Data & digital architectures
Design and implement the best ecosystem blueprint to support your current and future business ambitions. Ensure a pragmatic design that is appropriate to your realities to support your data strategy. Implement state-of-the-art architectures, leveraging DevOps practices, to ensure the fastest and most cost-efficient path to support your business and secure scalable and future proof solutions.
Ready for your own success story?
Get in touch with our team of experts.
Blog post
Be more effective than dolly parton on open banking
Appropriate action is a combination of marketing automation and of the personal touch by your frontline staff. Make it data driven.
Blog post
Deep learning for unstructured data ? Yes, you gan !
Today, you take a picture of a paper bill and it gets suddenly processed by your banking app without you doing anything but confirming through Face Id recognition. Today, you speak to your microphone’s car while driving and it starts calling someone from your contact list. Today, you are probably old-fashioned if you never used google translate to process some sentence in another language, right?
Expert's opinion
Upgrade of a semarchy XDM Solution
In 2014, one of our clients (leading provider of packaging worldwide) sought a solution to bring structure to their customer base. They reached out to Keyrus who designed and developed the Customer Data Integration (CDI) tool.
Blog post
Why data science needs both machine learning and casual data analysis
Data Science is running complex machine learning algorithms on ever growing datasets. The promise towards business stakeholders is to replace gut decisions and experience with objective and improving algorithms. But is machine learning the only game in town data scientists need to help business decision making?
Blog post
Rise of the citizen data scientist
And why you still can’t replace your employees with software completely...
Blog post
Data Visualization and Decision Making
“In 2019, one of the leading actors in the Oil Industry, was assessing different possibilities for the implementation of a mobile payment solution in their B2B segment. In order to be able to take data driven decisions, they reached out to Keyrus to set-up a data visualization solution.”
Blog post
Data science explained by baking cake
A few times I have been asked what it is I do exactly as a data-scientist, and managers and potentials data-scientists especially are interested in the common struggles we as data-scientists have to deal with. Just listing all issues we comes across would not result in an interesting read, so I will present it to you in the form of an analogy you’re all familiar with: baking cake.
Blog post
Data and analytics : the four types of data analytics
You might have heard the saying Data is the new oil. This mainly refers to their potential value and in both cases this value is not merely in the raw product but rather results from the way it is processed. In this article we present a commonly used classification of data and analytics into descriptive, diagnostic, predictive, and prescriptive analytics. We’ll discuss each of these separately including some of the commonly used methods. Thereafter follows how these four types of data analytics relate to each other. First however we’ll explain what we exactly mean with data and analytics.
Blog post
The human behind the data (part 2 of 3)
In part 1 of this series you read all about the difficulties to stay objective when selecting the data you want to work with. Simpson’s paradox, multicollinearity, Robinson’s paradox, survivorship bias, and cherry picking were all issues showing how important your decisions as a processor of data are. In this second part we’ll show that you yourself can become data which will seriously influence the outcome of your research, and we’ll also show how critical it is to choose the right measurement tool.
Blog post
The human behind the data (part 1 of 3)
Humans are not very rational beings, even though we think we are. This impacts our personal as well as our professional lives, and the latter is of particular importance if you often work with data. If you work in business intelligence, data science, or any related field, people expect you to deliver them an objective truth. In this article we’ll discuss a lot of pitfalls that undermine this goal. Knowledge of these will help you avoid these mistakes and also to spot them in other people’s work. Many of the topics covered in this article involve pitfalls that can be classified as biases or paradoxes.
