Updated: Aug 29, 2021
Most organizations accumulate data but cannot use it to derive value or offer insights on time. Further, the volume and types of data continue to grow, as do the various types of data personas, ranging from data scientists to business users. Therefore, data management and delivery become significant roadblocks. This is where DataOps comes into the picture.
DataOps — An Overview
DataOps, also referred to as Data Operations, is a set of practices that accelerates and brings agility to end-to-end data pipelines processes — ranging from data collection to data delivery. The term DataOps is still at its nascent stages of awareness and adoption, which is why it comes in many working definitions.
Research leaders such as Gartner and MIT have focused on enhancing communication between data stakeholders and deploying automation tools within data flow and lifecycles to improve delivery practices. Others are simply describing DataOps as “DevOps for data analytics.”
According to tech firm IBM, DataOps is the “orchestration of people, process, and technology” to provide top-notch, trusted data to data engineers and data scientists fast. This practice promotes collaboration across a company to bring speed, agility, and new data measures and techniques at scale. With the power of automation, DataOps can solve inefficiencies in evaluating, preparing, embedding, and make data available. However, it is essential to know that DataOps isn’t a product, a single event or step, or a particular team or person.
Members Involved in DataOps
The majority of DevOps-based companies already have the nucleus of a DataOps team at bay. Once they have recognized projects that required data-intensive development, they only need to add someone with data training to the team. This particular person may even be a data engineer or a full-fledged data scientist.
At times, teams are built with people who have overlapping skills or people who take on different roles with a DataOps team based on their skills and expertise. In big projects, a specific DataOps role may include more than one person. However, it is also expected that some people will cover more than one role.
Operations and software development skills may overlap, and sometimes, people with software engineering experience may also be eligible for the role of a data engineer. Even data scientists have data engineering skills. It’s unlikely, however, to see overlap between data operations and data science.
Some of the major areas of data operations include:
● Integration and data pipeline
● Data quality rules deployment
● Data security and privacy
● Data to process orchestration and automation
● Data and model integration
Irrespective of makeup, data operations teams must have a common objective: the data-driven requirements of the services they support.
According to Michele Goetz, VP and principal analyst at Forrester, DataOps team members include:
● Data engineers, who provide ad hoc and system to business applications, business intelligence (BI), and business analytics
● Principal data engineers, who are developers handling product and customer-centered deliverables
● Data specialists, who support the overall data scenario and development best practices
Technical Challenges Involved in Implementing DataOps in Organizations
Adopting DataOps needs a blend of technical investment, change management, and organizational restructuring. It needs people to change how they handle work, which isn’t going to happen in a day.
Challenges to the adoption of DataOps in companies today can be divided into technical and human-based challenges. In both cases, DataOps citizens recommend making changes one step at a time. For example, companies can benefit from implementing a minimum viable product (MVP) and improving it over time. Likewise, enterprise data teams can profit from adopting DataOps best practices in a set of incremental steps.
Technical infrastructure and tooling are essential when it comes to DataOps success. DataOps teams need platforms that help break down data silos, orchestrate data governance practices, oversee data pipelines, and promote data management process automation. In any case, companies appear to be facing many challenges in putting effective systems in place to accomplish all of the above.
In a survey involving 100 European data and analytics leads, teams reported facing several technical challenges as they begin their DataOps journeys. Large organizations exist in one place where they have whatever data systems they have had in their offices since the beginning. However, across enterprises, there seems to be an operational gap when it comes to solving data quality challenges.
Challenges orchestrating code and data tools overall and difficulty managing the end-to-end data environment are some of the leading DataOps issues that data teams across enterprises face today. Around 44% of the participants cited the information. Approximately 22% cite challenges on behalf of insufficient levels of investment in automation tools, and 18% cite challenges related to building rigorous quality assurance tests upfront. Similarly, 31% report that a lack of investment in DataOps tools is thwarting their DataOps initiatives.
Another challenge is that all six components are engineering heavy skills that require highly skilled, highly paid, and very scarce Data Engineers with various technical skills. For example: In the US, there are 63K Data Engineers, 14K open roles for Data Engineering, 22% unfulfilled Data Engineering roles at any time. UK 14K Data Engineers, 1710 roles, 12%. There is a massive gap between demand and supply of Data Engineers, and SMEs and mid-market can't afford to hire expensive Data Engineers.
Success Factors for DataOps
DataOps provides better collaboration and data delivery, and insights in real-time to applications and decision-makers. In any case, well-established siloed systems and risk-free corporate culture prevent enterprises from deploying and understanding the benefits of DataOps, thereby affecting data democratization.
Most organizations are unprepared for DataOps, primarily because of behavioral standards — such as territorial data hoarding — and since they lack technical skills, they are often stuck with bulky ETL and master data management (MDM) systems. Further, with the arrival of new and more advanced technologies related to data, furthering the lack of IT skills, companies are forced to recruit from a smaller talent pool.
Moreover, it’s crucial to acknowledge the need for enterprise leaders to be involved, alongside the need for employee buy-in. DataOps needs a culture change, which is why executives must back the initiative. Moreover, it’s equally crucial for teams across the organizations to support it.
Data is siloed, incoherent, and mostly inaccessible across many organizations. There needs to be a process or approach to map IT capabilities to the business functions they support and identify how people, technologies, processes, applications, and data interact to guarantee alignment in accomplishing organizational goals. This requires an in-depth understanding of enterprise architecture and improving data intelligence to better understand the data ecosystem.
With DataOps, data becomes more democratized, enabling enterprises to act “data first” and be more competitive, be on top of the silo, and drive growth.
The Bottom Line
Data operations can help data-driven organizations serve the rest of the business more effectively. However, companies will generally need an iterative practice to overcome the technical challenges standing between them and DataOps maturity.
There is so much hype around data science and citizen data science. Even though over 70% of the efforts to get ROI are spent on understanding data quality, data collection, manual cleansing, data governance, data generation, otherwise referred to as DataOps. Citizen data science cannot be achieved without DataOps. But the major challenge is the complexity and the high cost involved to establish DataOps. This is where Quantumics.AI comes into the picture.
A no-code self-service data platform, Quantumics.ai is the world’s first Citizen DataOps platform engineered for business users where everyone can work with their data without having to write a single line of code. Moreover, it provides a self-servicing data toolkit for focusing on ideas and their implementations. With Quantumics.ai, your journey to DataOps will be seamless and effective.
When done right, businesses with Citizen DataOps initiatives are more likely to see early success in their data analytics capabilities and achieve data democratization.