The Hidden Costs of Big Data

How much does big data cost? There are two ways to answer that question: The simplistic way, which looks only at surface-level big data costs, and the comprehensive way, which factors in the “hidden costs of big data” as well.

What are the hidden costs of big data, you ask? Keep reading for an overview of the costs that you could easily be overlooking within your big data management processes.

Big data’s obvious costs

On the surface, the costs associated with managing big data may seem simple enough to measure.

You have obvious costs, such as:

• The price of the software tools that you use to manage and analyze data.
• The cost of storage infrastructure for your data.
• The cost in terms of staff time that your company’s data engineers spend managing data.

Those costs are easy to identify and calculate.

Hidden costs of big data

If you stop your big data cost calculations with the factors listed above, however, you won’t achieve a comprehensive analysis of the true cost of your data management operations.

You’ll be missing a variety of hidden costs, such as:

1. Inefficient data integration

Deriving value from your data typically requires you to transform and integrate it. If your data integration solution involves a lot of manual effort, redundancy or other types of inefficiency, it could be a major source of hidden costs in the form of wasted staff time and unnecessary infrastructure.

Precisely’s data integration solutions can help address this hidden cost by streamlining data transformation and related processes.

2. Networking data costs

Transferring data over the network can feel free because in most cases, the cost of bandwidth is so low as to be negligible. But when you are dealing with terabytes of data, those costs can add up. Whether you are only transferring data locally on your internal network, or you are moving data between a cloud and on-premise infrastructure, the networking devices and bandwidth that you’ll need have a price.

3. Unnecessary backups

You should always back up your data. However, if you are backing up your data more frequently than you require or storing more copies than you need, you are not operating in the most cost-effective way possible. You want to ensure that you have the right level of data backups, but not more.

4. Low-quality data

Data that is rife with data quality problems will likely cost you a lot more to store, integrate and analyze than data that is free of errors. While avoiding data quality problems entirely may not be possible, correcting data quality issues via automated tools can help you to minimize the costs you incur due to low data quality. See how our data quality solutions can help.

5. Uninformed employees

Are your employees citizen data scientists? Or do they totally lack an understanding of data management best practices? In the latter case, the lack of knowledge on the part of your employees could bloat your big data costs by creating bottlenecks and data quality errors whenever someone who is not a professional data scientist touches data. Avoid this hidden big data cost by turning your employees into citizen data scientists.

For more information about best practices for efficient data integration, read our eBook: How to Build a Modern Data Architecture with Legacy Data