A Guide to DataOps: The New Age of Data Management

A recent survey on big data challenges faced by businesses uncovered some startling facts about data utilization. 38% of the businesses “lack” a compelling business case to use their data. 34% of the companies did not have processes mature enough to handle big data technologies, and 24% of them are incapable of making big data usable for their end-users!

To call these findings shocking would be an understatement. If the survey’s results are true, then a large percentage of the businesses do not know what they can do – they must do – with the data they have and continue to collect from their customers. This puts them at a severe disadvantage in comparison to their competition.

In a data-driven competitive landscape, ignoring the benefits of data, or even the inability to extract its fullest potential, can only mean a disastrous end for organizations. To be sure, many of these organizations are collecting plenty of data. They just don’t want, know, or have the processes in place to use it.

Part of the problem is legacy data pipelines. As data moves from source to target in the data pipeline, each stage has its own idea of what that data means and how it can be put to use. This disconnected view of data renders the data pipelines brittle and resistant to change, in turn making the organizations slow to react in the face of change.

The solution to this challenge is DataOps.

What is DataOps?

DataOps, short for data operationalization, is a collaborative data management approach that emphasizes communication, integration, and automation of data pipelines within organizations.

Unlike data storage management, DataOps is not primarily concerned about ‘storing’ the data. It’s more concerned about ‘delivery’, i.e., making the data readily available, accessible, and usable for all the stakeholders. Its goal is to create predictable delivery and change management of data, data models, and related artifacts to deliver value faster across the organization and to consumers.

DataOps achieves this goal by employing technology to automate the design, deployment, management, and delivery of data in order to improve its use and the value it offers. This makes it easy for all stakeholders who use data to access the data and also accelerates the cycle time of data analytics.

In doing so, DataOps drastically improves the response time of organizations to market changes and enables them to evolve to the challenges faster.

Challenges and Problems that DataOps Resolves

The most important promise of big data – quick and reliable data-driven actionable business insights – remains unfilled because of numerous challenges that can be broadly classified into organizational, technical, and human (people using the data) challenges.

DataOps helps overcome these challenges by combining learnings and practices from Agile, DevOps, and Lean Manufacturing methodologies. Here are the most important challenges DataOps tackles head-on:

• Speed

Modern organizations rely on (at least, must rely on) data coming from many different sources and in many different forms. Cleaning, improving, and then using the data can be such a complex and drawn-out process that when the insights that are finally generated from it, they are no more relevant to the rapidly evolving business landscape.

DataOps radically improves the speed at which insights are obtained from data. 

• Data Type

Sometimes, the data collected by organizations can be in unstructured formats, which makes it extremely difficult to extract insights from them. It’s entirely possible and even likely that such data sources may offer clues to emerging business challenges. Therefore, it’s not enough that organizations can crunch easily crunchable data in structured formats.

DataOps makes it possible for organizations to identify, collect, and use data from every data source available at their disposal.

• Data Siloes

DataOps breaks down data siloes within organizations and centralizes all data. At the same time, it builds resilient systems that enable self-service for every stakeholder who needs access to data. These systems evolve with the changes within the organization and outside it, and yet, give the “data users” predictable ways to find and use data that they need.

Business Benefits of DataOps

By overcoming the challenges, DataOps makes it possible for DataOps teams to deliver data to everyone who needs it – data engineers, data scientists, ML engineers, and even customers – and do it much faster than before. This achievement unlocks several benefits for data-driven businesses. Here are some of them:

• Maximizing Data Utilization

DataOps unlocks data for all “users” of data, be it analysts, executives, or even customers. It automates data delivery and in doing so, allows every department to extract maximum value from the data. The result is improved competitiveness, responsiveness to changes, and higher ROI.

• Right Insights at the Right Time

A common problem with big data so far has been right insights at the wrong time. Insights that arrive too late are useless. DataOps brings data to everyone who needs it quickly. Consequently, they can make more informed decisions faster than ever before, enabling the organization to evolve to market changes at a rapid pace.

• Improved Data Productivity

DataOps employs automation tools to operationalize data delivery as self-service. Consequently, any inherent latency between data requests and data access is eliminated, thereby allowing all teams to make data-driven decisions promptly.

DataOps also rids the organization of manual data pipeline change management processes. Instead, all changes to data pipelines are streamlined and automated to deliver quick, targeted changes.

• Data Pipelines Optimized for Results

DataOps incorporates a feedback loop into the data pipelines, which allows various data consumers to identify the specific data they need and obtain customized insights from it. Each team can then use these insights to reduce costs, discover new opportunities, increase revenue, and improve the organization’s profitability.

Principles of DataOps

Technology-wise, DataOps realizes one of the most groundbreaking milestones for organizations – making their data programs highly scalable without compromising the speed or quality of data analytics. Because it borrows lessons and practices from DevOps, DataOps overlaps with the former in many crucial ways. This is visible in the three fundamental principles of DataOps:

• Continuous Integration

DataOps identifies, collates, integrates, and makes available data coming from a variety of sources dynamically. When teams add new data sources for DataOps teams to process, the new data is automatically integrated into the data pipelines and made available to various stakeholders using AI/ML tools.

Thanks to automation, everything from data discovery to data curation, transformation, and customization of insights is fully streamlined. Indeed, data delivery can be made in real-time streams directly to predictive algorithms for delivery in-the-moment insights to users, esp. the consumers.

Such an optimized data integration process ensures that there’s no time wasted between data discovery and data utilization.

• Continuous Delivery

Organizational data is only as valuable as the insights generated from it. The more teams have access to it, the more insights are extracted from it. However, data accessibility also comes with data governance challenges. DataOps operationalizes data governance across the organization while democratizing data accessibility and enhancing its security and privacy.

Data is purposefully delivered to internal and external data consumers in a collaborative fashion that meets the internal data quality and data masking rules. Oftentimes, an “intelligent” data platform is used to realize this objective. When the quality, privacy, and security of the data are ensured, various stakeholders can use it to obtain accurate insights from it without having to worry about data governance implications.

• Continuous Deployment

Digital businesses rely on a flurry of data-driven apps to make real-time decisions in function that have far-reaching implications on the organization’s future. Mission-critical functions such as fraud detection, AI chatbots, sales, supply chain management, and so on, require the most up-to-date data readily available for decision-making. Continuous deployment makes access to fresh data seamless for all users.

DevOps vs. DataOps

Although DataOps borrows knowledge and operational processes from DevOps, the two of them differ significantly from each other. Here’s how:

• The Human Factor

Although DataOps participants may be tech-savvy, they are more focused on creating algorithms, models, and visual aids for data users. On the other hand, DevOps participants are software engineers with an operational mindset.

• Process

DataOps processes are characterized by data pipeline and analytics development orchestration, while there is little orchestration involved with DevOps processes.

• Testing

Unlike DevOps, DataOps relies heavily on data masking for testing purposes, and therefore, test data management becomes crucial. Also, DataOps typically tests and validates data at both data pipeline and analytics development processes before deployment.

• Tools

DevOps enjoys a mature tools ecosystem, esp. for testing. DataOps, being a new approach, often requires teams to build tools from scratch or modify DevOps tools for their purpose.

Evolution of DataOps Platform

In the early days of data analytics, ETL (extract, transform, load) tools emerged as capable tools to manage large volumes (relatively speaking) of incoming data. However, as the variety, veracity, and volume of the incoming data exploded, the need for scalability and high-speed data analytics become more urgent. The deficiencies inherent in data connectors proved to be a limiting factor too.

The emergence of the cloud would solve the challenge of data ingestion, management, and analytics. When ETL tools were combined with cloud resources, it accelerated the speed of analytics. Yet, a mounting challenge persisted – data accessibility. It wasn’t enough that the data was being used to generate insights; everyone should be able to get access to those insights.

And, DataOps was born!

DataOps democratized data access. Instead of a select few people having access to data, all stakeholders get access to secure, quality data subject to the organization’s data governance policies.