The need for DataOps stems from a simple fact: the world is experiencing a dramatic surge of data. With an average tech company generating around three terabytes of information daily, its overall amount is predicted to double every 1.5 years and reach the incredible 175 billion terabytes within the next four years.

The trend for exponential growth is here to stay and results from multiple factors:

  • The popularity of purpose-built data engines   
  • Innovative technologies such as artificial intelligence, machine learning, or natural language processing  
  • Total digitization  
  • The increasing number of RPA and IoT solutions  
  • The rise of customer data platforms, and more 

But why does it matter?  

The old models are failing. You might expect that more ;data means more business insights for companies. Instead, piling up information shows how outdated many analysis and processing models are. The COVID-19 pandemic highlighted this problem in its entirety: the skyrocketing popularity of remote work tools and IoT solutions brought down the accuracy of many data models from 90 to 60 percent. This resulted in immature and low-quality data, inadequate intelligence, and immense business losses for many companies.

To survive in 2021, companies need to gain accurate business insights in real-time. Fortunately, the approach to fast and efficient processing of large amounts of information has been around, waiting for its time to come. Meet DataOps.

What is DataOps?

In brief, the new approach in question is an Agile methodology that combines DevOps and lean manufacturing principles to optimize data lifecycles and generate accurate business insights.

However, it is not just “DevOps for data.” The main difference between DevOps and DataOps lies in the focus of these methodologies, while other discrepancies hide in their BTR pipelines.

DataOps мы DevOps

Information has become essential for the decision-making process at many enterprises. Relying on CI/CD principles, automated routine, optimized operations, and cross-team collaboration, DataOps makes the most out of it. And this is what makes it popular today.

Why DataOps?

The rush is understandable. Considering the trend for information volume growth, businesses that follow outdated practices risk to face significant problems:    

  • Lower work efficiency because of siloed teams  
  • Poor data quality leading to the lack of accurate insights, hence less adequate business decisions  
  • Time and resources wasted on manual testing and fixing errors  
  • Slower response time to development requests and decreased product release speed  

To avoid it and to stay on the competitive edge, you might need some assistance to handle the overwhelming quantities of nformation. Many enterprises partner with experienced IT consulting providers and get timely assistance to handle the overwhelming quantities of information. At TEAM International, we offer analytics services to help you collect, structure, and understand generated data better. And, by implementing the DataOps methodology into business operations, you will achieve maximum efficiency in analytics and BI acquisition.  

In fact, the advantages you can gain go much further than that.  

What is in it for your business? 

Mostly, companies that consider or already implement the DataOps approach look to optimization of operations, improved analytics, and more efficient governance. Based on our experience, below we want to outline a more specific list of benefits from adopting DataOps.   

  • Boosted productivity. Data specialists tend to spend 56 percent of their time on operational tasks and about two months per year on troubleshooting. Streamlining labor-intensive processes and automating data flow stages will reduce errors and reduce the manual tasks load. Your teams can save time to work on more critical tasks such as data monetization or business insights delivery.  
  • Cost reduction. Money saved means money earned. The new methodology lets you increase the efficiency of your data processing workflows while lowering expenses. For instance, companies that have implemented Delphix DataOps solutions report a one percent increase in revenue and significant drops in the costs of data operations (39 percent) and storage infrastructure (72 percent).   
  • Enhanced communication and collaboration. You can make data more accessible even to non-technical specialists. Instead of team silos where everyone works in their isolated vacuum, the new methodology promotes cross-functional cooperation and interaction between teams and entire departments.   
  • Shorter data analytics cycles. You will be able to optimize each stage of the analytics cycle, enabling your teams to process larger volumes of info while carefully distinguishing what is useful and what is not. As a result, you will receive valuable business insights in real-time.   
  • Secure data workflows. DataOps adopts the DevSecOps mindset, viewing security as an integral part of a software development life cycle (SDLC) and a responsibility shared by all team members. The relationship between DevSecOps vs. DevOps is simple: the former is all about integrating security methods into DevOps processes. Instead of pushing mandatory security requirements, it focuses on establishing safe data workflows, securing production processes, and eliminating any possible vulnerabilities from the start.  
  • Self-service analytics. DataOps brings data closer to people even if they do not directly participate in data workflows and provides everyone with the information they need. With optimized workflows, automation, and advanced business intelligence tools, it becomes much easier for business stakeholders to build their analytic pipelines, generate reports, create visualizations, and more.   

Now, the question is, “How do you unlock all these benefits for your company?”

Introducing your business to DataOps

Adopting DataOps can be a meticulous process that depends on your corporate strategy, resources, and other factors. However, with almost three decades in the IT business, we have identified several practices that can help your enterprise migrate successfully.   

  1. Democratize your data. In other words, make it transparent to business stakeholders. Faster access to accurate and timely info will facilitate the decision-making process and result in more precise business strategies.  
  1. Introduce the new culture of collaboration. DataOps is impossible when people work in siloed teams. Throw a few bridges between your IT operations, software engineers, and data specialists and show how they equally participate in creating high-quality data products.   
  1. Use appropriate tools. Automation does not exclusively belong to DevOps benefits. iCEDQ, ServiceNow, Ranorex, GitLab, Jenkins, or Bamboo will help you implement automatic testing at as many data flow stages as possible. We also recommend using a version control system (VCS) such as Git, CVS, or Mercurial. This will keep the code associated with your data pipeline in one place and let your data specialists safely work with the same code simultaneously (branching and merging). This way, you will speed up testing, bug fixing, and new features implementation. 
  1. Take advantage of DataOps platforms. They combine multiple data collection and analysis tools, technologies, and practices in a single work environment that your team members have access to. You can choose from many solutions: DataKitchen, Delphix, IBM, Lumada, and more.   

In a nutshell

The trend towards the increasing amounts of data that companies produce is likely to intensify in the upcoming years. Without DataOps, businesses won’t be able to extract valuable business insights from the overwhelming quantity of information acquired. Hence, if you want to gain accurate business intelligence, develop adequate business strategies, and boost your team’s productivity, adopting DataOps is one of the best decisions you can make in this regard.