Data movement use cases that aren't immediately obvious

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How much is data movement worth to your organisation? Not an odd question considering the data migration market is predicted to clock in at $22.7 billion by 2026.

With 98 percent of businesses relying on on-premises servers in one way or another though, legacy-to-cloud migration accounts for fewer of those billions than you might think. There are far more use cases that you could be spending your data management budget on. And they could deliver even higher ROI than the obvious applications.

So, could data movement prove to be more valuable to your organization than you think?

To find out, you need to reconsider the common, one-way definition of “data movement”. We’re going to clarify what data movement encompasses and then walk you through some of the valuable use cases this unlocks.

What counts as data movement?

Data movement refers to the movement of data from one place to another. No surprises there.

There are a few techniques that make data movement possible, and the one you choose depends on the way you’re storing and using said data.

  • Extract, transform, load (ETL): This technique gathers data from the source, transforms it to fit the structure of its destination, and loads it to its destination database. Use this when you need structured data or control over the kind of data you’re storing. For example, when loading into a data warehouse, or adhering to data privacy laws. ETL is a good choice for complex processing. For example, you might want to filter the data significantly or make significant changes before it’s ready for the target.
  • Extract, load, transform (ELT): Use ELT when you need to move high data volumes and carry out simple transformations in the target. It’s best to apply this method when you’re loading to a data lake, which takes large volumes of data to be sorted out later. You’ll then be able to transform the data as-needed, rather than all at once. It makes the loading process faster but slows access post-transfer.
  • Reverse ETL: What happens when you want to send data from a warehouse to one of your applications? Reverse ETL. Your data isn’t worth much if you can’t use it within your day-to-day tools, so a platform that integrates warehouses with software is valuable. Reverse ETL can be challenging nowadays as warehouses and lakes are often in the cloud and many tools are designed for one-way ‘to cloud’ processes.
What's the difference between ETL and ELT?

ETL, reverse ETL and ELT are the most popular movement methods. There are another two techniques that are worth knowing, which make it possible for databases to exist in several places at once.

Replication and synchronization

Replication does what you’d expect – it creates a copy of your database or dataset. That’s useful for several reasons, but it would be even more helpful if that copy remained up to date with its source.

That’s where synchronization comes in. This usually relies on data being structured. Updates can happen at set intervals by pulling data from the source, or in real time by pushing data from the source to the copy.

Common use cases

So, what does all that transforming, loading, replicating and synchronizing make possible? There are a few applications you’re probably already familiar with:

  • Digital transformation: Moving data from legacy systems to cloud environments is a foundational step in most digital transformation efforts.
  • Analysis: If you have multiple data streams in different formats, data movement can translate information into a common structure to make centralized analysis possible.
  • Integration: The free movement of data between your warehouse and applications means that all your tools operate from a single source of truth.
  • Optimization through geographic proximity. You might simply want to move your data closer to you to reduce processing latency.

Each of those applications are incredibly valuable, but they’re the obvious use cases. There are several more that could prove even more useful to your business:

What else can data movement do?

Protect your data

The average cost of a major data breach has climbed to $4.35 million. It’s a common and expensive threat, so it’s not surprising that data engineers have found ways to use movement to mitigate the impact of data theft and loss.

Replication and synchronization make it possible to “back up” your database by keeping an updated copy in case of emergencies. That way, when you register a breach, you can deal with the threat without impacting productivity. While you secure the affected database, your team can work from a replica to avoid downtime.

The same is true in a disaster recovery situation. Don’t put all your eggs in one server-shaped basket. Should part or all of one database be corrupted or lost, you can make the most of synchronization’s two-way transfer and bring the impacted database back up to speed.

Increase productivity and keep your team in sync

In such a closely connected digital landscape, it’s strange to think of physical distance having an impact on productivity and speed. It does, though. It may be negligible if your entire business is dispersed within the same country. But if your organization has offices around the world, server location will have an impact.

The further data has to travel, the longer it will take your colleagues to access it. Compound that over a full day’s worth of data requests, and productivity will begin to suffer. Once again, data movement provides a solution.

Replicating databases on servers closer to each international office will speed things up. Synchronize those datasets or load data to a shared cloud data warehouse, and you’ll ensure that your entire team is synched up and analyzing the most up-to-date data possible.

It’s not the only people performance that data movement improves though.

Improve server performance

Not all data operations are created equal. “Reading” data takes significantly less processing power than “writing” data, and it’s possible to get granular about where those operations take place.

The experts at G2 sum it up neatly:

“By saving all read operations to a replica of the original [database], you’ll be able to save processing cycles on the primary server for higher importance write operations.”

In other words, data movement allows you to allocate operations to the servers that make the most of your processing power. That performance boost is especially valuable if you work in a time-sensitive industry like finance or healthcare.

Meet compliance requirements

It might seem counterintuitive but moving your data from one place to another can improve your compliance, rather than harming it.

Many regulatory bodies and systems like CFPB and HIPAA require long-term data retention. If you can’t supply said data when the time comes for an audit, you run the risk of non-compliance.

In this instance, the more traditional ‘A to B’ approach to data movement can help. Continuously archiving data to a secondary database will reduce the load on your operational servers while maintaining a structured set of historical data.

The ETL process also allows you to tailor the type of data you retain. Regulations like GDPR stipulate the data points that businesses can keep on record. You can set those parameters at the transformation stage to avoid storing the wrong sort of customer information.

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Beyond the basics

Legacy to cloud migrations and data stream integration will always be high profile use cases for data movement. They’re undeniably important, but not just in their own right. They’re crucial because they also pave the way for some of the less obvious use cases mentioned above.

Once your data is mobile, it’s possible to go beyond the basics of digital transformation. Shored up security, optimized server use and boosted productivity are all on the table if you (or your data partners) go beyond the obvious.

Posted on November 28, 2022
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