WHY IT MATTERS: Digital Transformation

The core idea behind data lineage is the ability to fully understand how data flows from one place to another within the infrastructure that was built to house and process it. It seems like it should not be a difficult problem, but it is. In fact, it is a huge issue for organizations as they face 2022 and beyond. If organizations do not know where their data comes from or goes, they have uncontrolled environments that have risk on many different levels. Having uncontrolled data environments means that it is also very difficult to extract value from data, and data is the new oil or new gold. Organizations that cannot extract value from data stand a good chance of being outcompeted and replaced by organizations that can.

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

Business intelligence white papers on BI industry trends, and the latest BI and analytics analyst reports from Gartner, Dresner, BARC and Forrester.

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.

Gartner Top 10 Data and Analytics Trends for 2021

12 technology trends that will help shape the future of digital business.

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.

Stay ahead of threats with the insights in the 2020 Data Breach Investigations Report (DBIR) from Verizon Enterprise Solutions. Read the official report now.

This infographic examines the privacy policies of Facebook, Google, Apple, Twitter, Amazon and Microsoft.

The C3.ai COVID-19 Data Lake uniquely integrates multiple data sources in a unified data model, ready for analysis – not just a list of links or a collection of data sets. Stop wasting time wrangling data and focus instead on generating insights. Access data at no charge with any utility that supports RESTful APIs.

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.

Use the 2019 Gartner Hype Cycle for Digital Marketing and Advertising to prioritize your marketing technology investments.

The number of marketing technologies available to businesses has soared in the last five years – from about 150 in 2011 to over 3,800 in 2016, according to ChiefMarTech.com. And a study by eConsultancy and Tealium found that businesses use on average more than 20 marketing technology vendors to deliver their campaigns and customer experiences. This rapid expansion of the MarTech space has created significant challenges for marketing, technology, and management teams alike making it difficult to build cohesive, end-to-end customer experiences because of gaps between marketing technologies, the data they leverage, and how they are implemented. This paper provides an overview of the four core problems businesses are facing with data due to the proliferation of marketing technology, and how adopting a Universal Data Hub (UDH) solves these problems. 

Machine learning continues to gain traction in digital businesses, and technical professionals must embrace it as a tool for creating operational efficiencies. This updated primer discusses the benefits and pitfalls of machine learning, architecture updates, and new roles and responsibilities.

Machine Learning (ML) is a new field, and there is a competitive advantage to those first adopters. Yet, many companies are buying ML technologies and developing strategies before they have taken the appropriate step back to evaluate whether their dat

Finding shabby abodes like these and making them respectable is the load-bearing wall of Amherst’s strategy. Amherst depends on humans to find cities, towns, and neighborhoods where fixer-uppers can become profitable, then relies on automation to pick individual homes. Negri, 31, heads the human team. He spends 150 days a year on the road overseeing Main Street ­Renewal’s operations from Atlanta to Denver, searching for “sweet spot” neighborhoods that combine affordable rents with a strong middle-income employment base.

The technology to unlock the value of personalization at scale is readily available, and you may be surprised how much of it you already have in place. You don’t need to tackle everything at once. Develop a road map with regular stage gates to evaluate platform progress based on the trade-off between value at stake and ease of implementation. Identify and prioritize quick wins that make it easy for the organization to continue funding the transformation.

Personalization is a crucial weapon in the marketer’s arsenal to achieve that goal. That’s how businesses can deliver tailored recommendations, content, offers, and experiences, across all channels and devices, along the entire customer journey. Personalization at scale has the potential to create $1.7 trillion to $3 trillion in new value (Exhibit 1). Capturing this value will require mastering the technologies and addressing the organizational disconnects—all while forging trust with customers and protecting their data.

• Sales, Marketing and Operations are most active early adopters of IoT today.
• Early adopters most often initiate pilots to drive revenue and gain operational efficiencies faster than anticipated.
• 32% of enterprises are investing in IoT, and 48% are planning to in 2019.
• IoT early adopters lead their industries in advanced and predictive analytics adoption.