As a business grows, it starts to collect more and more data. Even if a business isn’t specifically trying to collect data, it’ll end up with more than it expected just because it’s expanding its operations and reaching a broader audience.
This means that growing businesses have to invest in data storage solutions even if they don’t plan on collecting data in a large-scale way. This is just good business; even organizations without broad data strategies can still use data for insight.
To compound this, customers and clients are creating more data points than they ever have before. Even if a business is holding steady and they’re not aiming to collect more data, they’ll end up getting more data than they anticipated. As more and more operations take place online, the kinds of data that businesses can collect will grow.
Not only does this mean businesses collect more data about their customers and their operations; it also means they collect data in different formats. Businesses don’t always end up collecting structured data; they also collect unstructured data like pictures and videos.
That means businesses have to figure out storage solutions for large amounts of data, and sometimes that data isn’t structured. Considering every business has their own priorities, it can be very difficult to actually find a solution that works.
Businesses often can’t even figure out what type of solution they should implement. There are dozens of different strategies for storing business data in a large-scale way, and businesses need to learn which strategies are best for which situations.
Some solutions are more standardized and structured, while other tools offer a looser, more decentralized approach to storing business data.
The three most common data storage techniques are cloud databases, data warehouses, and data lakes. Most businesses will use one of these three techniques for managing their business data.
Businesses without a data strategy need to know what each of these strategies are, how they differ from one another, and which use cases they meet most effectively.
The first tool that businesses often use to store their data is a basic cloud database. There are cloud databases designed for personal use, which often have smaller data storage limits and databases that are specifically geared toward businesses.
To understand cloud databases and other modern data storage solutions, businesses need to learn what exactly makes a tool cloud-based. Most data storage options nowadays are cloud-based, with even more organized and secured tools.
The ‘cloud’ is a collection of remote servers owned by third parties. Customers can upload their data to these remote servers instead of storing it on servers they own. Using the Internet, they can access that data from anywhere.
Cloud databases are some of the simplest kinds of storage. They’re generally designed with a small scale in mind, and can handle common data formats in their original states.
This means that if you upload an Excel file to a basic cloud database, it’ll stay an Excel file. It won’t be transformed into another format or be expressed in another programming language.
In terms of structure, they’re closer to an actual computer or server than they are to other data storage formats. Users upload their data to their cloud database, and then it’s stored in its original format until someone else needs it again.
Cloud databases can be useful for smaller businesses, or ones that need to store structured and unstructured data in the same place, but basic ones often lack the scale necessary to effectively manage data in a centralized way.
In addition, they require additional work to be useful for BI applications. Data is stored in its original format, which means if it needs to be transferred into a BI tool, it needs to be reformatted.
Basic cloud warehouses work best in conjunction with other strategies. They’re useful as small-scale, local, self-managed options that different teams and departments can use to manage their data and then send it on to a more centralized tool once it’s ready for use.
Data warehouses are a common data storage approach for mid-sized businesses that are starting to manage data in a more agile way. They’re great for storing structured data that can be immediately passed to a BI tool for insight.
A data warehouse doesn’t store information in its original format, like a basic cloud database does. Instead, data is translated into a structured format, one that the data warehouse can store in a native way.
To understand what sets a data warehouse apart from other data storage solutions, businesses need to understand the difference between structured, semi-structured, and unstructured data.
Structured data is stored in a clearly organized, highly rigid format within a structured database like a data warehouse. Its high level of organization makes it the best choice for data analysis, but it can’t be brought out of its storage tool without losing some of its organizational effectiveness.
When structured data is brought out of its original tool and exported in a file format, it becomes semi-structured data. Semi-structured data still has a level of organization, but there’s no way to organize between files.
Often, semi-structured data has to be uploaded into a data warehouse and turned back into structured data. Businesses can still use it for analysis, but they can’t do that unless it’s re-structured within a structured database.
Lastly, unstructured data has no organizational structure at all. Usually, unstructured data means things like text, video, audio, and other files that don’t contain any sort of numerical data.
Businesses can’t analyze unstructured data unless they use cutting-edge analytical techniques. Things like machine learning and neural networks can help to analyze unstructured data, but businesses need to specifically look for tools that offer these features.
The key thing with a data warehouse is that it can only store structured data. Any data that a business stores in its warehouse will be transformed into the data warehouse’s storage format, losing its original file format.
In addition, a data warehouse can’t store unstructured data at all. If a business wants to collect and store large amounts of unstructured data, they’ll need to find another solution.
Second, data warehouses have the scale that larger businesses need to store their data. Businesses can store massive amounts of data in their warehouses, and because these warehouses are cloud-based, businesses can buy more space if they need it.
Lastly, many BI tools offer data warehousing tools and capabilities. This way, a business can store all of their data in the same tool that they’ll use for data analysis and visualization.
Some of the largest enterprises use a data storage strategy called a data lake. Data lakes have minimal structure and don’t usually contain actionable data, but their scale makes them a valuable tool for the largest organizations.
A data lake is similar in concept to a basic cloud database, but it differs in terms of scale and connectivity. Just like a basic cloud database, a data lake stores information in an unstructured way, using original file formats.
However, a data lake operates at a much larger scale, and like a data warehouse, it acts as a central data repository. It can also connect to data sources and collect their data automatically, a feature that’s not common among more basic databases.
Some data lakes even offer structured data storage options, so that businesses can store their structured data meant for analytics in the same place as the rest of their data.
Data lakes are most useful for businesses that need to store a large amount of unstructured data. A ‘large amount’ of unstructured data in this case doesn’t mean a few terabytes of data; businesses often need to store hundreds of petabytes.
Businesses’ data demands are also constantly growing. A business may already have hundreds of petabytes of data stored and are adding hundreds of terabytes to that every day.
For businesses working with data at this scale and who need to use unstructured data for advanced techniques like machine learning, data lakes are generally the best option. For smaller businesses, though, other data storage solutions are more effective.
Putting it all together
Businesses often use multiple different techniques to build their data infrastructure. There aren’t really any one-size-fits-all solutions, and businesses are better off designing their own data storage using these strategies as building blocks.
For instance, many businesses use a data warehouse in conjunction with more basic databases. They use databases for initial data collection and storage, and then send it over to the data warehouse when it’s ready for analysis.
In other cases, a business may use an unstructured database to supplement their data warehouse. They use their data warehouse to store all of their structured business data and then use a database for storing their pictures, videos, and text files.
Other businesses take the opposite approach. They use a data lake as their primary store of information but also use a data warehouse to manage their structured data in a more organized way.
Regardless of the strategy that your business uses, the first step is to get started. For businesses that already use one, a BI tool is an excellent place to begin. With their data warehousing tools, they can help you identify your data needs without investing in another tool.
Many businesses find that their BI tool is perfectly suited to acting as their primary data storage solution. Other businesses may find they need data storage at a larger scale, or a tool that can handle unstructured data in a more effective way.
If your business doesn’t already have a BI tool, then there’s no point in managing your data in the first place. A BI tool is essential for putting your stored data to use. With data analytics and visualizations, you can easily find insight.
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