Which Issues Can DataOps Solve?

DataOps is a viable approach that combines data engineering into operations processes. It aims to promote data management practices and procedures that improve the speed and accuracy of analytics. This includes automation, data access, integration, quality control, and model deployment and management.

DataOps attempts to cultivate and foster collaboration among data scientists, engineers, and technologists so, every team is working in sync to leverage data appropriately in less time and according to a survey in 2018, 73% of companies have invested in DataOps in the previous year.

DataOps Manifesto and Principles
The fundamental principles of DataOps are simple. The discipline is formed by the agile methodology, and strive to integrate continuous and real-time data analytics into the DevOps process. Practically, it means incorporating DevOps and data management staff into a collaborative team.

DataOps teams value analytics that works; they determine the performance of data analytics by the insights they deliver. The team accepts changes and attempts to understand the continually evolving needs of the customers. The DataOps manifesto is mainly based on:

  • Analytics working over detailed documentation
  • Client collaboration over contract negotiation
  • Cross-functional ownership of operations over siloes tasks
  • Experimentation and feedback over extensive designs
  • Individuals and interactions over processes and tools

The Issues Solved by DataOps
DataOps has proved itself in solving some serious issues which were neglected in the past, these are:

Bug Fixing - Besides improving the agility of development processes, DataOps has the power to boost the incident management process. Fixing bugs and defects in products are likely to include input from both data and development experts, and it is also an essential business function. With better communication and collaboration among two staff groups, the time to respond to bugs and defects reduce dramatically.

Efficiency - In the DevOps model, every team compiles reports of their work, and it is then passed between multiple, hierarchical, and vertically-organized structures. However, in DataOps, the data staff and development staff work together with each other, and thus, the flow of information is horizontal. Instead of comparing information at monthly meetings, the exchange happens regularly which significantly improves the efficiency of the organization.

Goal Setting - DataOps provide both development and management teams with real-time data on the performance of their data systems. Such data is not useful for monitoring success against any business goal. However, if adequately adaptable business processes are in the right place, the data allow managers to adjust and update the performance goals in real-time.


Limited Collaboration - Implementing DataOps workflows means increasing collaboration between data-focused teams and development-focused teams. DataOps also aims to eliminate the differences between these two business functions. It is also a fundamental process of goal-setting, as both data and development staff need to collectively develop an overview of the data gaining journey by your organization. By doing so, both can witness where the work of the other can be used to improve their work.

Slow Response - One of the most prominent challenges that organizations face today is responding to development requests, both from the users and from higher management. Overall, requests to integrate new features include the same claims being sent backward and forward between data scientists and the development team.

As the DataOps team involve both of these functions, staff can work together on new requests. It allows the development team to witness what effect the original features have on the data flow through the organization. Also, it helps the data teams to focus better on processing the actual goals of the organization.

Challenges Faced by DataOps
While approaching Big Data, more volume of data looks better than less data. However, it is a fact that more data means more dependencies and more points of failure and management. So what are the challenges that DataOps teams face?

Data Silos - DataOps need to cope with data silos that are made as different departments, and teams create data pools with individualized and narrowly optimized processes. Many groups view their operations as inviolable in which each silo is a barrier towards success for implementing better data management strategies throughout the organization.

Lack of Cloud Usage - Most technology experts have understood the benefits offered by the cloud. Yet, still, many organizations do not store their big data apps in the cloud. As a result, DataOps teams are burdened with data applications that require more storage servers and need reconfigured groups to ensure database optimization. Also, this can provide hackers an entry point to compromise valuable data.

Lack of Skills - It is a fact that data professionals of all kinds are short in the tech market. The lack of availability of the right people to manage Big Data projects means that projects don’t happen quickly or are likely to fail. Thus, putting more data at a team that doesn’t have the knowledge and resources to handle is a way to failure.

Future of DataOps
In the future, DataOps intends to significantly improve communication between two parts of a business by integrating data management and development processes functions. DataOps supports the fact that for modern business, data is the most valuable asset.

For every process undertaken by the firm, they must inform the data analytics. To achieve this, every organization should have its own data scientist with proper skills and knowledge, so the challenges left unsolved can be addressed.

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