This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
In the AI era, organizations are eager to harness innovation and create value through high-quality, relevant data. Gartner, however, projects that 80% of data governance initiatives will fail by 2027. This statistic underscores the urgent need for robust data platforms and governance frameworks.
Workshop facilitation is not an easy trick, folks Hello, folks Another treat to share – a new episode of the Passionate Business Analyst podcast with Jan Meskens, Data & AI strategy consultant. A data-driven culture fosters trust and collaboration around data usage.
What is one thing all artificial intelligence (AI), business intelligence (BI), analytics, and data science initiatives have in common? They all need data pipelines for a seamless flow of high-qualitydata. Hevo Data is one such tool that helps organizations build data pipelines.
In todays digital age, managing and minimizing data collection is essential for maintaining business security. Prioritizing data privacy helps organizations ensure they only gather necessary information, reducing the risk of data breaches and misuse.
In todays rapidly evolving global landscape, data sovereignty has emerged as a critical challenge for enterprises. Businesses must adapt to an increasingly complex web of requirements as countries around the world tighten data regulations in an effort to ensure compliance and protect against cyberattacks.
In todays data-driven world, organizations manage vast amounts of data. As enterprises expand and grow business functions, theres corresponding linear growth in operational data. This encompasses both master data and transactional data.
Data migration the process of transferring data from one system to another is a critical undertaking for organizations striving to upgrade infrastructure, consolidate systems, or adopt new technologies. However, data migration challenges can be very complex, especially when doing large-scale data migration projects.
I go to data conferences. We have lots of data conferences here. I’ve taken to asking a question at these conferences: What does dataquality mean for unstructured data? Frequently. Almost always right here in NYC. Over the years, I’ve seen a trend — more and more emphasis on AI. This is my version of […]
The core capability of an Enterprise Architecture Management tool: Planning the future IT landscape Towards a mechanical planning of the future IT landscape In this short post, I discuss what an Enterprise Architecture Management (EAM) tool is and how an EAM should be understood in relation to metadata management in general.
In preparation, weve been pouring over this years data and we were curious to know what 2024s top performing Field Notes were. Moving a product into the cloud isnt going to generate cost savings if its architecture isnt modernized and it stays a monolith. We had some wins, some losses, and a TON of opportunities to learn and grow.
It connects regular human language with machine data using a combination of AI, computer science, and computational linguistics. These algorithms first identify the patterns in data and then convert this data into a format that computers can work with. Sentiment Analysis assesses the emotions or opinions expressed in a text.
Around this time of year, many data, analytics, and AI organizations are planning for the new year, and are dusting off their crystal balls in an effort to understand what lies ahead in 2025. But like all predictions, they are only helpful if they are right.
Data collection sits at the foundation of clinical trials, historically gathered from meticulous in-person visits to clinical trial sites.To improve efficiency, clinical trial research has significantly transitioned in recent years.
We organize all of the trending information in your field so you don't have to. Join 57,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content