Enterprises have struggled to collaborate well around their data, which can impact everything from digital transformation to advanced concepts like AI and ML. DataOps is not without challenges; building, managing, and scaling data pipelines requires careful thought around reusability, portability across infrastructure and applications, and long term maintenance and governance. And these are just a few of the issues facing enterprises. For this reason, DataOps technology stacks need to focus on providing key capabilities include data extraction, integration, transformation, and analysis.
DataOps – Why is it important?
The enterprise is undergoing a seismic shift from a siloed application development and data repositories, to a more composable and reusable architectures. Additionally, there is a growing demand for speed and agility, as well as the influx of disruptive technologies, such as the cloud, IoT, and AI. Organizations that desire to win big must act and adapt quickly to deploy and scale new software and solutions that provide customers with a superior experience and satisfy their rapidly evolving needs. To do that, they also must be able to rapidly aggregate, integrate and analyze data sources.
Even with this need for speed, the tools and processes for the creation and processing of data are not standardized in a way that promotes rapid innovation and ultimately help organizations transform. DataOps is critical to address the challenges involved in acquiring, storing and governing data. In addition, companies are dealing with increasing complexity in their IT environments. In a recent survey, more than 80% of enterprises have a hybrid cloud or multi-cloud strategy. In order to have cost effective and secure management of increasingly large amounts of data, enterprises must adopt DataOps.
DataOps for managing data growth
DataOps has been around for about a decade but has recently gained momentum because of the overwhelming challenge companies face today in dealing with a large, complex set of data that is being generated at increasingly faster rates. With new technologies like Internet of Things (IoT), cloud computing and the power of Big Data now integrated into everyday use, companies are generating at least 50 times more data than they were just five years ago. And with more data comes a need for greater efficiency, and data experts are in high demand.
When we first heard about DataOps and thought about its potential impact on businesses, we were excited but thought of it as a radical new methodology. What we didn’t realize then was that many companies had been using similar practices to deal with some aspects of data management, particularly around data warehousing and analytics. And just like DevOps, DataOps isn’t a product, but rather a cultural shift supported by many products – many existing products. So it’s important to for businesses looking to adopt DataOps to consider what they already have, such as enterprise data warehouses and ETL tools, and what they may need to acquire, replace or modernize. In the end, companies will end up with several systems to support the data pipeline.
DataOps and big data challenges
Most enterprises have invested in significant data infrastructure in order to extract, load, and store their data to take advantage of Big Data analytics and technologies. However, often these infrastructures are layered, hard to manage, and full of legacy tools which hinder the transfer and integration of data. Additionally, organizations have invested significant resources in tools such as data warehousing, data warehouses, data marts, and data marts. These data warehouses have been utilized to model data but have been typically implemented with a predefined data model. A business intelligence (BI) system, an enterprise data warehouse, or any other set of tools might assist in the execution of a data transformation job.
The technological revolution is creating unprecedented opportunities to make things happen with data. By analyzing the data that powers modern business, we are able to provide value in every dimension of our organizations. As is the case with every transformation, the benefits of building an integrated, self-service analytics environment will be realized by “users” rather than IT. Leveraging the power of data science, business leaders can achieve operational excellence and compete effectively against the growing waves of competitors. For the data science and analytics staff to be successful, they must be exposed to all of the necessary technology and processes to support effective use of data.