What is DataOps and why should you consider this model in your data strategy?
The dream of every manager is to have the right information at the right time and in the right format in order to assess scenarios and make decisions. In my experience as a Director of Operations in a company which works with data, I have gone through a long process in my professional life to go from advising entrepreneurs and managers on how to use data to using it myself for making decisions.
Due to my background, I love preparing the analyzes that my company is going to work with. I love taking the time to analyze the data and find the best way for them to speak to us. However, it is frustraing not being able to count on them in a correct, simple and, above all, fast way. There are so many processes to get data into the hands of those who need it!
It is essential to look for schemes to work with which are increasingly safe and simple to get the right data for those who need it. The complexity that exists today in the operations of companies, the speed with which everything moves, and the amount of data that is processed minute by minute, should be considered to achieve this goal. This is what the DataOps philosophy is based on!
DataOps is a concept that today is the current day-to-day routine for those of us dedicated to data issues. It is what we all dream of achieving for our organizations, i.e. an automated data flow, from source to destination, to turn our companies into what we call a Data-Driven Organization at all levels. That is why it is worth knowing what is behind DataOps and evaluate how close we are to implement it according to the situation that our company is living.
What is DataOps?
A DataOps strategy consists of achieving the automation of the ingestion, selection, transformation, cleaning and validation, not only of the code that supports the analytics and data science applications, but also of the veracity, completeness, certainty and trustworthiness of the data, in a constant and organized way.
When implementing a DataOps process, organizations seek to deliver data quickly and reliably, that is, despite the volume of data, deliveries to end users are as agile as possible, eliminating rework. This avoids data movement and transformation processes that do not add value and mitigate bottlenecks.
All data movement and/or transformation generates cost, reason why in addition to speeding up the delivery of curated data, implementing a DataOps process reduces costs in the medium and long term, hence justifying that a company seriously considers the implementation of platforms that allow migrating from its current architectures and models to a DataOps strategy.
To achieve this, a series of principles, practices and, of course, technological platforms must be considered among all the players in the process of getting the data to tell us something meaningful and relevant. All of this through a fluid and reliable process that goes at the speed that the business requires.
These principles are based on
- Constant communication between the parties
- Close and honest collaboration
- The integration of all the necessary elements to orchestrate the pipelines
- As well as the cooperation between end users, audit layers and developers to establish all the necessary rules
The practices range from taking into account what ought to be (Best practices) at each stage of the procedure, in the proper application of every technology, and ideally in the incorporation of AI algorithms that enable us to identify potential errors in the information without the need for human intervention.
Many organizations are on the way, but there is still a lot of work and effort from Managers and Data Engineering Experts to make DataOps a simple reality to implement. There are important prior considerations, technologies that need to be configured, and culture that needs to be implemented for the goal to be achieved.
One of the strategies that most quickly promotes and sustains a DataOps process is the migration of our systems and data sources to the cloud. This is due to the kind of platforms to which we have access in the cloud, and the ease they provide for integration of data. I would say that this is one of the first steps.
Follow us to meet the others!
In the next few days, we will be releasing a series of publications with some examples of DataOps architectures and the technologies through which Derevo helps its clients in this process.
- Cost reduction, optimizing data movement, reducing duplication and minimizing errors.
- Agility in the delivery of data, going at the speed of our current changing environment and in line with our businesses.
- Assurance of the quality of the data delivered. Always the correct data, always integrated, and always at the right time.
- Scalability, it gives the possibility of reaching many more destinations using the same processes.
- We all know the value of data when it is correct and used as a competitive advantage in a business, so having them under a DataOps model with everything we have already highlighted can bring benefits to the entire business. For instance, boosting income and lowering expenses, not just in relation to data, but in any area that makes use of this advantage to make the best judgments and furthermore assist us in decreasing the risks that pose a threat to the operation.