Business leaders can achieve operational excellence and compete effectively against the growing wave of competitors using DataOps.
Companies today are facing an overwhelming challenge in dealing with large, complex data sets that are being generated at increasingly faster rates. In many cases, they are turning to DataOps for help.
Why? With a growing demand for speed and agility, as well as the influx of new and disruptive technologies like the Internet of Things (IoT), cloud computing, and the power of Big Data integrated into everyday use, companies are generating at least 50 times more data than they were just five years ago.
Additionally, the pandemic has spurred many organizations to become fully digital – adding the complexity of data access, use, and storage across the network with analytics on use, security, and other insight generation into the mix. According to a recent survey, more than 80 percent of enterprises have a hybrid cloud or multi-cloud strategy, further exacerbating this complexity for IT teams. With more data comes a need for greater Efficiency – and data experts are in high demand.
Organizations that want 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 – something they’ve long struggled to do, even when data was centralized in on-premises data centers. Now, data is distributed across multiple clouds and out to the edge with IoT, mobile and sensor devices. To further complicate matters, this data often needs to be used by large, dispersed workforces, which means data must be able to be delivered quickly and securely.
To manage this increasing complexity, many organizations are adopting DataOps teams, leveraging the same agile methodology as DevOps, which has transformed the speed and capabilities of software development teams over the last decade. Organizations continuously aggregate, transform, enrich, and deliver reliable data, often via automated processes, so that the business can make faster, data-driven decisions.
DataOps is critical to addressing the challenges involved in acquiring, storing, and governing data; it also provides enterprises with cost-effective options to securely manage increasingly large dispersed, dynamic and varied datasets.
DataOps for optimizing the use and value of data pipelines
The concept of DevOps has been around for a little over a decade, but DataOps has been around for less than three years (not counting Manifestos as origin dates, particularly for DevOps). In the early days of DevOps, it was thought of as a radical new methodology with resistance from IT and Developers for totally separate reasons. Developers were resistant to the scrutiny and teamwork required by small incremental, transparent changes that rapidly went through test to deploy, and IT was resistant to the rapid speed of change DevOps represented. All of this apprehension is in the past, and DevOps has now caught on with IT, development, quality assurance, and product management teams. In combination with agile and lean methodologies, the technologies, processes, and cultural changes have taken hold and only strengthened over the past two years through COVID-19.
Yet, with all the success of DevOps, the ability to rapidly develop, deploy and iterate on applications and their underlying IT environment, it doesn’t fully address the issues with the data associated with those applications or the data necessary to know if those applications are optimally delivering on the business objectives.
