DataOps Engineer – The Emerging Role in Analytics

DataOps Engineer is all that every Company now needs.

What Exactly is DataOps-

DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle, from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.

DataOps incorporates the Agile methodology to shorten the cycle time of analytics development in alignment with business goals.

DataOps Objectives-

1. Data stack should be reproducible

Let's assume that you lose all the datasets that you have worked on in the previous month. How will you recreate them? Will it be easy to recreate them? Let's say that you had the code in the source control. Then, it is easier to do so. However, if the datasets are built with Python scripts, notebooks, or SQL queries, it is much more difficult to do so.

2. Data definitions must be centralized, discoverable, and shared

The various departments and teams in an organization must be able to find the code or the query generated in a dataset, model, or dashboard. Thus, ensuring that the data definitions are centralized, discoverable, and shared is important. Everyone must be able to collaborate via the datasets and ensure team growth.

3. Production data should be isolated

Since the production data is highly important, it is essential to keep the development data clear of production. Ideally, the production dataset will change as a result of the source-control being updated.

4. Change your data definitions quickly

For any data-driven company, it is essential to make use of new software as soon as they are introduced. Product reporting requirements should not be a hurdle, and it must be possible to change the data definition and update it within a couple of hours or at the latest, within a few days.

5. Know if your data pipeline breaks 

Knowing whether a script breaks, if your data pipeline stops working, or if the quality of data has been impacted is essential to maintain a data-driven culture.

6. Automate manual processes 

If your organization automates any manual work, it becomes easier to reduce the workload and to reduce mental stress on any of the teams that are involved in the work. Automating also helps in freeing up time and reduces the chances of human error as well.

7. Data access should be controlled

Depending on the size of your organization, controlling the data access may or may not be an issue. In the case of larger organizations where there are over a hundred employees, controlling data access is a necessity since data is highly sensitive.

Benefits of DataOps

One of the main goals of DataOps is to build a collaborative environment between IT operations and data scientists while each of them is working towards intelligently leveraging the data. We have a large amount of data available to us today, and ensuring that this data is used to its full potential is important to gain a better understanding and insight, come up with better solutions, and also gain greater profits. Let us now take a look at a few benefits of DataOps.

1. Data Problem/Solving Capabilities

With the advent of the internet, and as we have entered the digital age, the amount of data being generated daily is increasing rapidly. It is said that the data created is doubling every twelve to eighteen months. With the help of DataOps, we will be able to convert this raw data into actionable information quickly and efficiently.

2. Enhanced Data Analytics

The use of multifaceted analytics techniques is promoted in DataOps. Machine learning algorithms that can help in guiding data through the various stages of analytics are used. Machine learning algorithms also help in collecting, processing, and classifying the data before it is delivered to the customers. The suggestions or feedback from the customers is also given quickly.

3. Finding New Opportunities

The entire work process within an organization can be changed with the help of DataOps as it provides a greater amount of flexibility. New opportunities are presented to us as the priorities shift and a new ecosystem is created that has no borders or barriers between the different departments in an organization. Data engineers, data analysts, developers, operations managers, and marketers are now able to collaborate in real-time and to plan and organize ways in which corporate goals can be achieved. Through this, response time is accelerated, and the organization can also provide better customer service.

5. Providing Long-term Guidance

The practice of strategic data management is promoted through DataOps. Multiple groups work towards negotiating the needs of the clients and work towards organizing, evaluating, and studying the data and the feedback given by the customers. Automating processes helps us in making the business more efficient and effective and thus providing long-term guidance. It can be considered as a two-way street between the data users and the data sources.

How important is DataOps to organizations now and how important will it be in the future?

It's important to realize why dedicating an entire technical person to this endeavor is worthwhile. It doesn't have to even be their full-time job. In smaller groups, it can be a part-time position, but it's important to really lay it out on paper as someone's responsibility. Someone needs to own the DataOps transformation for it to be successful. A lot of times what we're seeing in the early stages is that it's spread across five, six or seven people who do different bits of it, and that can work, but it will only get you so far. At some point, there needs to be more directionality, and that comes with having a dedicated person or group of people — depending on the size of the project — who are thinking about this as a critical element of their job and this totally happens with a dedicated person called the DataOps Engineer.