Personally identifiable information (PII) data is any sort of data that might reveal a person’s identity. Moreover, PII data includes data such as a person’s name, address, date of birth, credit card details, Social Security number, or even medical records.
In the digital age we live in, data has become the most valuable asset for a company. This means a company might hold a lot of PII data, making it a target for hackers to breach their data. Nowadays, PII data is considered to be highly sensitive data that needs to be properly protected.
Many organizations underestimate the effort to protect their users’ data. Besides that, many users aren’t aware of the number of companies holding PII data about them. Therefore, we need strong data privacy tools like encryption to protect users’ PII data.
This article will guide you through encryption methods and define a plan that will help you get started with implementing data encryption techniques. First, let’s introduce encryption.
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What is data modeling in software engineering? Let me explain.
Every company has a lot of data in its databases. But don’t you think managing massive chunks of data can create confusion? We know for a fact that data becomes obsolete after some time if it’s unorganized. After that, it doesn’t matter how relevant it was. To clarify, without organization, data isn’t of much use. Moreover, you can’t use it to its full potential. In fact, messed-up data makes it tough to store, retrieve, and capture it efficiently.
Data modeling is a method that helps you avoid the cons that come with poorly designed data. It’s like a map that helps organize information for better use. In this post, we’re going to take a detailed look at data modeling and why it’s important. We’ll also check out the types of data models and the pros and cons. This post will also cover the steps of data modeling in detail. So, let’s dive right in!
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If you’re like most data scientists, scoping projects probably isn’t your favorite part of your job. Scoping projects can often feel like a mix between tedious busywork meant to placate executives and wildly guessing. Chances are, you’ve had projects in the past totally miss their scoping requirements, only to see no negative side effects. Feeling like your work is meaningless, tedious and baseless is a recipe for frustration, no matter your profession. For data scientists, who are used to measuring things to determine their efficiency, it’s excruciating.
Fortunately, it’s possible to get better at scoping your projects. Project scoping is never going to go away, and despite what it might feel like, it’s not meaningless. Project scoping helps decision-makers determine how to prioritize projects for an organization. Doing it well means both that the most important projects receive the attention they need, and also that you’re more likely to be successful when you embark on a new project. In this post about how to scope a data engineering project, we’ll walk through a detailed guide on what you need to understand.
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