
How Inaccurate Data Impacts Your Bottom Line
Most data problems don’t show up as dramatic failures. They appear as small problems or hide in plain sight. A number that needs to be

Most data problems don’t show up as dramatic failures. They appear as small problems or hide in plain sight. A number that needs to be

Amazon generates 35% of its revenue from data-powered recommendations. Netflix enjoys an 89% retention rate by personalizing every experience using viewer behavior, preferences, and interaction

Everyone says data is power. But let’s be honest: in most companies, data is politics. It’s locked in tools no one understands, hoarded by teams

About 68% of enterprise data goes unleveraged. Why? Because 90% of data is unstructured, making it difficult – if not impossible – to extract actionable

Data matching helps ensure that disparate data sources are accurately aligned, cleansed, and ready for use. And that’s where an effective data catalog becomes essential

Data cleaning and matching are critical processes for maintaining data integrity and deriving actionable insights from large datasets. This review dives into the advanced techniques

Data cleansing and data preparation are not the same. When you are cleaning data, you are removing inaccuracies, invalidities, and junk from it. But when

Where there is data, there is regulation. Most of all in the financial industry. Banks, insurance companies and financial institutes must deal with a complex

Data preparation tools have been around for quite some time now. Most of these tools though require users to be proficient in programming languages &

As companies are spending billions of dollars investing in big data with the hopes of turning data into money, the need for efficient, easy-to-use data preparation

Different changes in the world of data have allowed organizations to manage their day-to-day requirements while preparing for big data future, analytics, and real-time operations.

What if the very process you rely on to manage your data is holding your business back? Extracting meaningful insights from the flood of data