Data masking helps you and your organization improve data security. The term means hiding sensitive information but keeping it useful and realistic, so you can continue to develop and test with it while keeping it private. Many companies underestimate the power and importance of data masking. According to IBM Security, the average cost of a data breach has reached almost $4 million. Therefore, it’s essential to shore up your data security, no matter the size of your organization.
Data masking personally identifying information (PII) may include many techniques, such as data shuffling or data substitution, that help you make data unidentifiable. It’s an important aspect of data security that every organization should care about.
This article will help you understand what data masking is and why it matters. Further down the line, we’ll discuss a few data masking techniques—including one that isn’t recommended for most types of sensitive data. But first, let’s discuss data masking in general.
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In today’s digital-driven age, keeping business and customer data secure is more important than ever before. Not only are data breaches extremely costly, they’re also incredibly disruptive to business. Once a breach occurs, organizations have to move quickly to remedy it. Let’s hope customers won’t be too upset when they find out what’s happened.
Just how expensive are breaches? Hold your breath. A recent study revealed that the average data breach sets a company back $3.9 million. More granularly, they can expect to pay about $150 per compromised record. The price tag is even heftier in the healthcare industry, with the average breach costing upwards of $6.45 million. What’s more, U.S.-based companies feel the brunt of the pain, with the average data breaches setting them back $8.19 million.
But, when it comes to data security, financial impacts aren’t the only thing to worry about. Businesses need to be mindful of several other serious consequences, including theft of intellectual property and unhappy customers.
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Do believe the hype!
Many companies are migrating their data to the cloud. Cisco forecasts that by 2021, 94% of all workloads and compute instances will be processed by cloud data centers.
But Gartner’s 2018 hype cycle did have cloud migration falling into the “trough of disillusionment.” This could be because migrating data to the cloud isn’t easy. Organizations encounter numerous issues when trying to take advantage of the cloud’s benefits.
Organizations need to plan properly before undertaking such an endeavor. Let’s look at what you can do to minimize the issues that can plague a cloud data migration project.
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Most businesses nowadays use data analytics to improve the performance of their products or to study the market. Data analytics is at the heart of the fourth industrial revolution. However, making sure that the graphs and charts made are trustworthy and reliable is not as easy as producing them.
This is where today’s post comes in. I would like to talk to you about auditing data analytics and how it works. First, I’ll tell you about the different pitfalls of skipping over this step in your workflow. Then, I’ll explain the different steps to follow to audit your data analysis. I’ll also be sure to mention the different techniques you should keep in mind for your audit.
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Utter those two words to someone who has not heard them before, and you’re likely to get back a quizzical look. A look that encompasses intrigue, doubt, skepticism, and maybe even a little cynicism.
Is this some buzzword made up by consultants? Is this something my kind of business really needs? If it is important, then who is responsible—finance, marketing, IT?
This post will help anyone who’s curious understand what data governance really is, why you should care about it, and some tangible business benefits that it can lead to.
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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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Do you know the biggest challenge companies face? It’s maintaining a large information base. Too much information compromises data flow. But it’s important to manage the information at hand. A company has to succeed in managing data well. Only then can the employees make the right decisions. That’s where data lifecycle management comes in. What is data lifecycle management, you ask?
No matter how important data is, it loses its relevance over time. After all, no information can hold the same relevance for eternity! You have to manage the data to make the most out of it. But before we you can do that, it’s important to understand the data lifecycle.
In this post, we’ll take a detailed look into data lifecycle management (DLM), including the different phases of DLM and best practices. We’ll also highlight the importance of managing the data lifecycle in a company. So, let’s dive into the details.
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Businesses across all industries are racing to embrace digital transformation. At the same time, serious dangers in the form of data security risks lurk in the shadows.
Each day, businesses face a multitude of cyber threats from sophisticated hackers and bad actors. As for the consequences of enduring a cyberattack? They’re severe. According to one recent IBM study, the average U.S. data breach costs upwards of $8.19 million.
Think a data breach won’t ever happen to your company? Think again. Hackers frequently target even the most sophisticated organizations. Companies like Uber, Experian, Sony, Anthem, and Target have all been attacked in the past few years.
Chances are that your company has far fewer resources than any of these five. In that light, your business is probably even more vulnerable than they were.
When a hack occurs, immediate financial losses are only one piece of the puzzle. Data breaches are extremely disruptive to business. If your customer finds out that their data has been stolen, there’s a high probability that they will be hesitant to transact with you down the road. What’s more, you could also have your intellectual property (IP) stolen, leading to even greater losses in the long term.
If you’re concerned about the data security risks that your business faces, you’re not alone. A recent study revealed that 44% of U.S. CEOs feel that the risks involved with digital adoption are preventing their businesses from moving forward. Companies are literally chained to their old ways because they aren’t sure how to protect themselves against modern threats.
Before we examine what you can do to reduce the likelihood your business suffers a data security setback, let’s take a step back and get our definitions straight.
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