Tech and IT experts say that data is the most valuable commodity in the world, and it can make or break a business in no time at all. Aside from data, what matters most is how accurate, and up-to-date the data is, which helps you make more informed and well-timed decisions.
Over the past few decades, data capture and processing have played a significant role in the evolution and innovation of information technology. Moreover, the data environments have also transformed rapidly, which requires understanding data in a more streamlined manner. This is where DataOps comes in.
In this article, we are going to discuss:
What is DataOps?
DataOps, short for ‘Data Operations,’ is the newest and most advanced approach to data management. DataOps can bring together technologies and processes in an organization while fusing them with business processes and principles to automate data management and organization.
DataOps encapsulates several components into its methodology, including agile development, personnel, data management technology, and even Development Operations, popularly known as DevOps. These merge into a complete data framework that offers valuable insights for the stakeholders of any business.
Data processing and management is a staple for any industry, and it has become imperative for marketing and sales personnel to drive better results and decisions backed by data. DataOps helps them meet the growing expectations by providing them with a complete and comprehensive framework.
In simpler terms, DataOps delivers relevant and high-quality data to customers of a particular business, accelerating the construction and implementation of automated data workflows. In reality, the DataOps definition is much broader and complex, and its applications may vary from organization to organization.
Why Should You Implement DataOps?
In today’s fast-paced and data-driven environment, a business has to manage several data flows. Data management is also getting increasingly complex due to the higher and faster influx of data, and companies need to facilitate this process.
If you are still contemplating whether or not to use DataOps for your company, here are some reasons to convince you.
1. Increase in Data Fluency
In the past few years, there has been a significant rise in data fluency, and more so because of the transformation in enterprise software.
Business software is becoming much easier for the end-users to understand and learn, which puts pressure on data and analytics software providers to develop tools that are difficult to use.
Moreover, people within the organization have also become well-versed with the use of DataOps tools to make data-driven and sound decisions.
2. Software That Connects with Data
Earlier, the tech industry was all about building new software for every industry, but now the focus has shifted to software that leverages data in each sector and revolutionizes the processes.
This has brought about an increased need for companies to implement DataOps to be able to utilize data in a better way, so they can lead the market and become the agents of change.
3. AI and ML
One of the biggest reasons why you should consider investing in DataOps is because of the massive shift of businesses to the cloud, which has given them increased capabilities for artificial intelligence (AI) and machine learning (ML) operations.
Since quality data is the key to success in AI and ML operations, your company will also need to invest in accurate and extensive data sources.
Challenges Addressed by DataOps
DataOps offers you complete control over the processes and operations of your organization. Moreover, it also does away with the hurdles that stand in the way of rapid data management, and this results in improved productivity for your team. As a consequence, you are able to roll out new products, services, solutions, and much more within a fraction of the time that it would normally take.
DataOps solves a wide variety of challenges and problems that are usually faced by data teams, as well as sales and marketing teams. Some of these challenges include:
1. Fixing Bugs
DataOps plays a major role in the incident management process. Identifying and fixing bugs in products and services don’t just require input from the DevOps team. Rather, data experts also have an important part to play in the process, and the communication between the two teams greatly accelerates the bug fixing system.
DataOps is also known to optimize productivity and efficiency for any business. Traditional development practices involve performance reporting through several tiered structures. However, when you switch to DataOps, both the development and data factions of the company work in real-time, thus facilitating the exchange of information.
3. Goal Setting
Through DataOps, both data and development teams get access to insights on the performance of the data systems. The data derived from the teams can be manipulated through a set of business processes to determine and update their business goals in real-time.
4. Limited Collaboration
DataOps calls for the level of collaboration between data management and development that is needed for smooth operations. It can be used for seamless communication and collaboration between the two teams. Both teams can work together and determine the direction of their data capture journey.
5. Slow Response
Generally, companies have a lot of trouble in managing development requests, which mostly result in back and forth claims and requests between data and development teams. However, DataOps can help change that, since it allows both teams to collaborate on developing and upgrading applications and products.
The DataOps Methodology
There are several steps involved in the DataOps methodology, which are responsible for streamlining the design, implementation, and management of data delivery while keeping the policies and procedures in check. This is important to optimize the use of data in a dynamic environment.
The DataOps process begins with a data pipeline, which depicts the flow of data through different stages inside a project. The project starts with data extraction from various sources and culminates when the data is converted into a visual representation for use by business executives or managers.
The entire data pipeline is automated and managed by DataOps so that the data can be leveraged for production in line with the CI/CD practices used in DevOps. There are three main steps of the data pipeline automation process.
The first step is known as the Sandbox, and it involves the first iteration of data analysis. It is done by data management teams, who scour the data for the value they can derive from it. At this stage, data cleansing and subsequent steps aren’t a priority.
The Staging step involves the cleaning of the analyzed data, which is followed by documentation and modeling. These steps are iteratively repeated to improve the data quality, and the final iteration leads to validate the models that are suitable for production.
The final step involves the use of analyzed data models for the production stage, which results in valid and accurate data for the end consumers. The data can be used by the company to make business decisions and generate a higher return on investment (ROI).
The DataOps definition encompasses a set of principles that can be used by individuals and organizations, and it derives these from:
- Agile Development, and
- Lean Manufacturing
These principles are highly essential for businesses to make data-driven decisions.
Agile and DataOps
The Agile methodology is highly popular among software development teams, and it allows them to roll out new applications in just a few hours, that too with impeccable quality. Data teams can make use of Agile principles for real-time business decision-making. Without it, the data teams can take a long time to implement any business changes, which can delay the production process greatly.
However, with DataOps and Agile principles in place, you can quickly have the right data and bring analyzed data models into production. Not only will this accelerate the product development process, but it will also make communication better between the development and data management teams.
DevOps and DataOps
DevOps acts as a bridge between the development and operations teams in a company. It is known to accelerate software development and deployment. Moreover, data teams can make use of DevOps principles in DataOps to collaborate better with development teams. Whether your data scientists require data analysis, modeling, or deployment of the machine learning algorithms, they will have to depend on IT.
However, when DataOps and DevOps principles are set in place, data teams can deploy their own models and also perform analysis quickly, thus reducing the time. We will discuss the difference between DevOps and DataOps in detail in the next section.
Lean Manufacturing and DataOps
Lean manufacturing is a method that optimizes the product quality and efficiency of the development teams, while also reducing any kind of waste gathered in the process. Data teams build pipelines that facilitate the flow of data from extraction into reports and visualizations for stakeholders and decision-makers.
A traditional model would involve the data scientists building data models and data engineers figuring out how they can be moved into the production stage. However, when DataOps is implemented with Lean manufacturing principles, you can experience a much quicker turnaround time.
As you can see, DataOps makes use of the combined principles of DevOps, Agile, and Lean Manufacturing for improved data management, including streamlined processes and a more productive team.
DataOps vs DevOps
By now, you may have understood that DataOps is much more than a portion of DevOps with a data pipeline. In fact, there are quite a few differences between the two.
The main difference between DataOps and DevOps is that the latter encompasses software development and IT operations while ensuring automated deployment. On the other hand, DataOps involve the ingestion, transformation, and orchestration of data workflows.
DevOps is typically implemented in companies that have software production processes. It brings together software development and IT operations to accelerate the release time of premium software. It offers a single automated package that combines the building, testing, and deployment processes.
Although DataOps isn’t an extension of DevOps, it does derive its name from there. It has nothing to do with automated software deployments, and more to do with data workflows and their management.
There are several advantages that companies can derive from using DataOps along with DevOps principles, including a centralized repository of the complete data ingestion processes and data delivery monitoring on version control systems. Moreover, it automates the real-time data integration of the developer’s code with live data pipelines.
Another benefit of DataOps is that it allows data and development teams to evaluate the data pipeline during the testing process so that the changes made after the QA and diagnostics processes can be implemented into the code before the data models go into production.
Last but not least, it offers the flexibility of continuous delivery, along with auto-syncing the source code with the repository and pushing updated data pipelines into production with just a click.
DataOps vs MLOps
Just like DataOps and DevOps, there are several different departments that depend on IT operations. A few years ago, companies usually kept their IT operations separate from their business operations, but things have changed now.
Along with DataOps, there is another process that is quite close to it: MLOps, which basically combines IT operations with machine learning. It helps data scientists and IT professionals to collaborate and communicate with each other regarding the production of the machine learning model lifecycle, which involves six different steps.
The Six Steps of MLOps
- Problem Understanding
- Data Collection
- Data Annotation
- Data Wrangling
- Model Development, Training, and Evaluation
- Model Deployment and Maintenance
Similar to DataOps, MLOps focuses on facilitating more automation and producing a machine learning lifecycle with greater quality and efficiency, while also complying with business regulations and laws. The common point in both DataOps and MLOps is that both of them are focused on faster project deployment with optimized quality.
MLOps also borrows some of the practices from DevOps, such as continuous integration and continuous deployment, which are applied to machine learning. It facilitates the training of data models, while also feeding them with new data. If you implement MLOps in your business, your data scientists will be responsible for driving results and delivering value for your organization.
With the passage of time, data is increasing in magnitude, frequency, and diversity. This also means that there is an increased need for structured data to make key business decisions, and organizations can’t do it on their own with the existing infrastructure they have. Therefore, the DataOps revolution is here to stay, and it will only continue to evolve with time.
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