Top 3 Benefits of Utilizing Data Masking

Top 3 Benefits of Utilizing Data Masking

In many ways, data is the lifeblood of a company. Through the use of customer data and internal information, a business can set themselves up for success and stand apart from the competition. However, that makes data a valuable thing that untrustworthy individuals want to gain access to. Read on to learn the top three benefits of utilizing data masking.

What Is Data Masking?

Data masking transforms information from sensitive information into a nonsensical, non-sensitive string of characters. For instance, “John Smith, 707 Main Street” may become “asdw g54Gep v35 54Yb tw90f.” This keeps the information safe even if it falls into the wrong hands.

The Customer Element

One benefit of using data masking is that it proves to your customers that you are trustworthy. Customers want to know that their private information will stay secure, and they’re willing to go out of their way to ensure that happens. If you don’t have credentials on your website that make potential customers feel safe, they will not want to do business with you.

The Compliance Element

You must meet certain legal requirements when working with sensitive information. Data masking and protection are not always something a company can utilize, but in many cases, they are legally required to maintain compliance.


Data masking isn’t only capable of protecting customer information—it also protects your company’s sensitive data. Bad actors can seek out a business’s information in the same way they may look for customer social security numbers and credit card numbers.

When you want the best data protection for your company, come to ChainSys. We’re here to answer any questions you may have and will set you up with top-notch data masking software.

Now that you know the top three benefits of utilizing data masking, you can keep your information and that of your customers out of the hands of bad actors. Even if they gain access to the information, data masking will ensure they won’t be able to understand or use it successfully.

6 Important Implementations of Data Science in Business

6 Important Implementations of Data Science in Business

Data science is the backbone of modern business. When you look at any of the most successful companies in the world today, you can see example after example of data science implemented to its fullest extent.

But what if you’re not a multi-billion-dollar company—can you still utilize the data science to get ahead? We’re happy to inform you that you can use data science and change the game. Read on to discover important implementations of data science in business.

What Is Data Science?

Data science is a lot like all other types of science—scientists create datasets to form hypotheses and test them through experimentation. In addition, scientists perform tests to confirm the quality of their data and put it into a cohesive structure for later analysis.

Data science uses more than scientific principles—it also uses math. Algorithms are essential in a data scientist’s toolkit to facilitate proper collection and analysis, especially with large datasets.

The best algorithms can notice patterns that human eyes wouldn’t usually see while also making data gathering and analyzing more efficient.

Data Science vs. Data Analytics

There are a lot of similarities and connections between data science and data analytics. In truth, you shouldn’t have one without the other. To make a long story short, data scientists learn new ways to model and interpret data using algorithms and predictions. On the other hand, analysts view datasets to find trends that help businesses make more calculated decisions.

1. Customer Insights

Customer insights can tell you nearly everything about the people buying your products or services. You can learn the demographics, habits, and even the goals of your patrons so that you can more effectively market to them in the future.

Perhaps your algorithms highlight that a person consistently visits your online store, puts an item in their cart, and leaves it. When your algorithms catch this, you might have your system send out an email to remind that customer about the item in their cart or even to offer a small incentive to come back.

When that tactic proves successful, you’ve learned something about your customer: they wanted the item the whole time, but something held them back from making the initial purchase. With a little more work in customer insights, perhaps you can determine what kept them from pulling the trigger so you can address that inadequacy for the future.

2. Produce Better Goods and Services

You may think your product is as good as it can get, but customer reviews often tell you otherwise. Of course, your data science platform may need to sift through quite a few reviews before it finds genuinely helpful suggestions, but there is no shame in adapting your products to suit your customers’ needs.

The companies that consistently improve their goods in a free market are often the most successful.

3. Effectively Manage Your Business

Data science is about more than customer-facing factors. You can also leverage data science to perfect your internal management structures. Just as your customers are rich with information about their spending habits, your business is rich with information about its processes.

With the right algorithms, your data scientists can sniff out patterns that indicate what is working and what needs work.

4. Predict the Future

While data science can’t actually predict the future, it can paint an accurate picture of the outcomes of certain decisions. Even without data science, businesses have spent years trying to perfect the art of prediction. When will markets crash, and when will they boom? Is the Internet bubble ever-expanding, or will it pop?

With data science, those predictions can rely more heavily on hard data than ever before. While gut feelings and intuition will always be a part of the business world, data science can hone instinct to drastically reduce risk.

5. Assess and Double-Check Your Decisions

Once you make a decision, the process isn’t over. It’s essential to assess your choices and track how the decisions affect your company’s performance. Even when a choice leads to a negative repercussion, it’s not all bad. You’ll gain valuable information, and your data science platform will have more evidence to understand what went wrong.

6. Detect Anomalies

Datasets are imperfect. When dealing with petabytes of information, it’s impossible to ensure that all of it is flawless. However, data science is excellent at scouring datasets to find anomalies and isolate them.

Fraud detection, for example, is a process made far easier through the implementation of data science. When fraudulent activity occurs, it’s essential to address the issue quickly—data science platforms can detect problems within minutes and alert you so you can take the proper steps.

How Data Science Works in Practice

To analyze the benefits of data science in a real-world environment, all you have to do is look at Walmart. While Walmart may have more pieces of data to work with than your company, what they do with the data may well be the same.

For instance, if you have a physical location, especially one with checkout lanes, you may be able to take a page out of Walmart’s book. Walmart uses its data analytics platform to keep track of the times of day when checkout lanes become overcrowded. This allows the platform to suggest staffing more cashiers during certain hours on certain days.

In this way, a simple staffing change can reduce traffic at the front of your store while also providing a better experience for your customers.

Additionally, Walmart uses data to keep track of the purchasing patterns of its store patrons. Perhaps pizza ingredients sell better on Saturdays, while pickles sell best on Wednesdays. This information can ensure enough items are in stock at the correct times so you don’t miss out on sales.

Now that you know these important implementations of data science in business and real-life examples take your company’s data game to the next level with your own data analytics platform. Training your people and getting the most out of the platform will take some time, but the investment is worth it.

4 Major Differences Between Data Backup and Archival

4 Major Differences Between Data Backup and Archival

When you hear people talk about data backups and archival data, you probably hear those terms used interchangeably. Even in tech-savvy companies, people tend to get backup and archival confused. However, these terms refer to entirely different processes, and it’s important to understand what each process brings to the table. Read on to learn the four major differences between data backup and archival.

What Is a Data Backup?

Essentially, a data backup can be thought of as a copy of your data. Most of the time, data backups are created on a set schedule or whenever your original data is modified. This allows you to constantly have the most up-to-date data in your backup, meaning you lose as little progress as possible in the event of a data disaster.

When a backup is made, you keep the original data and your latest backups—older backups are deleted to make room for the new ones. Plenty of devices can be backed up, from phones and computers to large servers. In addition, you can specify the data you want backed up. Some companies want everything in their backup, like the operating system, application files, and data, while others just want the data.

Many hackers and bad actors try holding data for ransom through a ransomware attack. Essentially, they lock your team out of their computers and threaten to delete your data unless you pay them. Companies without a good backup strategy are vulnerable to this type of attack, as all of their data would disappear if it were deleted from their main machines.

A business with a good backup strategy could feasibly ignore the hackers and allow them to delete the data, then update their security and restore their backups.

On top of that, backups are great for dealing with internal problems as well. If a file becomes corrupted or a large portion is deleted, you can recover an older version of that file and get back to work.

What Is a Data Archive?

A data archive, on the other hand, is a copy of data that you make for reference as well as long-term storage. Once the data is archived, you can either keep the data on the original machine or delete it, as a copy of the data is already safe in your archive.

If you’re not sure why you would want an archival copy of data, consider an advertising agency. A skincare company approaches this agency to develop an ad campaign for them—the agency collects information, puts together a campaign, and runs it. Everyone is happy with the outcome, and the skincare company feels as though they got what they needed out of the exchange.

The ad company puts all the information from the campaign into an archive and deletes it from their main computers to clear up space for their next client. A few years later, when the skincare company wants another campaign, they decide to return to the same agency as they have a good relationship.
Instead of spending weeks or months collecting information again, the ad agency can simply restore their archived information and get right back to work without missing a beat.

A data archive can also be used to store legal documents and other inter-office documents that are not regularly needed or to meet information retention requirements for a business or corporation.

Most of the time, data is archived on a last-used basis. If data has not been accessed within a given period of time, or there is data for a project that is no longer active, the information is archived and stored, just in case.

The Differences

Hopefully, the definitions for each term have clearly distinguished the major difference between archives and backups. However, there are a few additional differences that are important to understand.

Problems Solved

At their very core, data backups and data archives are processes designed to solve entirely different problems. While backups are utilized to keep a close copy of your data for quick recovery, archives are designed to protect your inactive data for long-term storage.
A backup allows you to return to normal operations with minimal downtime in the case of a security breach, and archives seek to retain all your company’s inactive data in a safe, cost-effective system.


Because of the nature of each process, backup and archival access are significantly different. With a backup, you want to have quick access to your data. Whether you need to restore information after a data breach or go back to a non-corrupted file, you want to be able to get that done as quickly as possible.
With an archive, however, you won’t even have to think about it. Naturally, a way to save on costs is with an archive system that is a bit slower than your backup.

Disaster Recovery

Disaster recovery and backups go hand in hand. The whole point of a backup is to allow your company to keep trucking through whatever disasters come your way, so full recovery of your system is made quick and easy.

Alternatively, archived data is a bit different. Depending on your archival system, you may find that the best solution is to purchase an identical archival system as a form of redundancy. While you have several backups from various points in time, your archive is one large pool of information. That means if you lose it, it’s very difficult to recover.

Which Is Right for You?

When considering which data management services to secure for your company, it’s natural to want to find a balance between cost savings and total protection. In practice, though, the truth is that most businesses need both backups and archives. They serve different purposes, and having one without the other will only solve half of your problems. With access to both short- and long-term data, you’re much better prepared for whatever happens.

Now that you know the four major differences between data backup and archival, make sure your company has a healthy dose of each. Without both processes working in tandem, you’re left vulnerable in the case of unauthorized access or a data disaster.

4 Benefits to Utilizing Data Analytics for Your Business

4 Benefits to Utilizing Data Analytics for Your Business

There are so many reasons to invest in data analytics. Whether you’re looking to improve productivity, cost savings, or decision making, data analytics can help. When you want to understand your customer better, choose data analytics. Read on to discover the four benefits of utilizing data analytics for your business.


There are so many day-to-day tasks that could be automated. Let’s consider just one department: human resources. There’s no need for people to manually push buttons to make payroll processing or candidate screening happen. Data processing can be significantly automated, leaving your workforce with more time to spend in other areas. AI doesn’t need to eliminate human jobs, but rather it can be a massive aid to the people doing those jobs.


Intuition is excellent, but intuition paired with statistics leads to more consistent results. Data analytics can go hand-in-hand with your employees to take decision making to a new level. Data is stellar at predicting future trends, so couple your workers’ minds with hard numbers, and you’ll see near-immediate results.

Predictive Modeling

Predictive modeling may feel futuristic, but it’s the same as playing out a scenario in your head. The only difference is the accuracy that data analytics can provide. Financial services can make great use of predictive modeling to quickly assess someone’s creditworthiness or pick out fraud risks. To say that data analytics tools are smart is an understatement!


In the modern era, customers expect a personalized experience. They want emails to address them by name and advertisements to cater to their unique sensibilities. Without data analytics backing your company’s personalization efforts, your customers will be left wanting. With modern analytics, you can easily determine niche interests and appeal to your customers in a way that’s beneficial for both parties. You make the sale, and your customers get the product or service they want!

Now that you know these four benefits to utilizing data analytics for your business, connect with Chain-Sys Corporation to determine which data analytics tools are right for you.

Differences Between ERP and Accounting Software

Differences Between ERP and Accounting Software

Whether you’re new to this field or you’ve been in it a while, it’s easy to confuse ERP and accounting software. While there is some overlap between enterprise resource planning (ERP) and accounting software, there are far more differences than similarities. Keep reading to discover the differences between ERP and accounting software.

What Is ERP?

The first key to understanding where accounting and ERP diverge is in learning just what enterprise resource planning is. At its simplest, ERP software is used to manage a variety of daily business activities. This includes everything from project management and compliance to risk operations and accounting. Eagle-eyed readers will notice the word “accounting” in that list—good catch!

You can think of ERP’s relationship with accounting software as that of four-sided shapes and squares: all squares are four-sided, but not all four-sided shapes are squares. ERP software can handle financial tasks and a lot more, while accounting software is only meant for the financials.

A Wall of Bricks vs. One Brick

In a wall made of bricks, every brick is important. Accounting is one of the bricks in your facility’s wall; if you remove that brick, your facility won’t be able to stand on its own. However, a wall is made up of many more bricks than one. With accounting software, you know you have one solid brick, but what about all the other factors that go into managing your facility?

It doesn’t make sense to have a unique piece of software to manage accounting, HR, purchasing, reporting, and everything else—that will just gum up the works. Instead, use ERP software and keep everything in the same place.

Industry-Specific Needs

Regular accounting software is great at managing financials, but it can’t meet industry-specific needs. You may want data masking tools or inventory tracking capabilities—these features don’t come standard with accounting software, but they do come with ERP software.

Now that you know the differences between ERP and accounting software, make sure you equip your facility with the one you actually need!

Differences Between Data Masking and Data Encryption

Differences Between Data Masking and Data Encryption

When you search for data security, you probably come across terms like “data masking” and “data encryption.” Lots of times, these words can sound like nothing more than synonyms. In this case, these terms refer to different processes, each with its own merits. Read on to learn more about the differences between data masking and data encryption.

Data Masking

People sometimes refer to data masking as “data de-identification,” and that term describes the protection process well. Instead of keeping sensitive parts of data on display, data masking replaces these chunks of data with random values. Therefore, masking hides identifiers and makes data useless to bad actors.

The three main types of data masking are static data masking, dynamic data masking, and on-the-fly data masking.

Static masking saves the masked version in your original database and sends a backup to a new location. Dynamic masking keeps all your data inside other systems of your development environment, giving you on-demand access. Finally, on-the-fly masking uses a process called extract, transform, load (ETL) to store masked data in the development environment.

Data Encryption

Like data masking, encryption also turns data unreadable with algorithms. However, you can think of encryption as a code. If you have the key to the code, then you can read the data it hides. If bad actors figure out the key with enough force, they can also read the code. Decrypting data makes it vulnerable, so the best use for encryption is for data that doesn’t need to be functional, such as data in storage.

How They Differ

Should you choose encryption or masking when you’re looking for processes to help with data breach prevention? The best data security strategies employ both processes for different reasons. You should secure data that you and your team are actively using with masking, while it’s best to protect data in storage with encryption.

Now that you know the differences between data masking and data encryption, you should contact ChainSys for more information on data security.