Which Data Management Tasks To Automate And Which To Keep Manual
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Which Data Management Tasks To Automate And Which To Keep Manual

Which data management tasks should you automate, and which are safer to continue undertaking manually? 

Automation is everywhere – and for good reason! Benefiting everything from error reduction to operating costs and productivity, every business leader is keen to explore how automation can sharpen a competitive edge.

In particular, enterprises are investigating how automation within a data management strategy can enhance the decisions made by technical and strategic staff alike. But with so much complexity inherent to data management – and with so much at stake if something goes awry – is it always right to automate?

To help settle the debate, we’ve shared three data management tasks that you should absolutely automate and three that are best left to the DBA experts.  



Database Administrators and Engineers are exceptionally skilled and knowledgeable people. Their experience is fundamental to fixing critical issues and for future proofing database performance, which means that you need them to be prepared for action, always. Automating some components of database – optimisation helps expert DBAs to focus their time where it makes the most impact.

For example, using real-time proactive monitoring hunts down and analyses problems in database environments before the issue impacts your business. Automated alerts are triaged in relation to business criticality, with those that require skilled intervention instantly raised with DBAs. All of this can be effectively undertaken using automated performance analysis and alert workflows.


Regular environment check-ups are the best way to stop significant or damaging issues from developing – a crucial data management task.

However, keeping on top of a database’s health is time-intensive, and why many organisations favour larger deep-dive analysis, typically conducted a couple of times a year.

Although these in-depth audits and strategy workshops are necessary, supplementing with regular database health checks is an effective assurance against disruptive, expensive risks – security, performance, availability or otherwise.

And the even better news – you can automate them. Automated database health checks (such as those offered by Node4) provide a real-time snapshot of system performance and offer essential near-and-future-term detail to enable both scalability and performance management.  


Streamlined data management isn’t just about backend optimisation tasks and technical strategies led by DBAs and engineers – it’s also about the output of those well-managed databases, including the insights generated by the software, applications and sensors they power. And today, many of these insights can be delivered automatically.

Predictive analytics detect data patterns and suggest what they might mean for your business in the future. As long as your data strategy ensures your data is available in the right place and in the right format, the critical business intelligence can be predicted, from sales volumes to security posture and resource demand. Through capitalising on deep analytics expertise, businesses are better equipped to make better informed decisions.



Performance tuning refers to the complex optimisation tasks undertaken by DBAs to ensure that databases run as efficiently as possible. It’s an essential component of data management.

The main goal of performance tuning is to improve the speed of interaction between the database and application layer or underlying infrastructure and as a result, ensure that databases serve you well as workloads grow or become more complex.

When it comes to performance tuning, strategy is everything. DBAs must have an intricate understanding of the business context in which the databases are operating and how any changes may interact with wider IT infrastructure. Therefore, this activity should be scoped and delivered on a planned basis, often involving wider technical specialists in relation to the infrastructure and application development teams.


As volumes of data grow and the speed at which decisions need to be made increases, so too the complexity of data environments becomes a critical consideration. Often businesses will be running multiple database technologies spanning multiple software versions and there will be a need to create data pipelines to bring data into a suitable repository for effective reporting.

Building the data architecture to best practice is critical and requires the expertise of an experienced DBA and Data Engineer working in tandem with the infrastructure and application teams.

The current and future functionality of the systems needs to be understood so that optimal architecture can be deployed to support the needs of the business going into the future.


Disaster recovery testing is a highly specialist and collaborative subject. At least once a year, DBAs should liaise with infrastructure and compliance teams to rigorously test DR plans and technology, addressing anything that fails or looks contentious.  

Often, DR vulnerabilities are subtle and technically pass the testing criteria. However, an experienced and skilled team will know from history or instinct what’s worth investigating further. Crucially, an automated testing system may not.

Although automated solutions are perfectly fine for ongoing tactical requirements such as testing nightly backups and synchronisation to failover or standby environments, they’re no substitute for the in-depth expertise provided by a DBA who understands both the technical and business context as to the DR requirements.  

Tap into the power of a team of experienced DBAs and next-generation tools with Node4’s Intelligent Data solutions

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