Stop wasting time and money by utilizing dark data

The COVID pandemic has exacerbated the dark data problem due to employees working in distributed and remote environments.

The volume of dark data has increased, and the locations where it resides have become more disparate and difficult to manage. This is significant, with businesses losing employee productivity and wasting storage costs.

The solution to this problem is two-fold. First, businesses must be proactive in discovering where their dark data resides. Second, they need to put processes in place to classify and manage this data appropriately.

So what proactive steps should your organization take to reduce the impact of dark data? In this article, we will look closer at the dark data epidemic plaguing our businesses and offer some steps to mitigate the issue.

What is the dark data problem?

In almost every business, there is data that isn’t being used. Data that falls under this category is known as dark data.

The problem with dark data is that decision-makers don’t have access to it when they need to make critical decisions. This can lead to suboptimal decision-making and ultimately wasted time and money for the organization.

The other issue with dark data is that it can take up valuable storage space. As businesses continue to generate large amounts of data, the cost of storing this data becomes a significant burden. If dark data is taking up valuable storage space, it costs the organization money that could be better spent elsewhere.

The COVID pandemic has only made the problem worse. With employees working in distributed and remote environments, the volume of dark data has increased significantly. At the same time, the locations where this data resides have become more disparate and difficult to manage.

For example, if you have a hybrid workforce operating across various cities, states, and countries, it’s going to be difficult to manage all of this data in a centralized location. This is especially true if you don’t clearly understand where this data resides.

The problem with unmitigated and uncontrolled dark data

You may not even realize that you have a dark data problem. This is because, in many cases, dark data goes unnoticed until it’s too late. By the time you realize that you have an issue, it may be too late to do anything about it.

Here are just a few of the potential problems that can arise from dark data:

1. Departmental silos

The first risk is that dark data can lead to departmental silos. When data is stored in disparate locations, it’s difficult for different departments to access and use this data. This can lead to a lack of collaboration and inefficiencies within the organization.

Why is this a problem? After all, isn’t it expected that data will be stored in different departments? The answer is yes, to a certain extent. However, when data is siloed, it can’t be used to its full potential.

For example, let’s say you have customer data that’s being stored in the sales department. This data could be incredibly valuable to the marketing department. However, if the data is siloed, the marketing department won’t be able to use it. As a result, they’ll have to generate their own customer data, which can be duplicative and inefficient.

2. Incomplete data sets

Another problem with dark data is that it can lead to incomplete data sets. When data is scattered across different departments and locations, it’s difficult to get a complete picture of what’s going on. This can make it difficult to make informed decisions.

For example, let’s say you’re trying to understand customer behavior. However, your customer data is scattered across different departments. As a result, you won’t be able to get a complete picture of customer behavior. This can make it difficult to make decisions about how to best serve your customers.

3. Lack of data governance

Another problem with dark data is that it can lead to a lack of data governance. When data is scattered across different departments and locations, it’s difficult for appointed employees to maintain control over this data. This can lead to a variety of problems, including data breaches and compliance issues.

4. Difficulty measuring ROI

Finally, dark data can make it difficult to measure ROI. When data is scattered across different departments and locations, it’s difficult to track and measure the impact of your decisions. This can make it difficult to justify your decisions to upper management.

The first step to solving the dark data problem is to discover and classify dark data. This means understanding where dark data resides and what it consists of. Once you have a clear understanding of your organization’s dark data, you can begin to take steps to address it.

What about dark data security?

In addition to the mentioned problems, dark data can pose a security risk. When data is scattered across different departments and locations, it’s more difficult to secure. This can make it easier for hackers to gain access to sensitive information.

To address this problem, it’s important to have a plan in place for securing your organization’s dark data. This can include encrypting sensitive information and establishing strict access controls.

Data governance plays a critical role in dark data security. But it will be impossible to create an effective data governance policy that addresses dark data if you don’t first take steps to discover and classify your organization’s dark data.

What to include in your dark data governance policy

Once you’ve taken steps to discover and classify your organization’s dark data, you can begin to develop a data governance policy that addresses it. Here are a few things to keep in mind as you create your policy:

1. Data classification schemes: As mentioned earlier, one way to address dark data is to use data classification schemes. This can help you understand where your organization’s data resides/a> and what it consists of. Once you have this information, you can begin to develop policies for managing and securing your organization’s dark data.

3. Data governance: Finally, another way to address dark data is to focus on data governance. This can help you establish policies and procedures for managing and securing your organization’s dark data.

The bottom line

Dark data is a problem for many organizations. It can lead to wasted time, money, and security risks. The first step to solving the dark data problem is to discover and classify dark data.

Once you have a clear understanding of your organization’s dark data, you can begin to take steps to address it. There are a few different ways to do this, including using data classification schemes, data security, and data governance. The best approach will vary depending on the organization.