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Using DevOps in Data Science

How to Leverage DevOps in Data Science

Using DevOps in Data Science

Data analytics is one of the biggest drivers of digital transformation. No matter if you are part of a small start-up or a reputed enterprise, you need to utilize the power of data to achieve the desired results. That is why it is becoming an essential part of every organization. Most of these organizations even have separate data science teams to help them make informed, data-driven decisions. 

However, to make the most out of data analytics and emerge as a data-driven organization, it is essential to ensure that the data science department works in tandem with other departments. While the rest of the departments must seamlessly embed the data science department’s findings, data scientists should also be able to use a mature self-service platform to develop their models.   

In simple words, organizing data science using DevOps is essential if businesses want to deliver rich business value backed by data and analytics. 

The Biggest Data-science Challenge Among Organizations 

One of the biggest challenges in data science is that most enterprises start with the data science team in isolation. They deploy separate business intelligence (BI) departments that gather crucial insights and learn from behavior patterns. They work in what is called the lab sessions that are isolated from the rest of the organization.  

The scenario is explained very well with the help of this image: 

While this kind of environment might seem favorable to data scientists, it has a big drawback. Contrary to the lab environment in which the data science team has a lot of freedom, many restrictions are involved in enterprise conditions. As a result, things become a lot difficult when it comes to practically embedding the findings of data scientists in the enterprise environment – and the following issues arise: 

  • Multiple builds for a single application 
  • Version mismatch of the libraries 
  • Customers facing issues during deployment 
  • Efforts Burn-out to integrate the software codes 

How Can DevOps be the Possible Solution? 

DevOps is a cultural and operational model that fosters collaboration between the development and the operations team to enable high performance for achieving business goals. The idea behind the approach is to eliminate the wall of confusion that occurs because both departments work for different objectives (Development aims for change, while operations aim for stability). 

We can use the same mindset to resolve the data science challenge organizations face when the data science team works separately. Instead of working in isolation, we can make the data science team works in coordination with the entire organization.  

How Can We Bring the Principles of DevOps into Data Science World? 

1. Break the Traditional Silo Structure 

The first step will be to break the traditional silo structure. This diagram explains it well:

On the left side, you can see the traditional silos structure in which different teams work separately. While the approach may work well for small enterprises, it can be a nuisance for large organizations where multiple teams are involved. 

Data science teams are also working in silos. We must break this silo structure and incorporate the data-science team in real-time business processes. It will help us create a multi-skilled, self-organized, and work-oriented team.  

2. Build a Modern and Mature Data Platform 

Unlike the lab environment, you cannot expect data scientists to build their technology stack. Instead, they will need a modern and mature self-service data platform to focus on the models they want to create.  

For those who still do not know, a data platform is an integrated solution that allows you to govern, ingest, store, analyze, access, and deliver data for strategic business purposes.  

A modern and mature data platform is the one that allows your team to take ownership of data and manage it independently. Here is what it needs to deliver: 

  • Means to schedule 
  • Data science development stacks 
  • Dashboarding 
  • Data catalog 
  • Means to computation 
  • Data exploration stack
  • 3rd party output integrations 
  • Datastores  

How to Organize Your Data & Analytics Team? 

The best way to organize a data & analytics team is to always start as a single unit working in isolation. This single unit may include people with data science, data engineering, and data platform skills. 

Then as you evolve, you can further break and integrate your team into several units explained in this diagram: 

It is not the only way we can organize the data and analytics team in your organization. There are several approaches you can follow that may differ on your requirements. Here are some of them: 

Bringing these DevOps principles into data science can help coordinate with the rest of the organization and achieve much-needed stability. A similar approach has helped us successfully build a data-first culture in our organization for digital transformation. So, you can also benefit a lot from it.  

Talk to our experts if you want to learn how to use DevOps in a data science world. 


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