Data Warehouse

AI is a collection of technologies that excel at extracting insights and patterns from large sets of data, then making predictions based on that information.

A data warehouse is a large collection of business data used to help an organization make decisions. The concept of the data warehouse has existed since the 1980s, when it was developed to help transition data from merely powering operations to fueling decision support systems that reveal business intelligence.

The large amount of data in data warehouses comes from different places such as internal applications such as marketing, sales, and finance; customer-facing apps; and external partner systems, among others.

Some Benefits of A Data Warehouse

  • Better data — Adding data sources to a data warehouse enables organizations to ensure that they are collecting consistent and relevant data from that source. They don’t need to wonder whether the data will be accessible or inconsistent as it comes in to the system. This ensures higher data quality and data integrity for sound decision-making.
  • Faster decisions — Data in a warehouse is in such consistent formats that it is ready to be analyzed. It also provides the analytical power and a more complete dataset to base decisions on hard facts. Therefore, decision-makers no longer need to reply on hunches, incomplete data, or poor quality data and risk delivering slow and inaccurate results.

Future of the Data Warehouse

As businesses make the move to the cloud, so too do their databases and data warehousing tools. The cloud offers many advantages: flexibility, collaboration, and accessibility from anywhere, to name a few. Popular tools like Amazon Redshift, Microsoft Azure SQL Data Warehouse, Snowflake, Google BigQuery, and have all offered businesses simple ways to warehouse and analyze their cloud data.

The cloud model lowers the barriers to entry — especially cost, complexity, and lengthy time-to-value — that have traditionally limited the adoption and successful use of data warehousing technology. It permits an organization to scale up or scale down — to turn on or turn off — data warehouse capacity as needed. Plus, it’s fast and easy to get started with a cloud data warehouse. 

The cloud data warehouse architecture largely eliminates the risks endemic to the on-premises data warehouse paradigm. You don’t have to budget for and procure hardware and software. You don’t have to set aside a budget line item for annual maintenance and support. In the cloud, the cost considerations that have traditionally preoccupied data warehouse teams — budgeting for planned and unplanned system upgrades — go away.

Organizations can get more from their analytics efforts by moving beyond simple databases and into the world of data warehousing. Finding the right warehousing solution to fit business needs can make a world of difference in how effectively a company serves its customers and grows its operations.

Knowledge Base

How it Works

AI is a collection of technologies that excel at extracting insights and patterns from large sets of data, then making predictions based on that information. That includes your analytics data from places like Google Analytics, automation platforms, content management systems, CRMs, and more.

AI is a collection of technologies that excel at extracting insights and patterns from large sets of data, then making predictions based on that information. That includes your analytics data from places like Google Analytics, automation platforms, content management systems, CRMs, and more.

Artificial Intelligence is a technique that enables machines to mimic human behavior. Whereas, Machine Learning is a subset of Artificial Intelligence. It is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so.