What can DataOps and DevOps learn from each other?
DataOps and DevOps: unify the common goals. Tech writer Kayla Matthews talks about how DataOps and DevOps can benefit each other and their shared efforts.
The now extremely popular DevOps process came about when software companies realized the compartmentalized way of working — whereby developers and members of the IT operations team did their duties separately — was too inefficient and didn’t support rapid launches.
Also, the development and production environments often differed, which slowed projects down and made it especially frustrating when problems arose.
So, united by a common goal of making projects progress faster and potentially bringing products to market without unnecessary delays, the two departments teamed up, and DevOps was born.
More recently, analysts have been abuzz about another kind of dual departmental effort, known as DataOps.
Like DevOps, DataOps emphasizes collaboration between departments. The professionals who specialize in DataOps focus on pouring over data to continually deliver analytical insights.
Those statistics often highlight trends in customer behaviors or needs, allowing a company to easily accommodate them.
There are several things DataOps and DevOps teams can learn from each other and thereby improve the respective workflows.
DataOps can teach DevOps how to apply its principles more broadly
Although DevOps principles have broad applications, they’re traditionally exclusive to software development and delivery.
DataOps specialists can grasp DevOps principles and suggest ways to introduce them to a wider audience. When that happens, other areas of a company could adopt DevOps principles for different, beneficial uses.
DevOps can introduce development-specific automation techniques
Automation is one of the hallmark principles of DevOps teams. They know that the more they can automate processes, the easier it’ll be to save time without sacrificing quality.
Analysts assert one of the primary tasks of DataOps teams is to eliminate data friction — otherwise known as the barriers that prevent people from using data effectively.
When mishandled, data friction also becomes an innovation blocker, making it more difficult for companies to succeed against formidable competitors.
Both DevOps and DataOps teams will depend on automation, but DataOps professionals may not be familiar with the tools or techniques DevOps uses.
That’s why DevOps teams can do their part to reduce data friction by explaining how they rely on automation for their everyday tasks. Then, DataOps employees can take that information and investigate ways to potentially use automation in ways they hadn’t previously considered.
DataOps professionals can explain how collected insights apply to DevOps
DevOps teams are primarily concerned with delivering applications. Because they generally don’t work with data, they may not even be familiar with the various stages of the data lifecycle. However, DataOps is likely to become increasingly important regarding applications and beyond.
For example, data makes it possible to facilitate retailers partnering with real-time analytics platforms. Then, they can understand what works best for marketing campaigns and adjust accordingly, not just take a look back afterward.
Even though DevOps employees may not work with data regularly, conversations with DataOps professionals could help them realize data insights could lead to development processes more closely aligned with what customers need, want and expect.
After all, every application either accumulates data or delivers it. Taking a careful look at what the statistics show could help DevOps teams improve their processes and glean useful knowledge from compiled data.
DevOps teams applying analytics to study projects in production
People who see the promise of applying analytics concepts to DevOps advocate for using data analysis to gain a greater understanding of how development projects have moved production perform in that environment.
Most DevOps teams strive for continual improvement, and even those that are mature find there’s always room to grow. Data could be the factor that illuminates problem areas and indicates where the most room for improvement exists.
After receiving information from a DataOps team or getting guidance about how to extract it independently, DevOps specialists can stay abreast of issues that occurr in production and quickly roll out fixes that ultimately improve final versions.
On the other hand, if certain elements of a new product perform excellently in the production environment, that’s a strong indicator more of the same functionality might work well in future products.
Both teams must learn how their roles are similar
Clearly, there is no shortage of things DevOps and DataOps teams can learn by becoming familiar with each other’s practices. However, it’s also crucial for the respective teams to focus on similarities.
For example, both DevOps and DataOps teams want to keep customers happy. They just go about achieving that goal in slightly different ways. Also, there is a mutual understanding that siloed work environments aren’t ideal for productivity. When collaboration occurs it’s easier to avoid bottlenecks and gain new perspectives.
In closing, an awareness of the similarities common to the teams makes their shared efforts even more valuable.
By spending time genuinely understanding the tasks people on each team carry out and how they benefit an organization, DevOps and DataOps teams can harmoniously thrive.