Data is increasingly important for organizations. After all, they are used for strategic actions, situation analysis and more assertive decision making, amongst a number of other issues, significantly impacting IT companies. Because of them, a market of data-focused solutions opens up to offer customers, such as the DataOps culture.
At the same time, there is a need to bring about internal changes to modernize developments, adapting them to this new market.
One of the current needs is to bring a DataOps culture. Do you know what this is? Keep reading this post and clear your doubts.
What is DataOps?
DataOps is a new approach to IT which brings together the areas of development, operations and data. You’ve probably heard about this strategy before, haven’t you? In it, there is an incentive for the integration of the areas of development (Dev) and operations (Ops).
In DataOps, we have the insertion of a third component: the data (Data). After all, they are increasingly fundamental to the internal operations of organizations. Through them, it is possible to identify customer behavior patterns, analyze the company’s internal situation and make more assertive decisions, amongst many other actions.
With this approach, it is possible to bring the following items to your business:
- quality;
- efficiency;
- error reduction;
- delivery speed;
- real value.
How to structure the DataOps team?
For DataOps to work properly, it is important that it is applied by an efficient, reliable team capable of bringing your company into this new data-centric era.
Therefore, it is important that you monitor how to structure your teams to be in line with these issues on a day-to-day basis. More information below.
Empower your employees
One of the main points to bring the DataOps context to your company is to ensure that your employees can have autonomy to carry out their day-to-day activities, without having to centralize decisions in the hands of managers. With this, there is agility in solving problems and greater freedom for creations and innovations.
If this was already a value in DevOps, for DataOps it is paramount. After all, professionals will have at hand the data that generates even more efficient decision-making, and this independence is an important value — and one that can unburden leadership.
Look for agility and flexibility
Another important point is having the flexibility to deal with routine issues, without being stuck in rigid models.
This becomes fundamental because, many times, professionals are stuck in preconceived production models, which may not work properly. With this, it is difficult to have the expected results for your IT company.
When you invest in flexibility, you allow your employees to have greater autonomy to carry out their activities and, thus, find ways to improve their performances — minimizing the risk that the stuck actions will have disastrous results for the business.
Perform information integration
If we are talking about the day-to-day use of data, it is important that information circulates between sectors, especially between development, operations and areas directly linked to data analysis.
Therefore, the company needs to invest in solutions that allow those involved to have easy access to information, without major problems. This can be done through the use of integrated solutions, which already generate the collection, treatment, analysis and sharing of information among everyone who has access to it.
Focus on data
One of the pillars of DataOps is precisely the transformation of data into a high-value resource. Therefore, it is important that teams are prepared to focus on four specific tasks related to them:
- supply;
- preparation;
- consumption.
What types of professionals make up a DataOps team?
In addition to everything we’ve said so far, IT managers need to have the necessary members in their teams to do a good job, fulfilling important functions.
Let’s take a look at the professionals who can’t be missed.
Data engineer
The data engineer has some essential roles in the company, taking strategic actions in a DataOps environment. In other words, they are responsible for:
- performing the first action within the operations, in other words, verify that the data supply is being carried out in order to guarantee greater fluidity, reaching the correct destinations;
- building the data flow that will serve as a basis for the actions of other colleagues;
- migrating data to a data lake;
- adopting data quality tests.
Analytics engineer
Analytics engineer will have the role of generating the data infrastructure that allows, at the same time, ensuring greater complexity for the analysis and allowing easy understanding for the other employees.
They have some essential functions, including:
- organization of data to carry out the analyses;
- data warehouse development;
- generation of access to end users and analysts, so that they can find the analyzed data in a more efficient and intuitive way, without wasting time in the complexity of the analyses.
Data analyst
This is another professional with a key role within DataOps routines, enabling the user to consume data.
Some of their functions are:
- answering business questions;
- monitoring the main Business Intelligence metrics;
- being responsible for generating the data synthesis, so that the user can consume them properly;
- enabling the generation of graphical visualization of the data for the users.
Data scientist
The data scientist is often closely tied to specific projects. Therefore, it may not necessarily be present in DataOps teams. However, when recommended, they should be part of the teams.
In addition to working together on previous roles, they will also build predictive models, with the idea of optimizing processes and creating innovations focused on artificial intelligence and machine learning .
DataOps engineer
This is responsible for taking care of the workflows between all professionals, uniting activities related to:
- agile development;
- DevOps;
- data analysis.
In other words, it is the manager responsible for efficiently articulating DataOps functions, playing a key role in this process. This is what will allow, for example, the adoption of the Internet of Behaviors in the near future .
Increasingly, data will play a key role in companies. DataOps is a possibility to bring this cultural shift to the business. Therefore, its adoption can be very advantageous for the organization.
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