Many businesses face the challenge of getting the right data to the right people at the right time.

Business systems obtain large volumes of data from various sources constantly. They need to mine key quality data to support critical decision-making. This embodies data management challenges ranging from procurement to high volume storage and transactions to deriving insights while making the process time cost-efficient.

The DataOps paradigm helps gain business agility by transferring and pushing data from various sources into a centralized platform.

Many businesses claim that some potential transformations didn’t gain fruition because 97.5% of their data never made it into the designated teams’ hands.

With DataOps, “what if” scenarios relating to data usage will diminish because of how the right data will empower teams.

What is DataOps?

DataOps is a data management framework influenced by the agile methodology, lean manufacturing, and DevOps to democratize data, build trust, and improve team collaboration.

This collaborative data management practice focuses on improving communication, integration, and automation of data flow. It helps to make data collection, analysis, integration, and implementation seamless.

Purpose of DataOps

There are many central objectives of DataOps. They are to speed-up data processes, automate the data engineering functions (data gathering, assembly, and curation), streamline the ingestion of data from various sources into centralized or distributed big data platforms, and provide business users with quality-driven data consumption.

DataOps takes the existing best practices of DevOps for continuous data integration, builds upon the recursive process using agile principles, and mandates quality checks/metrics at every step within the CI/CD pipeline.

According to a survey of 1,300 executives by CA Technologies in 2019, companies embracing DataOps and agile practices are experiencing a 60 percent increase in revenues and profit growth. They are 2.4 times more likely than their mainstream peers to grow their businesses at over 20 percent.

Businesses need to know the specifics of how to achieve this when data has become denser, diverse, and distributed.

The Need for DataOps

At Radiant Digital, we have identified a clear path to completely unleash the full power of the DataOps Advantage. Here’s why your organization will need the DataOps Advantage.

1. To bring better focus on the quality of data and its evolution through the best practices.

2. To ensure data governance and security while being used, reducing operational risks, and enabling new capabilities.

3. To improve business scale that allows the business value from relevant and timely data.

4. To move towards becoming a data-first organization that promotes collaboration among data consumers and data creators to drive value across the enterprise.

Challenges addressed by DataOps

Bad Data Quality

Low-quality data loses credibility in the entire analytics setup. Diverse data formats, data types, and schemas can cause integration complexities and data errors. These include duplicate entries, schema changes, and feed failures that can be difficult to trace and manage. Also, constant updates in the data pipeline need continuous validation, which is time-consuming.

Data Silos

The increased number of data pipelines with requirements from data analysts, scientists, and data-intensive applications have resulted in data silos disconnected to external pipelines, data generators, and datasets. As data resides in different systems and platforms, gaining access and control over them and identifying the right data is daunting.

Data Processing

A data pipeline includes integration, testing, and analytics that are overbearing. Data scientists work long hours to make sense of the data collected and segregate it. The automation of data analytics and other data handling processes can make the processes less complicated, more reliable, fast, and quality-centric for ETL engineers, data analysts, and data scientists.

What can Organizations achieve with DataOps?

DataOps helps organizations become more data-driven with emphasis on quality. Business Owners can make informed decisions using validated data, while data Scientists can develop models based on a catalog of information rather than investing in time-consuming Data Mining activities.

Benefits from DataOps

Streamlined Processes

Teams can adopt toolchains and workflow automation when dynamic data is introduced to systems from multiple sources for transformation, modeling, visualization, and reporting.

Better Collaboration

DataOps helps channelize data to the right systems and teams to promote self-service data solutions and better collaboration and productivity solutions. This mitigates the risks of slow data operations, data silos, and obsolete data.

Reliable Automation

In addition to data capture and mining, DataOps supports simultaneous activities like the generation of dynamic code, testing, modeling, and enhancements to the existing code/tools that utilize data.

This can be leveraged using fixed datasets and containerized environments with automatic versioning to enable developers, testers, analysts, and engineers to speed up the changes to production and the pipeline’s feedback mechanism.

The other significant advantages include:

  • Improved team collaboration through a shared understanding of data
  • Improved data management efficiency and quality through statistical process control (SPC)
  • Quick TAT for defect detection and bug-fixing
  • A quick and effective response to new requests
  • Helps avoid catastrophic scenarios through predictive data analytics and recourse
  • Better operations and support

Important Considerations

If your teams are working on a hybrid data ecosystem, here are some requirements met by DataOps.

DataOps automates a subset of Operations in the Data Management Process.

A standard DataOps process consists of the following stages and aspects.

Wrapping up

Data teams would not only deliver with DataOps principles but deploy and help businesses make decisions on-the-go. It ensures that this is done without compromising data quality or integrity at your business’s speed.

However, multiple challenges come from converting your data into value through DataOps without expert guidance.

At Radiant Digital, we help you overcome them and discover the full potential of your organization’s data. Connect with us to learn more.

Learn more about the Principles, Processes, Framework, and the Transformation Journey for DevOps in our next blog.  


by Sri Arepally, Radiant Digital
Practice Director, Big Data


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