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8 Modernization “Gotchas” & How to Avoid them with Data Observability

“Modernization” can mean different things depending on the data professional that you’re talking to.

It could be a tactical approach that involves replacing existing technology with a newer, better technology or about adding new capabilities to support new use cases. Or it could be a more strategic initiative that involves moving to the cloud or outsourcing to managed services.

Regardless, getting the best return from a modernization effort requires avoiding some common pitfalls. But let’s start with some of the major potential benefits of modernization.

Despite the benefits of modernization, wholesale changes such as a complete “lift and shift” to the cloud may not always be a winning strategy. You need measurement and analysis of the current and future state of your environment to help you make informed data modernization decisions and avoid these common issues faced by modern enterprises.

Watch Out for these Modernization “Gotchas”

From planning to implementation and post-implementation management, here are eight modernization “gotchas” to be aware of on your modernization journey.


1. Giving Up Too Much Control

Outsourcing infrastructure can present tradeoffs in terms of performance, capabilities, security, cost, and other aspects that may be more easily controlled in an on-premises environment.

Loss of control is a major reason why many organizations opt for a hybrid model rather than moving all applications, workloads, and data sources to the cloud.

What’s more, for large, consistent workloads, the economics of an on-premises environment can outweigh the benefits of the cloud.

2. New tech, same problems

Moving to a new home is a great opportunity to clean out the clutter in your garage, attic, and basement. The same is true for modernizing your data environment.

There’s a real cost (and risk) of retaining unused and redundant data, workloads, and pipelines. Consider decluttering and consolidating your data environment as part of your modernization effort.

3. Migration Costs

A lift-and-shift approach can make migration easier but might not take advantage of the benefits of the new technology or environment. Refactoring or re-architecting solutions can provide bigger benefits, including lowering production costs and improving performance.

Yet these engineering efforts bring additional cost and risk. In many cases, neither option will yield a return on investment, in which case, precious time and resources should be spent elsewhere.

4. Tech Sprawl

Cloud marketplaces can make a creative engineer feel like a kid in a candy store with a vast array of capabilities at their fingertips. Leveraging new technology can be necessary to support innovation for your business. The downside is this can also lead to tech sprawl that’s difficult to oversee, manage, and budget for — especially in a multi-cloud environment.

Risk increases from dependency on additional technology, additional skill sets, and greater complexity overall.


5. Data Migration Risks

Many aspects of data can get “lost in translation” as it gets migrated from one technology or environment to another. For example, moving data from a data warehouse to a data lake can provide flexibility on the one hand but a lack of control on the other.

That’s why data reconciliation — ensuring data arrives as expected — should be a key component for any modernization strategy. Of course these sorts of checks and balances are becoming ever more important post-migration as data becomes increasingly distributed and accessed on-demand and in real-time.

6. The Data Swamp

Siloed data in the cloud is still siloed data — it’s distributed and is only usable if identifiable. Migrating a data swamp from on-prem to the cloud doesn’t solve much. In fact, doing so might actually increase confusion and cost.

Data was hard enough to find before, and now it has moved. To maximize the impact of your data, you need to make it easy to find, explore, and validate.


7. The Meter is Running

Limitless scalability sounds great — until someone forgets to turn off a service that’s no longer needed. It can also be quite easy to generate workloads that scale out of control. Failing to implement the right monitoring and alerts could lead to a massive cloud bill.

Insight into how and why resources are consumed and how to improve workload efficiency can improve the price / performance ratio. Multiplied across all of your workloads and use cases, it’s easy to see why data observability has become such a hot topic for budget owners.

8. Cost vs. Benefit

In theory, accurately assigning compute and storage utilization costs to individual business processes should be easier with cloud technology that is metered. Unfortunately, that’s typically not how the billing works, which makes internal chargebacks much more complex than they should be.

To truly optimize data investments for the cloud you need visibility into the cost of supporting specific business processes so that you can align business and technical strategy and tactics for the maximum return on data investment. Unlike the sunk costs of on-premise infrastructure, the cloud brings the flexibility to adapt usage quickly based on business priorities, but this only benefits you if you measure what you manage.

Use Data Observability to Reduce Risk in a Modernization Strategy

Data observability can reduce risk in your modernization strategy by providing end-to-end visibility into your data, processing, and pipelines. Get the insights you need to make informed decisions for every step of your modernization journey:


Data observability gives you the insight needed to put the right plan in place to get the maximum return on modernization initiatives. It enables you to:


Beyond improving agility, lowering cost, and adding capabilities, modernization can provide an opportunity to improve data reliability, management and usability because it helps with:


“X”-as-a-Service means cost is not a one-time, fixed event but a meter that runs whenever it’s used. Cost efficiency can only be achieved by monitoring, analyzing and acting upon utilization. Data Observability technologies that provide the following capabilities often yield the highest return on investment:

While XaaS vendors do a great job of providing agility, they are not incentivized to provide efficiency. In fact, they profit from inefficiency. Data observability looks out for the consumer of XaaS.

Add Data Observability to Your Modernization Strategy

Get a demo of the Acceldata platform to see how data observability can help you make smarter decisions as you implement your modernization strategy.

To learn more about data observability, you may also download and read this white paper.



Thoughts and trends on data observability

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