/ 4 ways to tell if your data is big data

4 ways to tell if your data is big data

As businesses do more things digitally, they have the opportunity to collect far more data than they could before. The largest businesses might collect billions or trillions of data points over the course of a day. A smaller business might not collect that much data at once, but they often deal with datasets with hundreds of thousands, or even millions, of rows.

 

Data can be overwhelming

At a certain point, datasets start to overwhelm more basic data storage solutions like spreadsheets and personal cloud storage tools. At that point, these datasets are called “big data” sets.

There’s no clear definition on what makes a dataset a big dataset. This is important, because based on the scale of their dataset, a business has to choose between a less powerful BI tool for smaller datasets, or a fully-featured big data solution like Domo.

Businesses need some way to know whether their data is big data, so that they can find the tool that’ll work best for them. All too often, businesses get a less powerful tool than their data requires, and get stuck with a tool that can’t do what they need it to.

 
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Characteristics of big data

What makes data “big data?” There are four main ways of knowing whether your data is big data and requires a big data solution.

1. Volume

The most basic way to tell if data is big data is through how many unique entries the data has. Usually, a big dataset will have at least a million rows. A dataset might have less rows than this and still be considered big, but most have far more.

Datasets with a large number of entries have their own complications. A weaker data system might have limits on how much data it can display or analyze at once. These datasets are generally pretty hard to upload as well. A less powerful BI tool might take hours or even days to pull it in.

Usually, businesses need to analyze the whole dataset at once. They can’t just look at portions of the dataset, so they need a tool that will allow them to inspect everything at once. Smaller tools can only give them a snapshot, or take so long to analyze a dataset that it’s not feasible to use it.

With a large-scale big data solutions, businesses can properly analyze datasets of this size. If your business is struggling to deal with datasets with too many entries, consider implementing a BI tool in your organization.

2. Velocity

A dataset doesn’t necessarily need to have billions of entries to be a big dataset. There are more elements beyond scale that determine what a big dataset is.

A dataset that gets appended constantly or needs to be accessed constantly can also be a big dataset. The closer that a dataset is to updating in real time, the more likely it is to be a big dataset.

When a dataset is moving fast enough, it doesn’t matter if it only has a couple of entries per row. Even if there are only three or four entries per row, if the dataset gets appended every 15 minutes, it doesn’t take very long for the set to become so large it’s unwieldy.

Some BI tools don’t even have the functionality to update data in close to real time. Smaller tools might limit update speeds to every hour or every six hours. Often, that’s not fast enough.

If the dataset is updated with new information every 15 minutes, but your BI tool can only update it once every hour, that means you’re using outdated data for 45 minutes out of every hour. Businesses that have high-velocity data need tools that can handle that data.

3. Variety

Often, businesses end up collecting data that’s more complicated to store than integers and simple text strings. They may end up collecting pictures, or video, or audio files, or text documents like PDFs, or any number of different file formats.

Collectively, data like this is known as ‘unstructured’ data. Unlike spreadsheets or data stored in an SQL database, this data can’t be easily parsed by conventional data management tools.

Storing unstructured data comes with its own problems. While it’s easier to store in a general sense, since a business doesn’t have to fit it in with a database’s storage criteria, it’s much harder to store with traditional data management solutions.

When businesses start to look for storage solutions for datasets with a wide variety of files, they often find that their options are limited. On-premise storage brings all sorts of problems, but personal cloud storage rarely offers enough space to store everything that a business wants to store.

Businesses that need to store complex data types need a data storage solution that can handle those data types. Often, this means they need an enterprise-scale data warehouse like Snowflake or AWS. These tools can store large amounts of data in a wide range of types.

Not every BI tool can properly connect with a big data storage solution, though. When a business’s storage system becomes more complicated, many BI tools can’t keep up. They’ll need a BI tool that’s designed to handle big data and can properly communicate with the cloud data warehouse.

4. Complexity

Big data doesn’t necessarily need a lot of rows, or need to move fast, or have lots of unstructured data. It may have any or all of those things, but the thing that really defines big data is the complexity required to transform it.

The unifying quality of all big datasets is that they’re all hard to deal with using basic data analysis solutions. As a business collects more data and starts to scale their operations, they’ll start to press at the limits of a smaller tool until the smaller tool becomes entirely unusable.

For many businesses, the first sign they have that they need a big data solution is when their data overwhelms their small data solutions. For businesses in this situation, they need a BI tool

Businesses that can’t handle their current data demands with their current data solutions need to upgrade to better solutions, whether or not their data is technically ‘big’ or not. Big data solutions are the best choice for businesses that need a powerful data solution and expect to keep growing and collecting even more data.

 

 

Conclusion

Businesses often collect more data than they can reasonably handle. When a business’s data requirements start to reach the limits of their current BI solution, it’s time to switch.

Modern BI software is the best choice for businesses looking to handle big data. Its analytical tools are the best in the industry for processing datasets with even tens of millions of entries. Its tools are intuitive, and its connectors are simple to implement. No matter the size of your business, consider implementing a BI tool to help with your big data needs!

Check out some related resources:

Modern BI for All Field Guide: Operationalizing Modern BI Best Practices and Principles

How a UnitedHealth Group technology unit built better customer experiences

10 Ways to Turn Data into Actionable Insights

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