What is DataOps and Why Businesses Need It More Than Anything Else?

What is DataOps?

Data Operations or DataOps is a practice to help an organization collect, process, manage, and use its data. It optimizes its data pipelines through automation,collaboration, and agile principles.

At its core, data operation brings everyone together, from data engineers, and data analysts to business owners. This collaboration is important for efficient data management. 

Why Businesses Need DataOps?

1. World is Flooded with Data

The reality is we are generating data at an exponential rate. Back in 2018, the world data was only 29 zettabytes and now it's 147 zettabytes. The data is growing faster than anything else with a rate of 66% per year.

A study by IDC said the world data would reach 175 zettabytes by next year. Storing, managing, and most importantly, analyzing this huge data will become even more difficult. DataOps is a way to effectively perform these steps of data management.

2. Challenge of Data Duplicity

The growing data is not the only concern of businesses. Duplicate and inaccurate data is also a reason why big data projects fail. Through data operations, businesses can get rid of such data discrepancies and get high-quality data.

3. Data Management is Costly

Managing data internally or externally on a data management platform is costly for businesses. A typical platform costs them around $1000 to $6000 per month. Not just the platform fees, there is also the cost of data engineers, data analysts, IT professionals, and infrastructure.

Increased volume and poor quality of data increase this cost further making data management more expensive. DataOps not only reduces the cost of data management but also helps businesses gain valuable output from their data.

However, businesses must understand the fundamental ideas and key principles behind it to get the clear benefits of data.

Also Read: TechOps vs DevOps: A Must-Read Comparison for IT Organizations

Fundamental Ideas Behind Data Operations

• Agile- DataOps works on an agile approach to quickly respond to the changing data needs and environment.  
• DevOps- Continuous integration and delivery not only help in DevOps but also in data operation. It automates the data management process and also ensures all the data pipelines are updated.
• Product thinking- When developing data products, businesses must thoroughly evaluate their needs and goals. This idea helps them collect data that brings true value to their product and customers.
• Lean- The lean approach in data operation ensures the data teams don’t waste more time and resources than required. It increases the focus on delivery value and minimizes inefficiency.

What are the Key Principles of Data Operations?

Here are some basic principles that define how a DataOps ecosystem should be:

1. Automating the repetitive tasks.
2. Utilizing open standards and tools that best fit your needs.
3. Exchanging data in clear and structured format (tables) for easy understanding and processing.
4. Controlling data access with different levels of permission depending on user needs.
5. Tracking the data flow and how it transforms.
6. Integrating data through rule-based and statistical methods including human intervention when needed.
7. Storing data centrally or distributed across systems, with role-based access.
8. Processing data in real-time (streaming) or in larger batches, depending on the nature of the data and the analysis required.

Major Benefits of DataOps

By creating an ecosystem that runs on the above principles of data ops, businesses can get benefits in multiple ways:

1. Cleaner and More Reliable Data

DataOps helps the data team in running data check-ups to ensure the accuracy and quality of data. Any mismatch or misleading data can be fixed in real time.  The result is cleaner and more reliable data for making better decisions.

2. Faster Analytics

One of the benefits of data ops is it speeds up the time to interpret data and gain insights from it. They get more time to use those insights to their advantage.  

3. Less Expensive Data Management

Your data is a valuable asset for your organization. It needs to be securely stored and managed before processing. Though this sounds easy, a significant amount of money, time, and resources is involved.

With Dataops, businesses can utilize their resources in a better way to perform these essential tasks of data management.  

4. Reduce Siloes and Boost Efficiency

DataOps breaks down communication barriers between data teams and business users. It also reduce dependency on data engineers to find and understand relevant data.

Conclusion

DataOps is relatively new in the world of data, but its importance is much more than we have thought. The increasing importance of data science, the adoption of the agile approach, and the complexity of data have made it a necessity today. It gives businesses the power to make the most out of their data.