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This blog goes over the key differences, the growing interdependence of privacy and cybersecurity, and a robust compliance policy rooting out digital risks.

Hence, companies use frameworks that guide them toward information security best practices to navigate this difficult path. Information Security Management Systems (ISMS) play a crucial role in this regard. Let's take a closer look at them.

With data lineage, organizations gain insight into data flow throughout the lifecycle. It can help them spot errors beforehand, manage data governance, analyze the impact, and make better data-driven business decisions.

As the world gets more and more digitized, the amount of data available on the internet has substantially increased, which brings up a new peculiar problem - How important is protecting personal data?

It is crucial to understand the source, movement, and application of data within the business to determine its value. This is where data lineage as a process and tool comes in to uncover and examine the data source to ensure that it reaches the right place.

Two of the most popular ways of data storage are data lakes and data warehouses. Often, the terms are used interchangeably, but there are many differences between data lakes and data warehouses.

As new data technologies evolve constantly, technical experts must formulate best practices to integrate new technologies into agile and robust data architecture models.

When devices process vast volumes of data, decentralization becomes necessary. Let's take a closer look to understand the link between IoT and decentralization and its security aspect.

Think of a scenario where a developer transferred an application from one operating system to another and debugged multiple errors. Would it not be easier to bundle the application with all its related components and then port it to run in the new environment?