K
Kathleen Martin
Guest
DataOps will soon become integral to data engineering, influencing the future of data. Many organizations today still struggle to harness data and analytics to gain actionable insights. By centering DataOps in their processes, data engineers will lead businesses to success, building the infrastructure required for automation, agility and better decision-making.
DataOps is a set of practices and technologies that operationalizes data management to deliver continuous data for modern analytics in the face of constant change. DataOps streamlines processes and automatically organizes what would otherwise be chaotic data sets, continuously yielding demonstrable value to the business.
A well-designed DataOps program enables organizations to identify and collect data from all data sources, integrate new data into data pipelines, and make data collected from various sources available to all users. It centralizes data and eliminates data silos.
Operalization, through XOps including DataOps, adds significant value to businesses and can be especially useful to companies deploying machine learning and AI. 95% of tech leaders consider AI to be important in their digital transformations, but 70% of companies report no valuable return on their AI investments.
With the power of cloud computing, business intelligence (BI) – once restricted to reporting on past transactions – has evolved into modern data analytics operating in real-time, at the speed of business. In addition to analytics’ diagnostic and descriptive capabilities, machine learning and AI enable the ability to be predictive and prescriptive so companies can generate revenue and stay competitive.
(R-Type/Shutterstock)
However, by harnessing DataOps, companies can realize greater AI adoption—and reap the rewards it will provide in the future.
To understand why DataOps is our ticket to the future, let’s take a few steps back.
Why Operationalization is Key
A comprehensive data engineering platform provides foundational architecture that reinforces existing ops disciplines—DataOps, DevOps, MLOps and Xops—under a single, well-managed umbrella.
Without DevOps operationalization, apps are too often developed and managed in a silo. Under a siloed approach, disparate parts of the business are often disconnected. For example, your engineering team could be perfecting something without sufficient business input because they lack the connectivity to continuously test and iterate. The absence of operationalization will result in downtime if there are any post-production errors.
Continue reading:https://www.datanami.com/2022/08/31/why-dataops-centered-engineering-is-the-future-of-data/
DataOps is a set of practices and technologies that operationalizes data management to deliver continuous data for modern analytics in the face of constant change. DataOps streamlines processes and automatically organizes what would otherwise be chaotic data sets, continuously yielding demonstrable value to the business.
A well-designed DataOps program enables organizations to identify and collect data from all data sources, integrate new data into data pipelines, and make data collected from various sources available to all users. It centralizes data and eliminates data silos.
Operalization, through XOps including DataOps, adds significant value to businesses and can be especially useful to companies deploying machine learning and AI. 95% of tech leaders consider AI to be important in their digital transformations, but 70% of companies report no valuable return on their AI investments.
With the power of cloud computing, business intelligence (BI) – once restricted to reporting on past transactions – has evolved into modern data analytics operating in real-time, at the speed of business. In addition to analytics’ diagnostic and descriptive capabilities, machine learning and AI enable the ability to be predictive and prescriptive so companies can generate revenue and stay competitive.
(R-Type/Shutterstock)
However, by harnessing DataOps, companies can realize greater AI adoption—and reap the rewards it will provide in the future.
To understand why DataOps is our ticket to the future, let’s take a few steps back.
Why Operationalization is Key
A comprehensive data engineering platform provides foundational architecture that reinforces existing ops disciplines—DataOps, DevOps, MLOps and Xops—under a single, well-managed umbrella.
Without DevOps operationalization, apps are too often developed and managed in a silo. Under a siloed approach, disparate parts of the business are often disconnected. For example, your engineering team could be perfecting something without sufficient business input because they lack the connectivity to continuously test and iterate. The absence of operationalization will result in downtime if there are any post-production errors.
Continue reading:https://www.datanami.com/2022/08/31/why-dataops-centered-engineering-is-the-future-of-data/
