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Data can be the most valuable asset of a company – it can be even more valuable than it the company itself. But if data If delivery is inaccurate or constantly delayed due to problems, the business may not be able to use it properly to make informed decisions.
Have a solid understanding of a company information assets not easy. The environment is changing and becoming increasingly complex. Tracking the origin of a data set, analyzing its dependencies, and keeping documents fresh are all resource-intensive tasks.
Dataops run here. Dataops – not to be confused with your cousin, devops – started as a series of best practices for data analytics. Over time, it became a fully-fledged experience. Here’s what it promises: Dataops help speed up the life of your data, from developing data-driven applications to delivering accurate business-critical information to end users and customers.
Dataops occurred due to inefficiency in the database in most companies. Various IT silos did not communicate effectively (if they did communicate at all). Tools built for a team – data used for a specific task – often maintain the visibility of a different team. Data source integration was random, manual, and often problematic. Sad result: The quality and value of the information delivered to end users was below expectations or completely inaccurate.
Although Dataops offers a solution, those in the C-suite may be concerned that it will be high on promises and low in value. It may seem like a risk of disrupting existing processes. Do the benefits outweigh the concerns of identifying, implementing, and adopting new processes? I often quote and cite in my organizational discussions on the topic Decimal rule. It is ten times more expensive to complete a task when the information is defective than when the information is good. Using this argument, dataops are very important and worth the effort.
You can already use dataops, but you don’t know it
In a broad sense, dataops improve communication between information stakeholders. It frees companies from evolving information silos. dataops is nothing new. Many flexible companies already use dataops constructions, but they may not use the term or be unaware of it.
Dataops can be transformative, but like any large framework, a few basic rules are required to succeed. These are the three most important real worlds for effective dataops.
1. Be committed to being observable in the dataops process
Observation capability is key to the entire dataops process. It gives companies a bird’s eye view of their sustainable integration and uninterrupted delivery (CI / CD) pipelines. Without the ability to monitor, your company will not be able to safely automate or launch continuous delivery.
Systems that can be observed in an experienced development environment provide this unified view – and this view should be accessible between departments and incorporated into those CI / CD workflows. When you’re committed to being observable, you place it on the left side of your data pipeline – you monitor and configure your communication systems before the data enters production. You need to start this process when designing your database and monitor your non-production systems with different consumers of that data. By doing this, you can see how well the programs interact with your data – before moving to a database productabout.
Monitoring tools can help you become more informed and more diagnostic. In turn, your problem-solving tips will improve and help you correct mistakes before they become a problem. Monitoring provides context for information professionals. But remember to follow the “Hippocratic Oath” of Monitoring: First, do no harm.
If your monitoring puts so much strain that your performance is declining, you’ve crossed the line. Make sure your head is down, especially when adding observation capabilities. When data monitoring is taken as the basis for observability, information professionals can ensure that operations continue as expected.
2. Draw a map of your data property
You need to know your schemes and data. This is the basis for the dataops process.
First, document your general information property to understand the changes and their impact. As database schemes change, you need to measure their impact on applications and other databases. This impact analysis is only possible if you know where your data is coming from and where it is going.
In addition to changes to the database layout and code, you should monitor the confidentiality of the data and its compatibility with the full view of the data generation. Label the location and type of information, especially identifying information (PII) – know where all your information lives and goes. Where is sensitive information stored? What other programs and reports do this information flow between? Who can access it in each of these systems?
3. Automate data testing
The widespread adoption of Devops brought a common culture of unified testing for code and applications. The quality of the data itself, and how it works (or won’t work) with code and software, is often overlooked. Effective data testing requires automation. It also requires constant testing with your latest information. New information is not tested and is not accurate, it is changeable.
Test using the most variable data you have to make sure you have the most stable system available. Break things up early. Otherwise, you will push inefficient regimes and processes into production, and you will face an unpleasant surprise when it comes to costs.
The product you use to test that information – whether third-party or write your own scripts – must be robust and must be part of your automated testing and setup process. As the data moves through the CI / CD pipeline, you will need to perform quality, input, and performance tests. In short, you want to understand what you have before you use it.
Dataops are very important to be a data business. This is the first stage of information transformation. These three conditions will allow you to know what you already have and what you need to reach the next level.
Douglas McDowell is the general manager of the database at SolarWinds.
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