DataOps, a relatively new concept, currently has a wide variety of definitions. However, the term DataOps (data operations) was first coined in 2014 by journalist Lenny Liebmann. He described DataOps as the set of best practices that improve coordination between data science and operations.
Gartner defines DataOps as “a collaborative data management practice, really focused on improving communication, integration, and automation of data flow between managers and consumers of data within an organization.” In recent years, this definition is more widely used in the global data community.
The Growing Value of DataOps in the IT Space
We are living in a Digital Age where data and technology drive every business. Naturally, enterprises are heavily investing in information technology (IT) to ensure the productivity, efficiency, and innovative ventures of their data teams. Chief Data Officers (CDOs) are expected to deliver value to the business by efficiently utilizing available data, responding to demands, and ensuring the productivity of teams while taking care of every data management process.
The rapid growth of data volume and data types, along with different types of data citizens—from users to data scientists—turn data management and delivery into delayed processes. As a result, most businesses and CDOs fail to efficiently use the data collected to create value or deliver insights on time.
The following are some of the challenges currently faced by various enterprises.
Massive load of complex data
The rise of Big Data compels every business to work with large volumes of complex data from various sources in different formats. Notably, in larger organizations that deal with financial transactions and customer relationship management (CRM) data, the data landscape is much more complex, with tens of thousands of data sources and formats.
The overload of technology
To create value for any business, the data itself needs to be in an easily comprehensible format. That is the reason every type of data an enterprise gathers should be profiled, cleaned, transformed, and stored safely. It is to ensure data quality, integrity, and relevance.
All these processes are also critical for complying with data governance regulations and policies. To handle all of these processes, an enterprise might be using various technological tools from data cataloging and profiling tools to data analytics and reporting tools. All this leads to technology overload on an enterprise.
The diversity of the workforce
The skill sets and roles of humans using the tools and technologies to work on the data are also diverse.
For instance, data preparation and transformation is the primary concern of data engineers, while data scientists are concerned about capturing the correct data for their analytics engine. Similarly, the job of data analysts is to create reports and visualizations. At the same time, the IT department is responsible for maintaining data access protocols and guaranteeing data quality, integrity, and security. On the business user side, managers depend on data to determine the growth of the business.
Bringing together these diverse processes, technologies, and people is an expensive yet complex task. It can also sometimes create friction between teams, which is why an enterprise needs a DataOps framework in place. From collection to delivery, DataOps bring speed and agility to the end-to-end data pipelines process.
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How Businesses Currently Implement DataOps
DataOps has been helping enterprises transform their data management and data analytics processes. For instance, DataOps enables data teams to quickly spin up siloed testing environments to make more room for experiments and innovations.
Developers typically create applications with smaller test databases. On the other hand, data scientists and analysts may need to spin up a sandbox environment that includes applications along with massive volumes of data, usually in hundreds of terabytes. With the implementation of intelligent DataOps strategies, such as predictive analytics, automation, and cloning, organizations can easily spin up massive disposable data environments.
DataOps principles and strategies also enable businesses to work on their large production datasets, an unimaginable process just a few years ago. For instance, DreamWorks Animation LLC, an American animation studio, can easily share the datasets of its animated films in development with creative teams spread throughout the world. It enables rapid collaboration and dramatically shortens the production span.
Another example is Genuity Science (previously WuXi NextCODE), a genomics company. The company developed a genome platform that can compare human DNA containing millions of bits of data and integrate it on the DNA of a fly. This process was meant to explore the mutations that may cause cancer or other rare diseases.
But your enterprise doesn’t have to be an animated film studio or a genomics company to benefit from DataOps. Every company that requires actionable business intelligence in a timely manner will benefit from DataOps in six major ways.
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Six Ways Businesses Can Utilize the Benefits of DataOps
The primary goal of DataOps is to expel data silos and make the data teams capable enough to interpret the value of each data process, manage them efficiently, and centralize them.
Improved efficiency of the workforce
Basically, DataOps is all about automation and process-oriented methodologies that improve the efficiency of the workforce. By bringing testing and observation mechanisms into the analytics pipeline, employees can focus on strategic tasks instead of wasting time on analyzing spreadsheets or other mundane tasks.
Data with better quality
DataOps creates automated, repeatable processes and automates code checks and controlled rollouts. This practice can bring down the chances of human errors that may get distributed to multiple servers, produce inaccurate results, or take down the entire network.
Faster access to actionable business intelligence
DataOps enables faster yet easier access to actionable business intelligence. This agility is possible because DataOps combines the automation of data ingestion, processing, and analytics along with the elimination of data errors. DataOps can also instantly deliver valuable insights into customer behavior patterns, shifts in market trends, and volatility.
A macro perspective of dataflow
Beyond day-to-day business insights, DataOps can provide a macro perspective of the entire data flow over time, across the organization and out to users. The process reveals macro trends, such as the adoption rate of features or services and search patterns. Teams that use manual processes and constantly react to anomalies and errors in data can’t develop such a macro view of data flow, data management, and data analytics processes.
An easy cloud migration process
A DataOps approach can significantly benefit a cloud migration project. The DataOps process maximizes business agility as it applies DevOps, Agile Development, and lean manufacturing. A DataOps platform automates workflows on both your on-premises and cloud workplace. It can help your enterprise organize its data by virtually eliminating errors, bringing down product lifecycle span, and helping data teams and stakeholders collaborate seamlessly.
Enhancement of DataOps careers
DataOps is a rapidly growing professional arena. Excellent career benefits await the professionals in the data analytics and operations space who are willing to learn the implementation and management of DataOps processes. They can turn into the leaders of the next generation of data teams, and at least for the next decade, they can set the standard for data practices. Moreover, an innovative and rapidly growing enterprise that eliminates repetitive and monotonous business tasks can enjoy increased employee satisfaction and retention.
Leverage DataOps to Become Truly Data-Driven
According to the estimates, data-driven enterprises are 23 times more likely to win customers, six times more likely to retain them, and 19 times more likely to boost profits. These figures are convincing enough to accelerate your digital transformation journey to start generating value out of your data. DataOps is an approach that can help you in this endeavor and enable your organization to make the most out of data more effectively and efficiently.
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