3 limitations of cloud-based data tools

limitations of cloud-based data tools

How many cloud-based data tools does your business use? If you’re like most midsize to large businesses, the answer is surprising – often dozens of solutions are in place within a single business.

But as your technical team likely understands on an intimate level, cloud-based tooling, whilst useful (and even necessary for some use cases), potentially has a lot of limitations that shape how it can and should be used in partnership with your other tools.

Some of the most common limitations encountered include:

Robustness

Cloud-based tools are often designed for hyper-specific use cases. This means they often do that one thing incredibly well, but the flexibility of those tools is usually less than impressive.

Likewise, their ability to transform data prior to ingest is often limited, meaning data used elsewhere in the business has to be specially transformed for one platform, which can be time- and resource-consuming.

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Validation

When you’re connecting multiple tools, it’s critical to ensure that your data is validated so you know the output is reliable. But when your cloud tools identify bad data, what’s the protocol? What happens? Does the whole process fail? Is an error report generated? Where does it go?

Having little to no control over the data validation process (if there even is one to begin with) means your cloud tools are likely to become less reliable and out of sync over time. Or, even worse, they could act as the one bad apple that poisons the whole bunch, because the processes weren’t in place to identify and correct bad data.

Connectivity

Cloud tools are often very good at helping you get data in, but how easy is it to get data out? For most platforms, the answer is: not very. It may look like there are a lot of connected tools, but usually these are mostly readers – integrations designed to help you get data into the cloud tool. Often lacking are effective writers so you can extract data to use in other tools, a data warehouse, etc. This makes your entire tech stack far less flexible and scalable, because you can’t easily extract insights from some tools for use in other parts of the business.

Orchestration

When creating a cohesive data strategy despite so many tools being in play, orchestration is key. You need to be able to get a big picture view of your data architecture whilst also having the ability to zoom in and address individual use cases without losing context. Cloud-based tools can make that difficult because of their limited scope, or because it creates a lot of additional effort to maintain that big picture view.

Despite these challenges, cloud-native tools play an important role in the future of your business.

Their specialization in one use case is often too useful to ignore, and the price point is certainly more attractive than more robust platforms that have functionality you’ll never need to touch.

But as an enterprise-level business who relies on data being accurate and up-to-date, you need to make sure your cloud tools are integrated into the rest of your data architecture in a reliable and robust way.

With CloverDX, you can read data from or write data to any of your cloud-based tools just like you would any other platform, without sacrificing the important transformation and validation steps along the way. 

Learn more about how CloverDX empowers you to move data and functions to the cloud without sacrificing robustness, validation or connectivity.

Posted on January 14, 2022
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