The newest agile operations technique to emerge from the collective awareness of IT and big data experts is DataOps, or data operations. It focuses on developing data management techniques and procedures that increase analytics precision and agility, including access to data, quality assurance, automation, integration, and, finally, model operations and maintenance. Let's learn more about DataOps in the sections below.

Who Benefits From DataOps?

Better data management results in better and more accessible information. Better data leads to greater analysis, which results in better insights, business plans, and profitability. DataOps aims to encourage cooperation among data scientists, engineers, and technologists so that each team is working in unison to better use data in less time.

Leading companies in taking an innovative and purposeful approach to data science are 4 times more likely to experience growth that surpasses customer expectation than its less data-driven competitors. It's no surprise, therefore, that businesses are undertaking data management improvements to enable more accessibility and creativity. Several of today's disruptors — Facebook, Spotify, Stitch Fix, and others have already implemented DataOps techniques.

Anyone in the firm who needed data beyond the short, curated summary housed in the database system had to come to the statistics team and make a request, as was customary back then. Our data staff was fantastic, but it could only operate at a certain speed: there was an obvious bottleneck.

Hive, a data warehouse screening tool that enabled Facebook's team members to query data housed in a variety of databases, finally democratised the company's data.

Origin of DataOps

DataOps is one of several techniques that have emerged from DevOps, a software development strategy that Gartner forecasts will be implemented by 80% of Global Fortune 1000 firms within the next year. DevOps' success is based on bringing together all the two distinct groups that make up conventional IT: one that manages development and the other that handles operations. Because the entire squad is unified in recognising and addressing problems that arise in a DevOps environment, software rollouts are quick and continuous.

This concept is borrowed and expanded upon by DataOps, which applies it to the whole data lifecycle. As a result, DevOps principles like as continuous delivery, production, and management are increasingly being extended to the process of data science manufacturing: To monitor code changes, data science teams use software version control systems like GitHub, as well as container technologies like Docker and Kubernetes to establish environments for analysis and model deployment. Continuous analytics is a term used to describe a data science-meets-DevOps strategy.

Speed and Flexibility of DataOps

Data infrastructure, like data pipelines, is built using agile development techniques in DataOps frameworks. Data architecture is just code, or "infrastructure as code," on a detailed level (IaC). In agile language, IaC stands for "software product."

Cross-functional teams use a DataOps framework to run "data sprints" that develop data models and provide insights to specific stakeholders. Each team is made up of data managers (data engineers, business intelligence managers, and so on) and data users (salespeople, leadership, etc.). The sprint approach incorporates feedback from data users on a regular basis in order to swiftly enhance and refresh data assets.

How Do I Start Implementing DataOps?

As you may have guessed, there is no one-size-fits-all strategy to deploying DataOps at your company. However, there are a few important places where attention should be paid. This is where you should begin:

Democratize Your Data

As per Experian Data Quality, 96 percent of chief data officers feel that corporate stakeholders are expecting greater data access than ever before, and 53 percent believe that data availability is the most significant obstacle to effective decision-making. Nonetheless, there is enough data; by 2020, we will have created 40 zettabytes, or 5,200 GB of data for every individual on the planet.

Make Use of Platforms and Open Source Software

Being agile entails avoiding squandering time developing stuff you don't need or trying to reinvent the wheel when the technologies your team already knows are highly customizable. Analyze your data requirements and tailor your IT stack to meet them.

Automate and automate

This one comes straight from the realm of DevOps: It's critical to automate stages that take a lot of human labour, such as quality validity of the data and data analytics pipeline management, in order to accomplish a faster time to value on data-intensive initiatives.

Using microservices to enable self-sufficiency is also a factor. Offering your data scientists the flexibility to deploy models as APIs, for example, allows engineers to use that code without having to rewrite it, resulting in increased productivity.

Govern With Care

It's no surprise that more organisations are adopting a Center of Excellence approach to information science management lately. It's doubtful you'll receive the return on investment you expected from data science or DataOps until you've developed a roadmap for success that covers the procedures, tools, infrastructure, objectives, and main performance indicators data science teams must consider.

Smash Silos

Collaboration is crucial when it comes to adopting DataOps. As part of your DataOps journey, the tools and services you employ should serve a wider aim of bringing people together to better use data.

Conclusion

At its most basic level, DataOps is about aligning how you organize your data with the objectives you have for it. If you want to lower your customer service costs, for example, you could use your customer data to create a recommender system that reveals goods that are pertinent to your consumers, keeping them buying for longer. But this is only feasible if your data science team has access to the information they'll need to construct the system and the tools they'll need to implement it, as well as the capacity to incorporate it with your webpage, feed it new data on a regular basis, monitor the performance, and so on, all of which will be an ongoing process involving input from your software development, IT, and business professionals.