Data mapping is crucial to the success of every data process. One mistake in data mapping can ripple throughout your business, leading to replicated errors, and ultimately, to inaccurate analysis….
HIPAA Compliance and ETL – Do they Go Hand in Hand?
Data privacy is critical – not only for customers but also for businesses everywhere. And this does not only apply to commercial businesses. The Health Insurance Portability and Accountability Act…
Data Integration Tools: On-Premise vs Cloud Integration
Let’s face it: Inaccurate data is affecting your company’s bottom line. In fact, a whopping 77% of companies believe their bottom line is affected by inaccurate and incomplete data, wasting…
No-Code ETL: How Is It Better Than Manual ETL Coding?
For years, companies have been using the hand-coded Extract, Transform and Load (ETL) process to load data from various data marts into data warehouses. Not only is this way of…
Data Preparation Process: Steps, Importance, & Tools
Data and analytics are shaping the future to be black and white. The future promises more visibility, eliminating risks and assumptions so businesses can make well-informed decisions. The power of…
ETL Testing Checklist: Avoid Data Integration Disasters
“When an ETL process can go wrong, it would go wrong” – Murphy on Data Integration. It is a reality that ETL processes breakdown regularly unless constantly maintained, leaving developers…
How Data Integration is Revamping Healthcare and Pharma
Today, data has become a critical asset for effective business decision-making. With the exponential rise in information, managing vast amounts of data has become strenuous. Under-staffing, lack of technical expertise,…
How to Improve ETL Performance in Data Integration Process?
Consider this: You are hired as a senior BI analyst for a retail chain with over 100 stores around the country. On the first day of your job, you observed…
Data Quality Rules in Data Integration – All You Need To Know
There are six data quality dimensions that play an important role in this case. Each of these is illustrated further with data quality rules examples for greater clarity.
Data Staging Area: How It Solves Data Quality Issues
Consider the data staging layer as an intermediary layer that sits between the source and the target. The staging approach can add significant value to the data integration process by…
Eliminate Data Silos with Data Virtualization In Business
Data Virtualization allows companies to access data from disparate sources like data warehouses, NoSQL databases and data lakes without any physical data movement, through a virtual layer that hides source data complexities from the end-user.
Six Data Integration Pain Points & How to Solve Them
Recent statistics from OKTA inc show that a significant proportion of large companies deploy more than 200 applications to support their operations. Data is created, captured, transformed, or transmitted by…