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 this new reality, leveraging processes like ETL (Extract, Transform, Load) or API (Application Programming Interface) alone to handle the data deluge is not enough. As per the TDWI survey, more than a third (nearly 37%) of people has shown dissatisfaction with their ability to access and integrate complex data streams.
Introduction: The Customer DataModeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer datamodels. Yeah, that one.
Understanding Data Integration in Data Mining Data integration is the process of combining data from different sources. Thus creating a consolidated view of the data while eliminating datasilos. Limited Scalability : The process is not workable for handling large volumes of data.
The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.
We organize all of the trending information in your field so you don't have to. Join 17,000+ 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