Xebia Articles

From October 15 to 19, the second edition of GoDataFest, festival of data technology, took place. Every day of this event, organized by GoDataDriven, highlighted a specific technology or platform: AWS, Dataiku, Databricks, Google Cloud Platform and open-source.

What challenges do data-driven organizations face? What are the most popular technologies and providers? How do organizations in various countries implement data applications? Data Survey 2018, the largest market research on the use of data provides answers to these questions and insight into global developments when it comes to the use of data.

At GoDataDriven we enjoy sharing our skills. During Dutch Data Science Week our consultants were invited to lead a few training sessions. For example, Ivo Everts lead a signal processing training, Rodrigo Agundez shared his insights during a deep learning training. Check out the videos!

Dutch Data Science Week provided data scientists the opportunity to apply their skills during various hackathons on topics that have societal impact. Reducing energy waste, preventing heart attacks, and improving city life.

Participants enjoyed the opportunity to apply, learn and share their skills during three hackathons. For Eneco, UMCG and the 2-Day hackathon for the City of Amsterdam the after-movies are available here. Check out the videos!

During Dutch Data Science Week several meetups took place on various topics. Watch the after-movies of the Blockchain in Business meetup and the Data Donderdag meetup. 

Blockchain in Business Meetup with Schiphol – Transavia – Zorginstituut – Ledger Leopard and Safeguard App!

At the Heineken Experience the 22nd edition of the Data Donderdag meetup took place. During this edition, the following speakers took center stage: Nelli Gofman (Heineken), Stefan van Heukelum (KLM) and Kicky van Leeuwen (GoDataDriven/UMCG). Check out the videos!

The monitoring of compliance with the GDPR will start in just over a month. What does that mean for your work as a developer or data scientist? "You have to continuously ask yourself what data you really need, and how you can minimize the impact of its use."

One of the defining aspects of a public blockchain is that anyone can add information to it. However, before it is added, the information must be verified. All cooperating nodes in the network must agree on the definitions of valid, and invalid, information. For example, in the case of Bitcoin, there must be a consensus on what defines a legitimate transaction. When the new information is deemed valid by all participating nodes in the network, then the consensus is reached. This article explores how a decentralized network could achieve consensus through two different algorithms: proof-of-work and proof-of-stake.

Today, most companies still rely on either traditional databases or data warehouses to generate the reports their management needs to make informed decisions. Those solutions have gotten us pretty far, but they also have some serious drawbacks. For one, loading all that data into the same place and then generating reports can take hours - and that’s only if everything works right the first time around! If something goes wrong during the process (and something often does) it can take even longer.

“There’s lots of value in data analytics. But when the low-hanging fruit in a dataset is gone, it becomes harder to extract value from the data.” It is tempting for organizations to find biased answers and draw faulty conclusions, like mixing causation with correlation. In a recent presentation at the Meetup Business Experimentation, Adam Kelleher, lead data scientist at Buzzfeed, emphasized that this is not without risk.

Putting predictive models into production demands a streamlined workflow, and highly skilled data scientists and engineers. For the past few years, organizations have focused on developing strong proof of concepts and initial use cases. Now they must successfully introduce data-driven concepts into their daily operations. This year, data-driven companies will make productionizing data projects the priority and focus. Productionizing will increase exposure in other parts of the company, so data science teams will turn to proven methods, like microservices and APIs, to streamline their process. Data governance will become a standard part of any data project. Big Data technology has matured and is mostly enterprise-ready, increasing the pressure on and demand for data scientists. Major tech players like Google, LinkedIn, and Facebook will continue to open-source their product innovations in an early stage. In 2017, the gap between textbook data scientists and seasoned practitioners will widen further. Companies that retain, foster, and attract people who can apply data science in production will gain a significant competitive advantage.