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3 Required For Efficient Data Operations

3 Required For Efficient Data Operations



3 Required For Efficient Data Operations
3 Required For Efficient Data Operations


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Data may be the company’s most valuable asset – it may be even more valuable than that the company itself. But if data is inaccurate or constantly delayed due to delivery problems, the company may not use it properly to make informed decisions.

Having a solid understanding of the company data assets it’s not easy. The environment is changing and becoming more complex. Tracking the origin of a dataset, analyzing its dependencies, and keeping documentation up to date is a resource-intensive task.

Here come the data operations. Dataops – not to be confused with his cousin, devops – started as a series of best practices for data analytics. Over time, this has evolved into a fully established practice on its own. Here’s his promise: Dataops helps speed up the data lifecycle, from developing data-driven applications to providing accurate business-relevant information to end users and customers.

Dataops came about because most companies had data inefficiencies. The various IT silos did not have effective communication (if they communicated at all). Tools created for one team that used data for a specific task often did not allow the other team to gain visibility. 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.

While dataops offers a solution, C-package representatives may worry that promises may be high and prices low. This may seem like a risk of disrupting existing processes. Do the benefits outweigh the inconveniences of identifying, implementing, and adopting new processes? In my organizational debates that I lead on this topic, I often quote and refer to it Rule ten. Performing a task if the data is defective is ten times more expensive than if the information is good. Using this argument, dataops is vital and worth the effort.

You may already be using Dataops, but don’t know it

Broadly, Dataops improves communication between data stakeholders. This saves the company from growing data resources. Dataops is not something new. Many agile companies already practice dataops design, but they may not use this term or be aware of it.

Dataops can be transformative, but like any large database, a few basic rules are required to succeed. Here are the top three essentials for effective real-world data operations.

1. Commit to observation in the dataops process

Observation is fundamental to the entire Dataops process. This gives companies an aerial view of their continuous integration and continuous delivery (CI / CD) pipelines. Without supervision, your company cannot safely automate or use continuous delivery.

In a skilled environment, Devops surveillance systems provide this holistic view – and this view should be available to all departments and included in these CI / CD workflows. When you make an observation, you place it to the left of the data pipeline – monitoring and setting up your communication systems before the data goes into production. You need to start this process when developing your database and monitor your non-production systems along with the different consumers of that data. By doing this, you can see how well the apps interact with your data – before the database goes into production.

Monitoring tools can help you stay more informed and conduct more diagnoses. In turn, your troubleshooting tips will improve and help fix bugs before they turn into problems. Monitoring provides the context of the pros of the data. But remember to keep the “Hippocratic Oath” of monitoring: first, don’t hurt.

If your monitoring creates so much overhead that your productivity declines, you’ve crossed the line. Make sure your overhead is low, especially if you add observation. If data monitoring is seen as the basis for observation, data professionals can ensure that operations continue as expected.

2. Map your real estate data

You need to know your charts and data. This is fundamental to the Dataops process.

First, document the total amount of data to understand the changes and their impact. As you change database schemas you need to evaluate their impact on applications and other databases. Such an impact analysis is only possible if you know where your data comes from and where it goes.

In addition to changes to the database schema and code, you need to control the confidentiality of the data and their compliance with a complete understanding of the origin of the data. Note the location and type of data, especially personally identifiable information (PII) – know where all your data is and where it is going. Where is confidential information stored? What other programs and reports convey this data? Who can access it in each of these systems?

3. Automate data testing

The widespread use of devops has led to the creation of a common culture of modular testing for code and applications. Often overlook the testing of the data itself, its quality and how they work (or don’t work) with code and applications. Effective data testing requires automation. It also requires constant testing with your latest data. New data is untested and true, it is changeable.

To make sure you have the most stable system, check using the most unstable data. Break things up early. Otherwise you will push inefficient procedures and processes into production and you will get an unpleasant surprise when it comes to costs.

The product you use to validate this data – whether it’s third-party or you’re writing your own scripts – needs to be reliable, and it needs to be part of your automated testing and assembly process. As you move data across the CI / CD pipeline you must perform quality, access, and performance tests. In short, you want to understand what you have before using it.

Dataops is vital in order to become a data business. This is the first floor of data conversion. These three required elements will let you know what you already have and what you need to reach the next level.

Douglas McDowell is the general database manager at SolarWinds.

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