The 5 Key Types of Data Analytics Every Business Should Know

The 5 Key Types of Data Analytics Every Business Should Know

In this brave new world of big data, it can feel like a challenge to stay on top of all the insights you’re told you need. We’re here to make things a little simpler by breaking down different types of data analytics that your teams will come across. Read on to learn the five key types of data analytics every business should know.


The simplest way to think of descriptive analytics is as statistics. Before you can start deciphering the meaning of your data, you need to collect information. The information you collect will differ based on your goals. For example, if you want to know about sales trends, you might collect sales information from a particular item to learn that it sells far better in summer than in winter.


Diagnostic analytics is all about taking a closer look at your data to find the root causes of things. With diagnostic analytics, you observe data points one by one until you can determine whether that individual piece of data has the effect you’re searching for.


Predictive analytics is self-explanatory—you’re looking for a forecast of things to come. Based on information from the past, what trends will be hot in the coming season or year? Predictive analytics alone can be powerful, but you’ll see the best results when you couple it with prescriptive analytics.


Prescriptive analytics takes predictions and tests out all the options before suggesting the best course of action. Salesforce data management and prescriptive analytics can help you avoid pitfalls while staying on the optimal route to your final destination.


Augmented analytics takes the best aspects of artificial intelligence and machine learning to provide automated data preparation as well as insight discovery. It takes predictive and prescriptive analytics, combines them, and gives you your answers in the snap of your fingers.

Now that you understand these five key types of data analytics every business should know, we hope you have the tools you need to take your business to the next level.

How to Use Data to Positively Impact Your Business

How to Use Data to Positively Impact Your Business

Data grows more and more each year, and it becomes harder and harder to avoid making data the linchpin of every business operation. As the saying goes, “If you can’t beat ’em, join ’em.” Using data for the benefit of your business isn’t just smart—it’s increasingly necessary to stay competitive in the modern era. Read on to learn more about how to use data to positively impact your business.

What Does It Mean To Be a Data-Driven Business?

When you are a data-driven business, it doesn’t mean that every choice you make is a simple calculation. Instead, driving your company with data means that you collect insights to make your intuition even better. Data can help you in several areas of your business.

While most of your data is great understanding for the customer experience and driving sales, other pieces of information can help you to improve your operations and record keeping.

The last thing you want in a thriving business is to put speculation and conjecture on pedestals. Your gut can make suggestions, but cross-reference what your gut tells you with what the numbers tell you. When they align, you know you have something real.

The Value of Data

Ten thousand numbers are meaningless unless you can use them properly. Your data is similarly meaningless unless you utilize proper data analytics tools. It’s through effective analytics that you can acquire the business insights you’re after. Once you have a crack team of data analysts, improving your processes and solving business problems will be easy.

The Risk Factor

You can’t (and, frankly, shouldn’t want to) run a business without any risk. The most successful businesses in the world rely on smart people making judgment calls—and the free market decides if your judgment call was right or wrong.

Even though you can’t eliminate risks in the business work, your choices don’t need to be shots in the dark. That’s where data comes in. When data is the driving force behind your decisions, you prevent yourself from being vulnerable to risky moves gone wrong.

Think about it like this: when you take on a new client, you don’t start your processes from scratch. Instead, you build on everything you’ve learned from past clients. You do your best to repeat the successes and avoid the failures. Starting from scratch would leave you open to all the pitfalls you experienced when just starting out.

Data-driven decisions manage risk in the same way. While no choice will ever be 100 percent risk-free, data can help you see what strategies will likely work well and which present more of a challenge.

What Business Decisions Should You Use Data For?

We’ve talked in general terms up to this point, but we know you’re after the specifics. After all, it’s important to understand what processes and decisions data is good for and those it cannot help you with.

In truth, it all depends on your organization. The right data can tell you just about anything, as long as you have the data. For instance, data can help you with business finances. It doesn’t take long for analysts to determine cost-effective hiring procedures or ways to market new services without blowing through your advertising budget.

In addition, you can use data to help improve customer loyalty and drive sales, promoting growth. Data can even help you improve customer service, touching on everything from upgrading support ticket processes to improving response times.

Many companies also fail to consider the importance of proper inventory management. When you have too much inventory on hand, you’ll spend more than is necessary to house it. When you have too little, you run the risk of customers finding an alternative when they see the “back in stock soon” message on your website.

Data analytics can support your inventory management systems to optimize your storage and stock alerts. Data can also help to speed up the process of finding inventory if your company offers a plethora of products.

Anticipation and Being Proactive

It’s no secret that the best businesses seem to be one step ahead of the competition. They aren’t contacting psychics, so what are they doing? They’re leveraging a combination of intuition and data to anticipate trends.

In addition to anticipating trends, you can also use data to anticipate the desires of your customer base. This way, you and your customers benefit. They get the products they need just as they need them, and you see more sales or conversions.

Customer Relevance

One of the worst things a company can do is to put out a product that is irrelevant to its demographic. You put in the time to develop and advertise a product or service, only for your customers to determine that it isn’t for them. Suddenly, your next shareholder meeting has you sweating instead of jumping for joy.

You can use data and impressions to check in with your customers—are the projects in R&D going to be well-received? In this way, data can help you avoid failures while tweaking products to ensure your customers will show up with credit cards in hand.

A Personalized Touch

Customers expect personalization nowadays. Interactions with customers no longer stick to the same strictures as 10 years ago—you can tailor messages to specific demographics without spending a fortune on advertising. All you need is data analytics.

Data can help you advertise based on location, habits, and even current mood—and every factor is crucial to driving sales.

Unparalleled Customer Experience

Brand loyalty is one of the biggest things to covet. In a world where immediate gratification is nearly always the top priority, setting yourself apart from the competitors as a business your customers want to return to repeatedly can keep your doors open for decades to come.

To offer that experience, you need to leverage data to produce an excellent user interface, great customer service, and highly efficient processes. Your customers have high expectations—see that your products and services meet them with a top-of-the-line data management platform.

Now that you know how to use data to positively impact your business, work with your analytics team to gather relevant insights so that you can make calculated moves.

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.