After realizing data’s importance, companies have started creating different designations and job roles in the Data Science field. Data Operations (DataOps) is an excellent example of such a role that has gained traction. A report from Hitachi has shown that DataOps increases revenue and profit growth of businesses by 60%.
A DataOps team can ensure that your organization gets accurate and detailed information about products, customers, processes, and markets. Thus, if your company doesn’t have a DataOps team, you should consider creating one. Again, if Data Analytics is not working well for your organization, you must set up a DataOps team.
This article will help you understand DataOps, Product-led Growth, how a PLG company should build a DataOps team, and what key functions are performed as you build a DataOps team. Read along to understand better how to build a DataOps team.
Table of Contents
What is DataOps?
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DataOps is an agile and process-oriented methodology for developing and delivering analytics. It brings DevOps teams together with Data Scientists and Data Engineers to provide the processes, tools, and structures necessary to support a data-focused enterprise.
If you build a DataOps team, the primary tasks they perform are to streamline the design, building, and maintenance of applications based on data and analytics. DataOps seeks to improve how products are created and managed and ensure that the improvements align with the business goals.
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What is Product-led Growth (PLG)?
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Product-led Growth is a growth model that focuses on the end-user and relies on the product as the main driver for customer acquisition, conversion, and business expansion.
Companies that use a PLG strategy grow faster and more efficiently by leveraging their products to create many active users who are later converted into paying customers.
These companies do whatever they can to make their products the best in the market. They get customer feedback and implement new product features based on what customers say.
How Should a PLG Engine Build a DataOps Team?
In this section, we will be discussing what it entails for a PLG engine to build a DataOps team.
Key Roles of a DataOps Team
A DataOps team is made up of four key roles. Larger organizations will choose to have many people in each of these roles. Smaller organizations may have a single individual performing more than one role. Let’s look at the critical roles in a PLG company if you build a DataOps team.
Data Engineer
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This software or computer engineer lays the groundwork for the other team members to perform Data Analytics. The Data Engineer is responsible for moving data from operational systems (CRM, ERP, and others) into a Data Lake and writes the transformation for populating schemas in Data Warehouses and Data Marts. The Data Engineer also creates tests for validating data quality.
Data Analyst
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The Data Analyst’s work is to take the Data Warehouse built by the Data Engineer and provide stakeholders with analytics. The Data Analyst helps to synthesize and summarize vast volumes of data. The Data Analyst also uses Data Visualization to quickly communicate information on an ongoing basis or respond to queries raised by the stakeholders. He/she is responsible for making predictions based on the insights extracted from historical data.
Data Scientist
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The Data Scientist takes the responsibility of performing research and handling open-ended questions. The Data Scientist should have domain expertise to help him come up with new algorithms and models for solving problems and answering questions. The models and algorithms created by a Data Scientist can be used to make predictions.
DataOps Engineer
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The DataOps Engineer is responsible for applying agile development, statistical process controls, and DevOps to Data Analytics. They also initiate and automate the Data Analytics pipeline to make it flexible and improve its quality.
Using the right tools, the DataOps Engineer can remove the barriers between operations and Data Analytics, making the entire team more productive.
By removing the barriers between Business Operations and Data Analytics, the DataOps Engineer makes the data more accessible to users by designing the Data Pipeline to be more efficient, responsive, and robust. This new role has the power to change what people think about Data Analytics completely.
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Difference Between Key Roles as you Build a DataOps Team
The following table summarizes the key roles to consider as you build a DataOps team:
| Roles | Other Job Titles | Responsibilities | Skills | Tools |
| Data Engineer | Database Architect, Database Administrator, Data Modeler, ETL Engineer, Data QA Engineer | Data Warehouses, Data Lakes, Data Marts, Schema Design | Programming, Databases, Simple storage, Cloud infrastructure | Informatica, SQL, SSIS, DataStage, Talend |
| Data Analyst | Business Data Analyst, Business Intelligence Engineer, Reporting Analyst, Data Visualization Designer, BI Developer | Visualizations: Graphs, Charts, Reports, Tables, Dashboards | Statistics, Programming, Data cleaning, Machine learning, Data visualization | Looker, Tableau, Knowi, Power BI, Excel |
| Data Scientist | Machine Learning Engineer, AI Programmer Actuary, Machine Learning Researcher, Quantitative Analyst | Models, Algorithms | Advanced mathematics, Programming, Data mining tools, Domain subject matter expert | SPSS, Python, R, SAS |
| DataOps Engineer | – | Automating quality, Promoting features to production, Orchestrating the analytic pipeline | DevOps, Agile development, Statistical process control | Shell scripts, Python, Hevo, Data test frameworks, DataKitchen. |
Key Functions of a DataOps Team
Building a DataOps team is just one aspect of an entire scenario. The other aspect involves the essential functions an organization should know and work closely with to have a solid data foundation.
These functions include:
- Data Supply: The DataOps team must ensure they read from the same script as the Data Source Owners. This means that the DataOps team must work closely with the Data Source Owners and develop ways to ensure that every aspect of the data is available to the business.
- Data Preparation: The DataOps team must transform the raw data into high-quality insights for analytics and other functions.
- Data Consumption: Data consumption is concerned with making the best and correct use of the data in the hands of the DataOps team. It could involve extracting hidden insights or using left-out data for other applications. Data is expensive, and companies that understand this can enjoy the benefits.
Key Principles of the DataOps Team
DataOps gets its cues from agile methodology. DataOps values Continuous Delivery of Data Analytics insights, with the primary goal being customer satisfaction.
The DataOps Manifesto states that DataOps teams value analytics that works and measure Data Analytics performance by the insights they deliver.
DataOps teams also accommodate change and are always willing to understand any change in customer needs. They organize themselves around goals and favor scalable teams and processes over “heroism.”
DataOps teams are also geared towards orchestrating data, tools, environments, and code from the beginning to the end to generate reproducible results.
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- Data Analyst vs Data Scientist: 4 Critical Differences
Conclusion
This blog gives you an overview of DataOps, Product-led Growth (PLG), and how a PLG engine should build a DataOps team. There are four different roles in a company when you build a DataOps team. These are Data Engineers, Data Analysts, Data Scientists, and DataOps Engineers. It also describes the three essential functions of a DataOps team: Data Supply, Data Preparation, and Data Consumption.
As you collect and manage data across several apps and Databases in your organization, combining it for a thorough performance review is critical. Continuously monitoring the Data Connectors is a time-consuming and resource-intensive process. To accomplish this effectively, you must allocate a portion of your technical bandwidth to integrate data from all sources. All of these issues can be easily addressed with a Cloud-based ETL solution like Hevo Data.
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You can share your experience building a DataOps Team in the comments section below.