Groothuis in 2014: they all won Olympic gold working with Orie. Apart from a mountain of medals, these skaters have left something valuable: a huge amount of data. Advanced analytics on almost two decades worth of data has helped Orie to train his team even more smartly in the run-up to the 2018 Winter Olympic Games in Pyeongchang, South Korea.
The results of Orie’s big data project have been astounding so far. Millions of viewers all over the world saw Sven Kramer (men’s 5,000 metres), Carlijn Achtereekte (women’s 3,000 metres) and Kjeld Nuis (men’s 1,000 and 1,5000 metres) skating to gold. And Patrick Roest (men’s 5,000 metres) won silver. Less visible is what exactly lies behind these successes. For many years, Orie has been using test data generated by skaters to calculate speed and stamina. For Pyeongchang however, he went one step further and collaborated with Leiden-based data scientist Arno Knobbe.
The big data approach, whereby computing power is used to perform calculations on big volumes of data has led to many useful insights. These include the relation between the type of training and the moment, duration and intensity of the training. A skater who has profited hugely from this is Kjeld Nuis. Data showed that stamina training in the morning proved ineffective for him, leading to an improvement in his training programme – and two gold medals in Pyeongchang.
For Orie, Knobbe and the skating sport in general, the big data journey is just beginning. For example, the phenomenon of ‘supercompensation’ still needs to be figured out. Supercompensation is what happens when an athlete temporarily lowers the training intensity, leading to recovery of the body and an increase in racing performance. Obviously, this effect needs to be timed perfectly in the run-up to an important race. It’s a complex equation, with the results of training sessions sometimes showing up months later and with training types having different effects on performance for sprinting distances (especially the 500 and 1,000 metres), on the one hand, and longer distances (1,500 metres and above), on the other.
Golden opportunities – everywhere
It is certainly not an exaggeration to say that the 2018 Winter Olympics have become the first big data Olympics. As a best practice, the example set by the Dutch skaters will be followed by other athletes looking to optimize their performance. And it’s not just in sporting events that data thinking is making such an impact. Many companies are becoming more data-driven. At
Basefarm, we work together with some of these companies to explore their existing wealth of unexplored data and find new use cases. In the manufacturing, service and maintenance industries, for instance, the use of predictive maintenance saves companies millions of euros every year. And this is only just the beginning. Undoubtedly, big data will shape the next Olympic games as well as the business world of tomorrow. Our question to you: will you be a contender for gold?
About Ronald Tensen
Ronald Tensen is Marketing Manager at Basefarm in the Netherlands. He has a broad experience in the internet and IT industry (B2B and B2C), successful at developing and launching new consumer services and brands, strong customer focus and of course he is a great team player!
Watch webinar on demand! Big Data inspiration with Big Data Chief Evangelist, Klaas Bollhöfer!
Big Data has become a buzzword over the last years. It is not just a stand-alone term but rather a combination of many aspects to reveal a whole picture.
You might ask why Basefarm in particular is hosting a webinar about Big Data?
We have been a managed service provider of mission critical solutions for years, and are now expanding our business with our acquisition of the German company “The Unbelievable Machine Company”.
Our Big Data expertise is relevant and interesting for a lot of industries – both in operational, developing and “ideation” perspective.
We have reference cases like Deutsche Post, Gebr. Heinemann, Audi, Deutsche Welle, Delivery Hero, Metro Group and Parship. Read more about *UM here!
In this session you will get an inspiring intro-webinar where we evolve Big Data possibilities presented by Chief Evangelist, Klaas Bollhöfer.
The webinar is for everyone, and you do not need any knowledge about the topic before the session. The session will be in English.
At the end of this session you will have a fundamental understanding of what Big Data is, the challenges that comes with it, why you should start looking into it in 2018 and last but not least – how you can turn your data into business opportunities.
Big Data Chief Evangelist – Klaas Bollhöfer
Klaas Bollhöfer has acted as the Chief evangelist of The unbelievable Machine Company, a Basefarm company, for more than 5 years now, and is pioneering data science in Germany, Europe and beyond. At the interface of business, IT, artificial intelligence and design he develops cutting-edge strategies, spaces, services, teams and sometimes escape routes, and describes himself as a for-, side- and backward thinker. Besides that he is founder and managing director of Birds on Mars, a Berlin-based consultancy exploring and developing the intersections of human and artificial intelligence. The time left is filled with lightning talks, guest lectures, program committee chairs and craft brewing. Klaas is a certified Scrum master, design thinker, mediator and coach and will never stop being curious.
Big Data is a buzz phrase that is used in various situations and is constantly developing.
To classify Big Data decisively is not so easy. Firstly, it is not just a stand-alone term but rather a combination of many aspects to reveal a whole picture. And secondly, Big Data is a buzz phrase that is used in various situations and is constantly developing. It is time to set things straight.
Buzz phrase? Collective term? Synonym?
All of the above. Fundamentally, Big Data represents large digital data volumes as well as the capturing, analyzing and evaluating of it. Therefore, Big Data is also the collective term for all digital technologies, architectures, methods and processes that are required for these tasks. Or as Hasso Plattner says: “Big Data is a synonym for large data volumes in a wide range of application areas as well as for the associated challenge of being able to process them.”
Large data volumes?
Very large. “By the year 2003, humans had created a total of 5 trillion gigabytes of data. In 2011 the same amount was created within 48 hours. Now, creating the same data volume requires just 7 minutes,” illustrated RBB Radioeins in simple and effective terms. Driven by the internet, social networks, mobile devices and the Internet of Things, the worldwide digital data volumes will grow another tenfold by 2020. In Germany alone the current figure of 230 billion GB will rise to 1.1 trillion GB.
This is exactly were Big Data comes into play: The huge data volumes are checked for relationships using a such algorithm, and the whole process requires a combination of several disciplines. “It ranges from traditional informatics and data science to interface design. Machine learning, deep learning and artificial intelligence to mathematics, statistics and data interfaces,” explains Florian Dohmann, Senior Data Scientists at The unbelievable Machine Company. “A lot of this is nothing new, but combining them all creates the basis for new opportunities.”
So it is only about data volumes?
Fundamentally, yes. Big Data is firstly defined by data volumes that are “too large, too complex, change too quickly or are structured too weakly to be analyzed with manual and traditional data processing methods,” according to Wikipedia. But to define where Big Data begins – i.e. from which point the targeted use of data becomes a Big Data project – you need to take a close look at the details.
Think data lakes are just a new incarnation of data warehouses? Our resident expert Ingo Steins rates the two.
Data lakes and data warehouses only have one thing in common, and that is the fact that they are both designed to store data. Apart from that, the systems have fundamentally different applications and offer different options to users.
A data lake is a storage system or repository that gathers together enormous volumes of unstructured raw data. Like a lake, the system is fed by many different sources and data flows. Data lakes allow you to store vast quantities of highly diverse data and use it for big data analysis.
A data warehouse is a central repository for company management, so it’s quite different. Its primary role is as a component of business intelligence: it stores figures for use in process optimization planning, or for determining the strategic direction of the company. It also supports business reporting, so the data it contains must all be structured and in the same format.
Challenges with data warehouses
Data warehouses aren’t actually designed for large-scale data analysis, and when used in this way these systems will reach their structural and capacity limits very quickly. We now generate enormous volumes of unstructured data which needs to be processed quickly.
Another limitation is the fact that high-quality analyses now draw on a variety of different data sources in different formats, including social media, weblogs, sensors and mobile technology.
A data warehouse can be very expensive. Large providers such as SAP, Microsoft and Oracle offer various data warehouse models, but you generally need relatively new hardware and people with the expertise to manage the systems.
Data warehouses also suffer from performance weaknesses. Their loading processes are complex and take hours, the implementation of changes is a slow and laborious process, and there are several steps to go through before you can generate even a simple analysis or report.
Virtually limitless data lakes
Data lakes, on the other hand, are virtually limitless. They aren’t products in the same way that data warehouses are, but are more of a concept that is put together individually and can be expanded infinitely.
Data lakes can store infinite different data formats in very high volumes for indefinite periods of time. Because they are built using standard software, the memory is comparatively cost-effective too.
Data lakes can store huge volumes of data, but need no complex formatting or maintenance. The system doesn’t impose any limits on processes or processing speeds – in fact, it actually opens up new ways to exploit the data you have, and can therefore help companies more generally in the process of digitalization.
Put on your swim suit
All you really need to start a data lake is a suitable database. This is relatively easy to set up with a solution like Hadoop. Companies who want to access a wide range of data and process it effectively in real time to answer highly specialized and complex questions will find that the data lake is the perfect infrastructure to realize this goal.
Ingo Steins is Unbelievable Machine’s Deputy Director of Operations, heading up the applications division from our base in Berlin. He has years of experience in software, data development and managing large teams, and now runs three such teams distributed across our sites. Ingo joined The Unbelievable Machine Company in January 2016.
Ingo Steins, Deputy Director of Operations, The Unbelievable Machine Company, part of Basefarm Group since June 2017