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In todays data age, getting data analytics right is more essential than ever. A robust data analytics implementation enables businesses to hit key performance metrics, build data and AI-driven customer experiences (think personalize my feed) and capture operational issues before they spiral out of control. The list of competitive advantages goes on, but the bottom line is that many organizations successfully compete based on how effectively their data-driven insights inform their decision-making.

Unfortunately, implementing an effective data analytics platform is challenging due to orchestration (DAG alert!), modeling (more DAGs!), cost control (Who left this instance running all weekend?!) and fast-moving data landscapes (data mesh, data fabric, data lakehouses ). Enter DataOps. Recognizing modern data challenges, organizations are adopting DataOps to help them handle enterprise-level datasets, improve data quality, build more trust in their data and exercise greater control over their data storage processes.

DataOps is an integrated and agile process-oriented methodology that helps businesses develop and deliver effective analytics deployments. It aims to improve the management of data throughout the organization.

While there are multiple definitions of DataOps, below are common attributes that encompass the concept while going beyond data engineering. Heres how we define it:

We broadly define DataOps as a culmination of processes (e.g., data ingestion), practices (e.g., automation of data processes), frameworks (e.g., enabling technologies like AI) and technologies (e.g., a data pipeline tool) that help organizations to plan, build and manage distributed and complex data architectures. DataOps includes management, communication, integration and development of data analytics solutions, such as dashboards, reports, machine learning models and self-service analytics.

DataOps is attractive because it eliminates the silos between data, software development and DevOps teams. The very promise of DataOps encourages line-of-business stakeholders to coordinate with data analysts, data scientists and data engineers. Via traditional agile and DevOps methodologies, DataOps ensures that data management aligns with business goals. Consider an organization endeavoring to increase the conversion rate of their sales leads. In this example, DataOps can make a difference by creating an infrastructure that provides real-time insights to the marketing team, which can help the team to convert more leads. Additionally, an Agile methodology can be employed for data governance, where you can use iterative development to develop a data warehouse. Lastly, it can help data science teams use continuous integration and continuous delivery (CI/CD) to build environments for the analysis and deployment of models.

The amount of data created today is mind-boggling and will only increase. It is reported that 79 zettabytes of data were generated in 2021 and that number is estimated to reach 180 zettabytes by 2025. In addition to the increasing volume of data, organizations today need to be able to process it in a wide range of formats (e.g., graphs, tables, images) and with varying frequencies. For example, some reports might be required daily, while others are needed weekly, monthly or on demand. DataOps can handle these different types of data and tackle varying big data challenges. Add in the internet of things (IoT), such as wearable health monitors, connected appliances and smart home security systems, and that introduces another variable for organizations that also have to tackle the complexities of heterogeneous data as well.

OK, so, how can we make this a reality? First, to manage the incoming data from different sources, DataOps can use data analytics pipelines to consolidate data into a data warehouse or any other storage medium and perform complex data transformations to provide analytics via graphs and charts.

Second, DataOps can use statistical process control (SPC)a lean manufacturing methodto improve data quality. This includes testing data coming from data pipelines, verifying its status as valid and complete, and meeting the defined statistical limits. This enforces the continuous testing of data from sources to users by running tests to monitor inputs and outputs and ensure business logic remains consistent. In case something goes wrong, SPC notifies data teams with automated alerts. This saves them time as they dont have to manually check data throughout the data life cycle.

Around 18% of a data engineers time is spent on troubleshooting. DataOps enables automation to help data professionals save time and focus on more valuable high-priority tasks.

Consider one of the most common tasks in the data management life cycle: Data cleaning. Some data professionals have to manually modify and remove data that is incomplete, duplicate, incorrect or flawed in any number of ways. This process is repetitive and doesnt require any critical thinking. You can automate it by either setting customized scripts or installing a built-in data cleaning software tool.

Additional processes that can be automated via DataOps include:

To develop your own DataOps architecture, you need a reliable set of tools that can help you improve your data flows, especially when it comes to crucial aspects of DataOps, like data ingestion, data pipelines, data integration and the use of AI in analytics. There are a number of companies thatprovide a DataOps platform for real-time data integration and streaming that ensures the continuous flow of data with intelligent data pipelines that span public and private clouds. Looking to increase the likelihood of success of data and analytics initiatives? Take a closer look at DataOps and harness the power of your data.

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Where DataOps and Opportunities Converge - DevOps.com