Every industry needs to process data. But the kind of data, its scope, and its use will illustrate if a data mart, data warehouse, database, or a data lake will be best solution for your enterprise.
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. But which is better for your industry? Is it more advantageous to use a data mart vs. data warehouse? Or would it be better to utilize a data mart vs. data lake?
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What is a Database?
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
What is a 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 vs. 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
What is a 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. An enterprise would want to leverage a data mart vs. a data warehouse. Primarily because 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
What is a 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.
While similar in bandwidth and both possessing the ability to store large amounts of data, a data lake vs. a data warehouse differentiate in the types of data they store. 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
Data Warehouse vs. 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.
Data Mart vs. Data Warehouse
The key differences between a data mart vs. a data warehouse include:
- Data marts are smaller subsets of data from a data warehouse.
- Data marts are a repository of essential data for a specific subgroup or use case where access can be restricted to that subgroup or use case. Only a few users have access to the entire data warehouse.
- Data marts are less expensive and can analyze data faster because they are smaller subsets of the data warehouse that is slower and overloaded.
- A data warehouse is significantly larger, generally a terabyte or more in size, where a data mart is usually less than 100 GB.
- Data warehouses contain all the filtered data for an entire enterprise and across multiple categories and organizations where a data mart has a limited range focused on one line of business.
- Multiple sources store data in a data warehouse, whereas only a few sources contribute data to a data mart.
Data Lake vs. Data Mart
The key differences between a data lake vs. a data mart include:
- Data lakes contain all the raw, unfiltered data from an enterprise where a data mart is a small subset of filtered, structured essential data for a department or function.
- Data marts are very specific, allowing for fast, effective analytics of relevant summarized information. Data lakes are better for broader, deep analysis of raw data.
- Data lakes are more flexible and the data is stored for an indefinite time where a data mart is restricted and exists for shorter time frames.
- Data lakes have a central archive where data marts can be store in different user areas.
Data Warehouse vs. Data Lake
The key differences between a data warehouse vs. a data lake include:
- A data lake stores all the data for the organization. A data warehouse will only store essential data for creating structured data models and reporting.
- Data lakes store the data forever so that enterprises can pull the data from any point in time for analysis.
- Data lakes utilize different hardware that allows for cost-effective terabyte and petabyte storage.
- Data warehouse extract data using quantitative metrics from transactional systems. A data lake will extract data from all data types, including non-traditional data types like web server logs, social network activity, sensor data, etc.
- Data warehouses are for operational users that need to generate reports for analytics. A data lake is for deep analysis that goes beyond the stored data of a data warehouse.
- Because data lakes store raw data the can be accessed and search before it has been cleansed or structured, user can get results faster.
How Do Industries Utilize Databases?
Industries that use databases need to have a highly efficient system of data retrieval for smooth operations. As technology and ecommerce expands, databases are a ubiquitous data processing tool for most industries. But these industries, in particular, rely heavily on databases:
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.
How Do Industries Leverage Data Warehouses?
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, data sensitive industries prefer data warehouses vs. databases.
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.
How Do Industries Utilize 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. A data mart vs. data lake creates two sides of the spectrum, where data marts are focused data and data lakes are huge
repositories of raw data.
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.
How Departments within an Enterprise Leverage 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.
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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?
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
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.
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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.
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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. This compared to data lakes, where finding crucial information can feel like trying to find a needle in a haystack.
Having said that, data lakes are excellent for organizations/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. If we compare a data lake vs. database, they each use different processing strategies. 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.
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.
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 high quality data 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 isn't just 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. Good software makes the lives of users easier, and processes faster. It should also offer sufficient security so the company's data is not accessible to anyone who is not authorized to access it.
Investing in either a database, data lake, data warehouse or data mart ultimately says something 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 the right business intelligence solution regardless of the size of your company. From marts, to lakes, to warehouses- we've got you covered!
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