Three must-haves for effective data operations

Three must-haves for effective data operations ...

Data is a valued asset for a business and it may even be more valuable than the company itself. Nevertheless, if the data is inaccurate or permanently delayed because of delivery difficulties, a company may not be able to properly utilize it to make informed decisions.

It''s not easy to have a good understanding of a company''s data assets. Environments are changing and becoming increasingly complex. Tracking the origin of a dataset, analysing its dependencies, and keeping documentation up to date are all resource-intensive tasks.

Devops was formed as a series of leading data analytics practices and evolved into a fully autonomous process. This is where data operations (dataops) come in. Dataops is not to be confused with its predecessor. It is therefore important to ensure that data operations are automated, and that data is delivered to end-users and users.

Most businesses encounter flaws within the data estate. Various IT silos were unable to communicate effectively (if they were) ensuring that the data was not allocated to another team. The consequences were horribly high, but the quality and value of the information provided to end users were minimal.

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Dataops may be high on promises and low on value. It can seem like a risk to disrupt new processes already in place. Do the benefits outweigh the danger of defining, implementing, and adopting new processes? The Rule of Ten is so popular in my own organization debates that it costs ten times as much to complete a job when data is flawed than when it is good. Taking this conclusion, dataops is crucial and effective.

You may already use dataops, but not know it

Dataops improves communication among data stakeholders in broad terms. It rids companies of the rising data silos. dataops isnt something new. Many agile companies already use dataops, but they may not use the term or be aware of it.

Dataops can be transformative, but, like any great framework, success requires a few fundamental rules. Here are three key points to consider when it comes to effective dataops.

1.Respect the dataops process.

Observability is critical to the entire dataops process. It gives businesses a bird-eye view across their continuous integration and continuous delivery (CI/CD) pipelines. Without observability, your company cannot safely automate or employ continuous delivery.

Observability systems are designed to provide that holistic view and that view must be integrated across departments and in those CI/CD workflows. When you commit to observability, you should begin this process when designing your database and observe your nonproduction systems, along with the different consumers of your data. In this way, you can see how well apps interact with your data before the database begins.

Monitoring techniques may assist you stay educated and perform more diagnostics. In turn, your troubleshooting recommendations will improve and assist corrections before they become problematic. First, do no harm.

If your monitoring generates so much overhead that your performance is reduced, youve crossed a line. Always ensure your overhead is low, especially when it comes to observability. Data pros can ensure operations continue as expected.

2.Map your data estate

This is why you must know your schemas and your data.

First, make sure you understand your data estate''s outline of changes and their impact. As database schemas change, you must examine their effects on applications and other databases. This impact analysis is only possible if you know where your data comes from and where it will go.

Beyond database schema and code changes, you must ensure data privacy and compliance with a full disclosure of data lineage. The location and type of data, especially personally identifiable information (PII), know where your data lives and wherever it goes. What other applications and reports do that data flow across? Who can access it across each of these systems?

3.Automate data testing

The widespread adoption of devops has enumerated a common culture of unit testing for code and applications. Often overlooked is the testing of the data itself, its quality, and how it works (or does) with code and applications. It requires constant testing with your latest data. It is also problematic.

Test your system to ensure you have the most reliable one. It is best to make the most of your data before getting rid of it. Otherwise, you''ll start producing inefficient routines and processes, and you''ll get a nasty surprise when it comes to expenses.

The software you use to test that data whether its third-party or youre writing your scripts on your own must be robust and it must be part of your automated testing and build process. As the data flows through the CI/CD pipeline, you should perform quality, access, and performance tests. In short, you need to understand what you have before using it.

Dataops is vital to becoming a data business. It''s the ground floor of data transformation. These three must-haves will enable you to know what you already have and what you need to achieve.

SolarWinds'' general manager, Douglas McDowell, is the technology director.

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