Every industry needs to process data. But the kind of data, its scope, and its use depends.

Analytics helps an organization make sense of their data in order to improve their performance and operations. Chris Savage, the CEO of Wistia says it best “As you gain fresh insight from your data, it opens the door to new questions. As you have new questions, you need instrumentation and analysis. Saying the process is done is saying you understand everything there is to know about your users, products, and channels.”

To get to the point of self analysis and asking the right questions, an organization must use the best data analytics system for the best outcome. At Zuar, we provide data strategy and staging services to make your business smarter. Start optimizing your business by learning about the four common types of data.

Find out more about Zuar’s services for meaningful data insight here.


A database is a structured assortment of related data. It is processed, organized, managed and updated, then stored electronically. It’s a popular method used by organizations to store information that needs to be retrieved frequently.

Main characteristics of a database

  • Organized according to company operations and applications
  • Highly structured
  • Fast retrieval and understandable system
  • OLTP (online transaction processing) application
  • Data recording capabilities

Data Warehouse

A data warehouse is the core analytics system of an organization. This system retrieves data and information from various sources within the organization, then stores and manages them. Business decisions using data reports and analysis typically build upon and assess data from the data warehouse. Like a database, it usually uses SQL to query the data, and it uses tables, indexes, keys, views, and data types to organize. The main difference between a data warehouse and a database is that it integrates copies of transaction data from multiple sources and is more immediately available for analysis.

Main characteristics of a data warehouse

  • Stores large quantities of historical data so old data is not erased when new data is updated
  • Allows complex data retrieval processes
  • Organized by subject
  • OLAP (online analytical processing) application
  • Data analysis tool

Data Mart

A data mart is a preferred method when working with departmental data because a data mart is a repository for summarized data derived from the data warehouse. The data mart offers subject-oriented data that benefits a specific set of people within the organization. For example, the company executives or the sales team might use a data mart for marketing analysis. A data mart is smaller in scope, focusing on a single area.

Main characteristics of a data mart

  • Focuses on one subject matter
  • Dedicated to only one business function
  • Only stores one subset of data
  • Often uses a star schema or similar structure

Data Lake

A data lake stores an organization’s raw and processed data at both large and small scales. Different users in the organization can dive in and retrieve the relevant data for their department to use. Data lakes and data warehouses are similar in bandwidth, both possessing the ability to store large amounts of data, but the key difference is that data lakes store raw data while warehouses store processed data. Data lakes are more flexible but less secure, and they often need data scientists to understand them.

Main characteristics of a data lake

  • Collects all data from various sources over an extended period of time
  • Meets the needs of various users in the organization
  • Is uploaded without an established methodology

Uses of databases

Industries that use databases need to have a highly efficient system of data retrieval for smooth operations. They include


The airline database generates important reports like the flight manifest, and it’s also used for scheduling flights and creating passengers reservations.


From their database, a telecommunication company generates customer bills, call logs, balances for pre-paid customers among other crucial operational information.


The sales department of any organization is perhaps the biggest beneficiary of the company’s database. The system enables them to track sales, customer information and product performance.


The banking sector relies heavily on databases to process their transactions and maintain up-to-date customer information and details. A properly updated database is also crucial to accuracy in serving customers.

Uses of a data warehouse

Data warehousing applies to industries that have a large volume of data to processes frequently. They include healthcare and insurance, as well as finance, government, education, services, and manufacturing. With heightened security, this is the way to go.


The healthcare sector has a lot of information being inputted on a daily basis from stakeholders to suppliers and of course, patients. This data is organized and stored in the warehouse, and can later be accessed to create treatment plans, strategize on purchases and processes and even predict epidemics in advance.


Insurance is another sector that sees a huge, continuous flow of data. Using a data warehouse allows the industry stakeholders to have current information on customer patterns and create a quick analysis of market trends. Because insurance is always changing, a quick way to share data is crucial to keep up with the industry changes.

Uses of data lakes

A data lake is an excellent, complementary tool to a data warehouse because it provides more query options. A data warehouse will provide structured and organized information. However, with the addition of a data lake the organization can tap into raw data that may offer even more insight or support because data lakes provide real-time analytics.

Research and Science

Science is ever evolving and it relies on real time data to make crucial deductions. Fata lakes are suitable for scientific use because not only is the data raw from feedback sources and algorithms, it’s also real time. Science is only as good as its most current and relevant deductions. Research needs to be fresh to have an impact on the reports or findings that it produces.


IT architects can access data from the data lake in its most original form and scale it up or down depending on their needs. By using raw data, the organization is able to create more accurate products that cater better to customer needs.

Uses of Data marts


Data marts are mainly used internally for department based information. Since it’s condensed and summarized, data mart information derived from the wider data warehouse allows each department to access more focused data to its operations.

What Makes the Best Data Management System?


The organization must ensure that the method they use is designed to work in their favor from the initial process of gathering useful data to implementation of the information. For an excellent data management system, select the most logical structure that supports the organization’s needs. Also determine the purpose of the system. Is it for internal, departmental data sharing or for real-time analytics of information from customers and other feedback sources to use on a larger scale?

Credible sources

Finding sources that provide credible data is crucial to having reliable data analysis. The best place to start gathering information is from already existing sources affiliated to the organization. For example, customer information, details, and trends from already existing clients form a realistic starting point to build on.

Once the sources are in place, the next step is determining the types of reports the organization would like to generate and their importance to their processes. This means having questions that data analytics should answer like how many sales per month, what are popular customer trends, or what are the emerging customer trends? These questions make the data management system a useful tool for the organization's operations.

A refined system

Always strive to store data in its smallest logical form. Regardless of the data management system an organization employs, smaller bits of information are easier for users to assimilate and use compared to larger more complex data.

As the organization grows and uses multiple data management system simultaneously or even one with devolved levels like a data warehouse with data marts or data lakes, they can refine their method of presenting the data to be more efficient. An organization can use lists, graphs or charts according to what best captures the information they need.

How to Choose the Right Data Management System

Data center

Databases, data warehouses and data marts have been around for longer than data lakes. However, the data lake trend is catching on as more and more industries have come to rely on real-time data analysis. The following are factors to consider when choosing a data management system.

Related: Zuar Data Strategy

Data model

The popular data model for a long time has been relational, meaning it's table-based. But recently, NoSQL models that use graphs or key values among other things have gained a strong following. The organization has to determine whether they will benefit from a data structure that uses the relational model or an unstructured data model. Relational models may be more convenient to use, but there is room for NoSQL models as more people embrace the change they bring.

Get started with Zuar Data Staging for data integration, pipelines, framework, and models.

Operational complexity

The more complex the operation, the safer it is to use a structured data management system like a database over a data lake. Databases are easily more scalable even when an organization continually grows compared to data lakes where finding crucial information can be like trying to find a needle in a haystack.

Having said that, data lakes are excellent for organizations or industries that thrive off unstructured data and have a long view to their information. Also, consider how many divisions in the organization will be served by the same data.

Real-time versus recorded

Data management systems are designed to be either reporting or analytical tools. That's why data lakes are popular for their real-time aspect. It allows users to access feedback and algorithms as they come in. On the other hand, databases are recording systems, so they rely on past transactions or information to form deductions.

It’s imperative that an organization evaluate which approach is best suited to their needs. Each is valuable in its own unique way, but it may depend on the industry.

Data consistency

Having a lot of data coming in on a consistent basis determines the system an organization should adopt. A data lake can take both raw and processed information and store vast amounts of it while a database can only work with highly organized refined data in lower quantities. Choose a system that can accommodate the type and amount of information the organization is or foresees receiving.

Data protection

Different data management systems offer varied data protection which is essential for data protection. The method of data protection is dependent on the structure of the data management system. The more unstructured the system, the more vulnerable it is. The more structured it is, the more secure it may be. To ensure that the system is secure an organization can use encryption to keep personal data locked away from intruders like hackers.

Best Data Management Practices

Ensure quality data

When an organization focuses on quality sources they’ll end up with quality data and actionable information. One way to ensure the data received is quality is to limit sources and check older data for reliability or new updated information that changes things. Also, eliminate duplication of data from leads by asking a broader array of questions.

Make data accessible

The more accessible the data, the better the actionable steps a team can take to utilize it. Of course, the data should have proper security protocol to prevent it from being seen by unauthorized people. Having said that, limiting data too much can interfere with the ability of the teams using the information to perform.

Set up logins and passwords that are specific to personnel using the data with management and company executives having more access than mid-tier to low-tier employees.

Have a data recovery strategy

A data recovery strategy is crucial, especially in this age of hackers. Losing all data can cripple an organization—if not in the long term, at least in the short term. Tactics like exporting data or saving to a cloud service come in handy. Also, creating backups ensures that the organization can restore everything back in case of a full-on deletion of all company data.

Invest in data management software

This is not only a good idea, but a crucial step in maintaining a healthy data management system. Ultimately, choose software that the team can easily use and understand. A good software makes the lives of those using it easier and the processes faster. It should also offer security so that the company data is not accessible to anyone who is not authorized.

Data warehouse versus Databases

The main difference between these two include:

  • Data warehouses store summarized data while databases utilize detailed data.
  • Databases are used for simple transactions unlike data warehouses, which are applied on complex transactions.
  • Databases use current information but the warehouses use both historical and current information.
  • Databases use information from one main source while data warehouses leverage information from various sources.
  • Data warehouse provides insight into the company’s overall business operations while databases are used for day to day fundamental operations.

Investing either a database, data lake, data warehouse or data mart ultimately says one thing about an organization. They care about acquiring and utilizing data responsibly and what it means for their business. Without data, there is no way to scale up successfully. Get started with Zuar to find a business intelligence solution no matter the size of your company. From data marts to data lakes, we’ve got you covered.

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