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When data integrity is discussed, it involves ensuring that the data within an organization is complete, accurate, consistent, accessible, and secure. These aspects together determine the reliability of the data. Assessing data quality against these criteria helps determine how suited the data is for its intended use. For organizations relying on data for decision-making, providing data access to teams, and offering data to customers, maintaining good data quality and integrity is crucial.
Data migration, the transfer of information between systems, is a pivotal operation in modern business. At its heart is data mapping, the blueprint ensuring each piece of data finds its new home accurately.
In this article, we’ll guide you through essential data mapping steps for a seamless migration.
No doubt that one of the greatest’s assets today is data. So, as consumers provide significant amounts of data to businesses, Data Privacy, Data Protection, and Compliance, became a major subject.
Data Privacy, Data Protection, and Compliance – all these are similar, but not the same. Most IT professionals use these terms interchangeably. The reason for that is somewhat simple, data protection deals with privacy, data privacy deals with protection and both detail compliance requirements. So, let’s expand on each term individually!
We’ve written about the vast amounts of data that organizations collect and process daily. Now, we dive into Data Cleaning! An important matter if your organization wants to process and analyse high quality data.
The pressure on businesses to become more efficient and better optimized keep increasing every day. To achieve such goals, many companies keep investing in digital transformation, exploring innovative technologies to get ahead on their competition. One such technology is Intelligent Document Processing (IDP).
In today’s digital world, the ability to collect, analyse and process data is one of the most principal factors for any organization, due to the vital role that data plays in day-to-day company decisions. With that, the amount of data generated keeps increasing, and with it the challenges of processing such amounts of data.