With the importance of accessible data on the rise in various industries, companies need a way to efficiently store that data. This means that every team member of the company must have successful data insights in all components and steps of the insights value chain. Although businesses are finding that gathering, managing, and deploying data efficiently is a powerful tool, tech industries, in particular, have a significant need.
In recognition of data’s central importance in software development and other efforts, the emerging discipline of data operations (DataOps) has attracted significant attention from business leaders. Does your organization need to consider building a DataOps team of its own?
Even though DataOps might seem like a foreign concept, it can be a powerful tool for your team. In this guide, we’ll provide an overview of DataOps. Additionally, we’ll go over some of the ways that your business can benefit from developing a DataOps team.
What is DataOps?
Coming from a need to better grasp data insights, DataOps was developed to improve data management and delivery. DataOps helps bring speed and agility to the end-to-end data pipeline process. It does so from the collection of the data until delivery.
Since it’s useful in a variety of industries, there are different working definitions for it. Some refer to DataOps as a data management practice, which focuses on improving communication, integration, and automation of data flows between data managers and data consumers. In this definition of DataOps, improving communication is a clear focus, and the overall goal is to deliver value faster by using automation.
Because of the growth in technology and software applications, DataOps is becoming increasingly important in the tech industry. An organization should understand its information architecture and use the right tools to do so.
In DataOps, the automation of data is integral to delivering data across software developers and business teams. By adopting DataOps, teams have access to ongoing planning instead of having that data stored away.
6 reasons your business might need a DataOps team
Because DataOps has such a strong focus on the cultivation and management practices of data, it’s shown to improve the speed and accuracy of analytics. With this in mind, DataOps has team members only look at analytics if there are certain goals and objectives beforehand.
As touched on earlier, DataOps is becoming incredibly important in the tech industry. It ensures that IT teams have access to the data required to build tools and deploy them for business systems. As a result, all tech businesses should be using DataOps.
In this next section, we’ll cover each reason you should develop a DataOps team for your business.
You want to improve the quality of your product
One of the principles of DataOps is “analytics is manufacturing.” According to this belief, analytic pipelines are more or less manufacturing lines. Therefore, DataOps should continuously identify and address any inefficiencies based on the analytics.
By having a DataOps team, you’re allowing each member to identify areas for improvement through accurate data. Since DataOps is a process-oriented approach to improving analytics, a DataOps team is continually working to better its data processes and obtain accurate data to address as a team. Having the data related to things such as previous sales, buyer habits, and reviews enables teams to address any areas for improvement and create a better product.
You need to create better teamwork in data analytics
Many team members function individually and have a sense of isolation from each other. Since their job functions don’t intertwine, they know less and less about one another and are less likely to communicate with each other. DataOps addresses this issue by placing a focus on improved communication.
For instance, DataOps brings data under the control of all team members, whereas before, it was in the hands of IT operations. In doing so, data can be shared easier between team members. As a result, data engineers, data scientists, and analysts develop more trust in one another and have a closer working relationship.
You want a simple way to analyze data
Another principle of DataOps is simplicity. Through continuous attention to good design and technical expertise, there is enhanced agility. For many industries, particularly software development, their most impactful elements are data. Since data discovery can be difficult, many missions are slowed down. Luckily, DataOps focuses on simplicity and solves this issue.
In DataOps, data needs to be:
- Discoverable: Data should be tagged and stored in common formats.
- Accessible: Data should be easily referenced and pulled from storage locations without excessive access control.
- Intelligible: Data needs to be stored in a way that when the file is opened, its contents are intelligible.
You want to have easily accessible data
The focus on simplicity in DataOps waterfalls into other areas. By improving the simplicity of data, there’s an increased focus on the discoverability of data. In other practices, data is unlabeled and difficult to find in advanced analytics pipelines.
DataOps makes data discoverable with trusted lineage. Data is treated as an asset and used to improve the product. Having discoverable data is made possible through consistent categorization and tagging, enabling team members to have access to essential data.
You need to standardize and improve your data quality
DataOps believes that “quality is paramount.” That is to say that analytics pipelines should be able to automatically detect irregularities in code, configuration, and data. By identifying these abnormalities, your team should gain continuous feedback. Ultimately, the standardization of data helps improve your product, as you’re able to make discoveries earlier than with other methods.
DataOps centralizes analytics development, which empowers teams to standardize metrics and control data quality. Occasionally, too much centralization can be a downfall and end up stifling creativity. Because of this, DataOps has developed to create harmony between decentralized and centralized development of data analytics. When decentralized teams make discoveries with new data, they can share their data with centralized development. This way, these individuals can help the solution be implemented on a larger scale.
DataOps brings centralized and localized data together:
You want to improve security
DataOps can help teams in a variety of industries improve their data security. Although DataOps encourages collaboration and the sharing of data across team members, it also has solutions for security issues. Companies following this practice have a security vault with encrypted, sensitive access control information (e.g., usernames and passwords).
Ultimately, DataOps helps team members work together in a variety of environments. With access control, every team member has access to the information they need. From sourcing to delivery, DataOps integrates security practices and controls every step of the way.
Build your DataOps team with leading independent experts
Regardless of your industry, a DataOps team can help your business improve in a variety of ways. Whether you need to increase collaboration among remote workers or standardize and improve data quality, having the right team is essential.
DataOps brings together team members with different skill sets to support data-focused enterprises. In this way, it improves data sharing through access control and enhanced security measures, protecting essential data while also enabling sharing.
Even if you’re not working with high-security projects, Upwork’s remote talent platform can help match you with the top independent talent in tech. Working with independent professionals gives your business access to the top talent in their field and the perfect expertise to develop a DataOps team.
