Star schemas and snowflake schemas are the two predominant types of data warehouse schemas. A data warehouse schema refers to the shape your data takes - how you structure your tables and their mutual relationships within a database or data warehouse. Since the primary purpose of a data warehouse (and other Online Analytical Processing (OLAP) databases) is to provide a centralized view of all the enterprise data for analytics, data warehouse schemas help us achieve superior analytic results.

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use. According to Gartner, by 2023 65% of the world’s population will have their personal data covered under modern privacy regulations.

Don’t sacrifice scalability for savings - have it both ways When left unchecked, the cumulative costs of your company data can ramp up fast. From training CPU-intensive machine learning algorithms that aren’t used in production to supporting enormous databases storing every minute event “just in case”. Letting your data operating costs run without checks and balances can quickly cause costs to bloat beyond your allocated budgets.

This is a guest post with exclusive content by Bill Inmon. Bill “is an American computer scientist recognized by many as the father of the data warehouse. Inmon wrote the first book, held the first conference, wrote the first column in a magazine, and was the first to offer classes in data warehousing.” -Wikipedia. The five critical considerations for corporate loyalty.

Domino’s Pizza, one of the world’s top restaurant brands, already knows how to translate data into great customer experiences and stronger sales with Talend. Over the past five years, the company has used Talend to integrate 100’s data sources into a single source of customer information — and has harnessed that data to improve everything from personalized promotions to logistics to financial forecasting. The latest ingredient to their success?

“What should you and your business focus on when trying to create better customer journeys and beat competition?” That was the question we asked Countly data captains (also known as Countly customers) when trying to determine how well they collect customer experience metrics and how well they were using that data to make data-driven decisions.

Organizations today are expected to fully utilize their data to speed decisions and drive business value. Modern analytics can help.