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
Here’s a brief comparison: Tableau: For data visualization specialists, Tableau is more preferred. QlikView: Provides powerful datadiscovery and analytics capabilities but is not as user-friendly as Power BI Looker: Mainly data exploration and, for companies already invested in Google’s ecosystem, makes even more sense.
Traditionally, these are the people who spend their days sourcing and managing the data pipeline, governance and security, customization, deployment, integration, automation, datadiscovery, calculations, reporting, and visualizations. These could be data engineers, developers, or analysts.
By AI taking care of low-level tasks, data engineers can focus on higher-level tasks such as designing datamodels and creating data visualizations. For instance, Coca-Cola uses AI-powered ETL tools to automate data integration tasks across its global supply chain to optimize procurement and sourcing processes.
While a data catalog serves as a centralized inventory of metadata, a data dictionary focuses on defining data elements and attributes, describing their meaning, format, and usage. The former offers a comprehensive view of an organization’s data assets. Are the benefits just limited to data analysts?
Builds a Unified Data Language A common business language and data quality rules help everyone in the organization understand data terms and standards similarly. This approach avoids confusion and errors in data management and use, making communication across the company more straightforward.
It ensures that data from different departments, like patient records, lab results, and billing, can be securely collected and accessed when needed. Selecting the right data architecture depends on the specific needs of a business. Discoverability Centralized metadata management simplifies data discoverability.
By Industry Businesses from many industries use embedded analytics to make sense of their data. In a recent study by Mordor Intelligence , financial services, IT/telecom, and healthcare were tagged as leading industries in the use of embedded analytics. Healthcare is forecasted for significant growth in the near future.
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