Are you responsible for the security of big data at your organization? Have you ever wondered if big data security is different from regular data security? Would you like to know more about some of the challenges of big data security?
If you answered “yes” to any of these questions, read on. Today, we’ll examine what makes big data unique and special. Then, we’ll focus on the high-level architecture and security challenges that come with big data. In the end, we’ll visit an imaginary car factory and have some fun with big data security. We have lots to cover, so let’s get started.
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Test data management is about planning, storing, and using test data effectively. Let’s suppose your company develops an app and it becomes a huge hit. The app has millions of users, and the demand for more features is increasing day by day. With new features in the development stage comes the need for new test cases. And new test cases demand more and more test data. Did you ever wonder how to manage such a huge load of test data? Manually managing it is next to impossible. So what are test data management tools?
In this post, we’ll discuss what a test data management tool is and why you need such a tool. After that, we’ll take a look at the features offered by six popular test data management tools. So, let’s get started.
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Data governance has to do with people, processes, and technology. Those three elements have a big influence on the data you capture and the quality of that data. Data governance helps you define standards and processes so you can guarantee the highest data quality.
This post will explain the benefits of data governance and provide you with seven practices you should consider when implementing your data governance strategy.
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Data security is a leading concern for CEOs across all industries—and for good reason. Each day, businesses face a wide range of potentially game-ending cyber threats. Malware, phishing, DDoS attacks, and rogue employees are just a few of the dangers lurking in the waters of digital transformation.
Why exactly is data security such a concern?
It’s simple: Data breaches can be exorbitantly expensive. In fact, a recent study by IBM revealed that the average cost to US companies is $8.19 million. Healthcare breaches tend to be the most expensive to resolve, with the average cost hovering around $6.4 million.
What’s more, the same IBM study showed that loss of customer trust accounts for 36% of the damages that companies face. This goes to show that data breaches are not only extremely disruptive and costly to businesses in the short term, their effects will be felt for years down the road—and perhaps even indefinitely.
In this post, we’ll be answering the question: What is the biggest threat to data security? Further, we will give you some tips on how to mitigate it.
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Big data is one of the top trending technologies today. I think it’s safe to assume that where there’s big data, there’s valuable information. And where there’s valuable information, there are security threats.
Obviously, we need to secure our data, and that’s what I’ll be talking about today. I’ll tell you what big data security is and talk about the challenges you’ll face with it.
The major topics I cover in this post are:
- What is big data security?
- Challenges of big data security
- Technologies used for big data security
By the end of this post, you should have an understanding of what big data security is, why big data security is important, where you would face challenges while setting up security for big data systems, and the approaches you can utilize to provide big data security.
Continue reading “What Is Big Data Security? A Complete Definition”
Data security is important nowadays. We see data breaches all over the news, but companies still don’t protect their users’ data accordingly. So how do you handle PII data in a database?
This post will explain important practices about storing personally identifiable information (also called PII data) safely in a database. We’ll explore topics including data encryption, data permissions, and the usefulness of a database management system.
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Once upon a time, application testing was really easy. All you had was a single mainframe and limited data sets. Users were satisfied with a simple application that provided the basic features. But nowadays the competition to develop the best app is intense. Companies are focusing on developing applications that provide the best user experience and features. This means companies are also focusing on application testing to ensure the features they implement work. And with testing, there comes a need to understand how to do test data management in agile. Why?
When it comes to testing, there are many factors that require consideration. Compliance standards like the European Union’s General Data Protection Regulation (GDPR) have added new challenges to testing, and overlooking even one area can expose an organization to massive risks, not the least of which are lawsuits and compliance fines. So how do you avoid these pitfalls? What do you think should be the number one task for testing teams?
With an increase in companies adopting agile, the need to accurately and efficiently manage test data has also increased. Proper test data is a must when shifting to a more flexible development process. After all, it’s the best way to improve the quality of tests. Therefore, it’s important to understand how to do test data management (TDM) in agile. Because there are various boxes to check before you can mark a test complete, TDM should be a high priority when it comes to streamlining testing.
Because agile is relatively new, handling TDM can be a challenge. So, in this post, we’ll take a detailed look at how to handle TDM in agile. Before we get started, though, it’s important to understand exactly what TDM means.
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So you’re convinced it’s time to start a data governance program. That’s great! Data is a vital asset to organizations today, and governing data is a strategic necessity (and often a regulatory necessity as well).
But where do you start? How do you launch such a program from scratch and ensure its success?
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A report from Deloitte shows that in recent years, the need for people analytics has reached high points of over 70 percent. More companies rank people analytics as a “high priority” in the organization. However, the same report lays down a frightening truth: Less than 10 percent of those organizations actually have usable data. So, are we not asking the right questions? Do we even know what to look for? This isn’t yet another post on complicated algorithms; after all, you’re not here to learn data science.
This post is about answering one question: how can data analytics improve the measurement of employees’ performance? It’s not going to be easy— assessing your human capital is always easier said than done—but I’ve got your back.
But before we dig any further, it’s important to make sure that we’re on the right page. There are a lot of buzz words out there, but data-driven management is by no means a new concept. Since the concept of “the cloud” came up, companies started moving their workforce to it. Big item metrics like acquisition (e.g. cost per filling, training ROI) led companies to also ask questions about revenue, retention, and, more importantly, the question we’re here to talk about: how to track performance.
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For any company or a single user, data is very important. In the wrong hands, it can cause chaos. With the growing cybersecurity threat, you need to keep your data safe, especially when your customers’ data is there as well.
But how will you know if your company’s data security is perfect to deal with hackers or prevent any breach? A data security audit is the answer.
Building on that, in this post we’re going to discuss what a data security audit is. We’ll also discuss why your company needs a data security audit. Finally, we’ll get to know some best practices before you carry out a data security audit at your company. So, let’s take a deep dive.
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