It can be hard to imagine a time before big data was a regular part of doing business.
But there was a time, not so long ago, when businesses didn’t have the ability to collect and analyze mountains of data. And while big data has brought with it a whole host of advantages, it has also created some new challenges—chief among them, dark data.
The data you use each and every day has various levels of importance. At the top is your most valuable data—the data that you use to make decisions, drive revenue, and improve operations.
Then there’s the data you don’t use very often, but you still need to keep around. This is often referred to as “dark data.”
So what exactly is dark data? Dark data is the data that organizations collect and store but never actually use. It takes up space and costs money to keep around.
While it may not be used daily, dark data still has value. In some cases, accessing data that you may not use very often can be helpful. The challenge is knowing how to manage this data and ensure that it is secure.
How does dark data play into big data?
To understand dark data, it’s helpful to first understand big data.
Big data is a term used to describe the large volume of structured and unstructured data that organizations collect and store. It’s the stuff that organizations use to make decisions, improve operations, and drive revenue.
Big data is collected from various sources, such as social media, website analytics, IoT devices, and more, and stored in data warehouses. Once it’s collected and stored, organizations use data analysis tools to mine this data for insights. If this data is not used for analysis it becomes dark data.
There are a few reasons why an organization might have dark data:
The data isn’t relevant anymore: Over time, an organization’s data can become outdated or irrelevant. This is especially common in industries where things move quickly, such as technology or financial services.
The data is no longer needed: In some cases, an organization may collect data for a specific project or initiative, but once that project is over, the data is no longer needed.
The data is too difficult to analyze: In other cases, the data may be too difficult or time-consuming to analyze. This often happens when data is unstructured, such as social media data or website logs.
The pros and cons of dark data
While dark data might not be used on a regular basis, it can still be helpful for businesses. In some cases, it can provide valuable insights or help organizations make better decisions.
In some cases, dark data can be used to improve decision-making. For example, an organization that is considering a new product or service can use dark data to understand customer behavior and preferences.
Dark data can also be used to plan for the future. For example, if an organization knows that a certain customer segment is likely to churn, it can use this information to develop strategies to retain these customers.
Dark data can also be used to understand trends. For example, if an organization notices that a certain type of customer is increasingly likely to purchase a certain product, they can use this information to develop marketing or sales strategies.
One of the biggest challenges with dark data is that it can be overwhelming for untrained staff. If an organization doesn’t have the right tools or personnel, managing and analyzing dark data can be daunting.
Another challenge with dark data is that it can be difficult to work with. In some cases, data may be unstructured or stored in a format that’s difficult to analyze.
Another risk with dark data is that it can be corrupted. This often happens when data is transferred between different systems or organizations. If the data is not properly formatted or translated, it can become corrupted and unusable.
How to secure your dark data
While dark data can be a valuable asset for businesses, it’s important to remember that this data can also be a liability. If not properly secured, dark data can fall into the wrong hands, leading to fraud or theft.
So what should your organization do to ensure that your dark data is secure?
Here are a few tips to help ensure that your dark data is secure:
1. Understand what data you have.
Before you can hope to secure your dark data, you need to first understand what data you have. Make sure to inventory all of your organization’s data, whether you’re using it for analysis or not.
How is this data stored? Who has access to it? Is the data being collected and stored on-site or is it in the cloud? Once you have a good understanding of what data you have, you can start to develop strategies for securing it.
2. Classify and secure your data.
Once you understand your data, it’s important to classify it. In other words, you need to understand which data is sensitive and which data is not.
Some data, such as credit card numbers or social security numbers, is classified as personally identifiable information (PII). This type of data needs to be treated with extra care and should be encrypted when possible.
Other data, such as website logs or customer feedback, is not considered PII. This type of data can be stored in the cloud or on-site without the need for encryption.
3. Monitor your data.
Finally, it’s important to monitor your data on an ongoing basis. This includes monitoring who has access to your data and how this data is being used. In addition, you should periodically review your security measures to ensure that they are still effective.
By following these tips, you can help ensure that your dark data is secure and doesn’t become a liability for your organization.
What can happen if dark data is not properly secured?
If dark data is not properly secured, it can fall into the wrong hands and be used for nefarious purposes. For example, if an attacker were to gain access to a database of dark data that contained personally identifiable information, they could use this data to commit fraud or identity theft.
To help protect your organization from these risks, it’s important to properly secure your dark data. By following the tips outlined above, you can help ensure that your dark data is secure and doesn’t become a liability for your organization.
The bottom line
Dark data is a valuable commodity for businesses, as it can be used to improve decision-making. However, if this data is not properly secured, it can fall into the wrong hands and lead to serious consequences.
Fortunately, there are steps businesses can take to secure their dark data. These steps include encrypting data, limiting access to data, and securely storing data in the cloud or on-site systems. In addition, businesses should monitor their data on an ongoing basis to ensure that it is being used safely and securely.
Check out some related resources:
Embracing the future of data with augmented BI
POV: Next-Generation Banking
Domo Tops Dresner’s List of Cloud BI Vendors in 2022
Try Domo for yourself. Completely free.
Domo transforms the way these companies manage business.