3 Signs Your Company Needs a Data Analytics Solution

3 Signs Your Company Needs a Data Analytics Solution

Data is a simple fact of running a business in this day and age. However, you must understand that data is more than facts. What good are a million customer insights if you can’t convert numbers into actionable information? We have the answers! Read on to discover three signs your company needs a data analytics solution.

Tons of Meaningless Data

A dataset consisting of tens of thousands of data points is potentially very useful—and potentially very useless. A pile of numbers is meaningless unless you have a way to parse the data and glean helpful insights into your customers’ habits.

If you have more numbers than you know what to do with, data analytics can help guide you to understanding. This is the single largest benefit to data analytics—it’s a way to make sense of the proofs and figures arranged in columns before you.

Reports Come Too Slowly

Many businesses rely on up-to-the-minute information to stay agile in a competitive space. When you learn about a new trend that could benefit your company, it makes sense to pounce right away so you can capitalize.

Unfortunately, when that information arrives on your desk too late, there’s not much you can do. Data analytics ensures you have the reports you need when you need them.

Old Spreadsheets Aren’t Cutting It

When you scour through the old spreadsheets in your data catalog, you may find them sufficient. However, where Excel once served you well (when you had far fewer fields to fill), you now find that populating an Excel spreadsheet is time-consuming and far from flawless. Data analytics provides an alternative without the possibility of an invalidated report due to a single incorrect formula.

Now that you know these three signs your company needs a data analytics solution, come up with your own solution and start benefitting from new insights. It won’t take long to see results, so set the wheels in motion and enjoy the results!

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.

Access

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.

Automation

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.

Decision-Making

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!

Personalization

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 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.