The purpose of Masking is to hide sensitive data and make it more difficult for someone to find out or use it.
There are plenty of reasons why data masking is important in your marketing efforts. For one, it’s a useful way to ensure that your company isn’t collecting sensitive data that could be used against you if an individual or group ever discovers what you’re doing.
In today’s post, we will cover a great technique that is commonly used in data science, but many marketers may not know about it. This technique is called masking.
1. Explain Masking Data
Masking data is a method of protecting privacy that involves hiding a person’s personal information in a larger set of data. For example, suppose you want to hide your personal email address from someone. If you say “[email protected]”, the recipient might think you’re just another generic email sender. However, if you disguise your name in a list of 100 people’s names, it makes it much less likely that the recipient would recognize it.
The data you present to customers doesn’t always match up to what you are actually delivering, which can be confusing and potentially problematic. You want to be transparent about the data you’re presenting but you also need to protect sensitive information. How do you do that? By masking the data. Masking means hiding the underlying data and making it look like something else. It can be done by removing specific pieces of data from a piece of information, adding more detail or explanation to it, or simply changing the formatting of the original piece of data. In this case, the information is the date the customer bought something and the amount they spent. The customer could see the date and price, but if they clicked on the “view more details” button, they would only see the date and the price with no indication of how much they’d spent.
2. Identify What You Want to Know
One of the biggest issues we find when doing research is identifying what exactly it is we want to know. This can often be difficult to identify in advance, and the more specific you are in your research, the easier it will be to answer your question.
So, now you know what you want to learn from your data. How are you going to do that? Most people will try to figure out how to get the data by looking at a few different tools. For example, when you Google a question about your data, the first thing that shows up will be a bunch of different tools to help you. But there are a few things you should consider before diving in:
- Why do you want the data? Do you just want to see what’s going on? Or do you have a specific goal in mind?
- Do you need the data to do something with it? If so, what?
- How many data points do you need? How big is your dataset?
- How to Determine If Your Data is Masked
To mask data with Delphix, or at least make sure it’s not recognizable, you need to determine whether the data is in a format that’s easily readable. This is especially important if it’s going to be used for anything that will reveal the true meaning of the data, like if you plan on making a statistical analysis with the data.
A good way to understand whether your data is masked is to look at the date and time. Are they all the same? If the data appears in the same time slot each day, then it’s not masked. If they vary from day to day, you have a problem. In addition, if your dates are in a different format than those used in the original file, you have a problem.
3. Common Pitfalls
A common pitfall in data masking is the idea of “keeping things simple.” There are plenty of examples of successful data masking, but those cases can be very difficult to replicate. The trick to a successful data masking campaign is to build a convincing case for masking the data without it feeling forced, artificial, or like a marketing gimmick. The reason for masking the data is usually not to make it less convincing, but more persuasive.
We all like to think that we’re above it all and that our data isn’t being tracked. While that might be true for some, for many others, their data is definitely being tracked. What kind of data are you being tracked on? How often are your data being collected? Who is collecting it? Are there laws in place to protect your data, and what are they? Are you aware of these things?
- Masks allow you to manipulate data so that the statistical properties remain unchanged.
- Masks can also be used to improve data quality.
- Masks are typically used to make datasets more suitable for analysis.
- The use of masks can lead to improved visualization and exploration of large and complex datasets.
- When creating and using masks, we need to keep in mind the integrity of the data.
- There are three common masking types: numeric, factor, and categorical.
- Some of the most common masking strategies are a. Missing value masking b. Conditional value-mapping c. Multiple value-mapping
The best way to learn how to use masking data is to actually use it. As your business grows, you’ll find that there are more things you need to track and monitor to keep things running smoothly. For example, you’ll need to know when your best customers are spending the most time on your site, which pages they’re visiting, and what’s bringing them to your website. You’ll also want to understand when people are engaging with your content, and what they’re clicking on to get there. The good news is that you don’t have to be a programmer to be able to do all this. It’s easy to mask data with Google Analytics, Facebook pixel, and even your own tools. You just need to make sure that you set up the proper configuration and that you understand how to use it.
Get an overview of data masking, how it works, and when you need to use it.