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 summary, predicting future supply chain demands using last year’s data, just doesn’t work. Accurate demand forecasting can’t rely upon last year’s data based upon dated consumer preferences, lifestyle and demand patterns that just don’t exist today – the world has changed.
This proliferation of data and the methods we use to safeguard it is accompanied by market changes — economic, technical, and alterations in customer behavior and marketing strategies , to mention a few. zettabytes of data in 2020, a tenfold increase from 6.5 Data pipeline maintenance. Unable to properly governdata.
Cloudera’s data lakehouse provides enterprise users with access to structured, semi-structured, and unstructured data, enabling them to analyze, refine, and store various data types, including text, images, audio, video, system logs, and more.
Challenges in Data Management Data Security and Compliance The protection of sensitive patient information and adherence to regulatory standards pose significant challenges in healthcare data management. This proactive stance safeguards against erroneous insights or decisions driven by flawed or incomplete datasets.
AI platforms assist with a multitude of tasks ranging from enforcing datagovernance to better workload distribution to the accelerated construction of machine learning models. What types of features do AI platforms offer?
See The Future of Data and Analytics: Reengineering the Decision, 2025. You mentioned a few times that most enterprises are not good at datagovernance. It is much less just about defensive controls and more about enabling capabilities. Which tools would you recommend for getting governance in place? Do you agree?
We organize all of the trending information in your field so you don't have to. Join 42,000+ 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