WHY IT MATTERS: Digital Transformation

Here’s a list of categories that are the key pillars to today’s Modern Data Stack. Inside each, you’ll find the most popular companies within that category.

An agile approach to overhauling data architecture improves speed, flexibility, and innovation.

To succeed with data and analytics initiatives, enterprises need a comprehensive view of critical technology capabilities. We believe these Hype Cycles draw a holistic view of technologies — including the new addition of lakehouse architecture as a trigger, which can make data and analytics programs modern, broadly applied, richer in functionality and successful.

Get the report and gain insight into:
-Changes in this Hype Cycle — including the addition of Lakehouse
-The current state of data management maturity
-The Priority Matrix

At first glance, the data fabric and the data mesh concepts sound quite similar. Meshes are often made from a type of fabric, after all, and they are both malleable items that can be lain atop things–in this case, your IT systems that are subject to the ever-growing data crush.

But there are fundamental differences to these two approaches, so it’s worth taking some time to learn their differences.

Dans ce profil, vous trouverez :

1. Une revue historique de la position du Québec en matière d’intelligence artificielle;
2. Une définition de la main-d’œuvre du secteur;
3. Un portrait global de l’emploi ;
4. Une taxonomie des métiers de l’intelligence numérique – que nous définissons comme étant la main-d’œuvre spécialisée en intelligence artificielle ainsi que les métiers périphériques essentiels reliés ;
5. Des fiches métiers traitant de l’estimation du bassin de talents et incluant de multiples informations spécifiques salaires, tâches principales, référentiel de compétences techniques et transversales);
6. Une répartition industrielle des métiers de l’intelligence numérique
7. Un portrait de la demande de compétences et de la formation, incluant les forces et défis concernant ce profil  de talents qui change à une vitesse déstabilisante.
8. L’ensemble de nos données sur l’utilisation des outils numériques, sous la forme de matrices Emploi/Outils numériques et technologies, en annexe du document.

In our in-depth playbook, we cover everything you need to know about:
The benefits of a Customer Data Platform;
Getting started with tried-and-true use cases;
Building a team with the right skillsets for any size organization;
Securing buy-in from executives across the company;
Measuring the ROI of CDPs to prove value to different stakeholders

How to build a modern, scalable data platform to power your analytics and data science projects.

An agile approach to overhauling data architecture improves speed, flexibility, and innovation.

Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights. Significant vendor growth, product development and myriad competing visions reflect a healthy market that is maturing rapidly.

The most recent version of the dashboards and Business Intelligence tools list. Start here to understand the BI landscape, browse options and understand pricing.