Five Principles for Laying a Successful DataOps Foundation

The story that follows is a pattern in many mid-to-large sized organizations that struggle to find intelligent ways to manage their data. A retail organization was suffering from siloed data spread across numerous departments. Lack of collaboration between these departments led to inconsistent workflows, making it difficult to gather data, create a single source of truth and analyze it to run daily operations. Several teams existed across the organization to handle various individual functions such as supply chain, sales, etc. Yet a lack of transparency and coordination between them led to deceleration of their operations, incorrect deliveries, inventory shortages and consequently shortfalls in profit. They decided to implement a collaborative approach to tackle their data operations challenges and oversee the end-to-end data lifecycle: DataOps.

What is DataOps?

DataOps is a practice, a culture that helps teams work better together. It ensures that the right kind of data flows through all departments, while integrating data processes in such a way that business value is created by enabling data solutions. It helps improve the agility, automation, and collaboration of the entire system. This increases cooperation between different stakeholders within an organization and aids in making informed decisions by minimizing errors and increasing efficiency. DataOps is a mindset that brings teams together by increasing collaboration, transparency, and dedication to achieving organizational goals.

DataOps helped the retailer in our example above by managing their business and data operations with increased efficiency. It provided a stabilized platform to collate vast amounts of data coming from disparate sources. Such a centralized approach enhanced team collaboration and end-to-end data pipeline monitoring made the entire data operations process highly efficient.

What helped the organization the most was leveraging five principles of DataOps to build a strong foundation, which allowed them to accelerate productivity, increase reliability & accuracy of data, and aid in data-driven decision making.

Let’s deep dive into each of these principles.

1. Right flow of information & processes

The first principle places great emphasis on the performance of the entire system rather than each team on its own. It highlights that teams, instead of operating in siloes, should be more transparent and that workflows and data should productively flow from left to right.

This means that any requirements are first identified by business teams and analysts, and then developed by the development teams. They then progress to the operations team, and finally to the end user to meet their needs. This structures the entire system in such a way so that no error or defect goes unidentified, and improves the end-to-end visibility of data across the organization. Increased transparency helps improve the alignment of teams around organizational objectives which leads to more informed decision making, hence creating a strong DataOps culture.

Figure 1: Data flow across teams

2. Continuous Feedback & Continuous Improvement

DataOps focuses on increasing feedback across the flow mentioned above. The idea behind this principle is to make continuous feedback flow from the end users back to each team, so that timely corrections are made, and errors rectified beforehand. Creation of such routine checks and refined business processes ensure smooth running of the data pipelines. Such a proactive approach can create a stabilized platform just as it did for the retailer where good quality data flowed back and forth throughout the organization. Continuous feedback leads to improved incident handling and also helps understand customers’ needs better to enhance customer satisfaction led by data-driven decision making. Such key elements create a strong DataOps foundation helping businesses focus on strategic objectives.

Figure 2: Continuous and enhanced feedback flowing back to the relevant teams

3. Automation

Another key feature to building a flourishing DataOps culture within an organization is automation. The principle states that as data processing becomes more automated, managing and analyzing data becomes standardized. This makes data processing more scalable, where organizations can manage vast amounts of growing data with efficiency across teams.

With automation, organizations can put in less manual efforts to manage data operations, which saves a considerable amount of time that can then be utilized to focus on more strategic areas, while also reducing the likelihood of errors. Automation also helps identify missing data, confirms its validity, and ensures it accuracy and reliability. All of this leads to faster data transmission and high-quality data being received by the relevant teams. Enhancing automation can lead to highly integrated and consistent processes across the entire organization, hence creating overall value for the business.

4. Collaboration

As DataOps is changing the traditional ways of doing business, the principle of collaboration is gaining popularity. The fundamentals of this principle focus on breaking down siloes between teams, thereby encouraging teamwork across the entire organization. This leads to increased transparency across business analysts, developers, quality assurance engineers, project managers, end consumers and all other stakeholders.

Managing operations and monitoring processes together can improve alert generation mechanisms. Better coordination between the teams not only identifies defects but also manages them timely and lays ground rules for their avoidance in the future. This helps to lay out common business objectives and enable teams to achieve organizational goals together. Such consistency and improved visibility between the teams creates overall value for the organization, consequently laying a firm base for DataOps.

5. Agility

One of the fundamental features to creating a strong foundation for a successful DataOps culture and one of the building blocks of a prosperous data driven organization is agility. An agile methodology stresses upon organizations to be more adaptive and responsive to changing needs and trends. Teams pursuing a DataOps culture must be more flexible to changing requirements in order to take up-to-date and informed data-driven decisions.

The principle of agility strengthens two of the principles mentioned above, continuous feedback and collaboration. Continuous feedback from business users helps in improving the processes and adapting to the changing needs of the market. While collaboration helps in increased knowledge sharing across teams leading to better understanding of the dynamic environment in order to proactively meet the needs of the business.

Conclusion

Nurturing a culture built on the principles and practices that are the key to a successful data-driven organization can only be possible when its foundation has been laid by strong DataOps services. The combination of these principles can help teams within an organization make more efficient, data-driven decisions in a more formalized way. Focusing on building a strong DataOps foundation can help overcome siloed data, increase transparency, enhance end-to-end data visibility. A unified culture created by DataOps is capable of solving some of the most significant data operations challenges, leading to superior decision making.

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