Today’s businesses need fast and reliable access to enormous quantities of data from numerous sources. They also require data teams that can field proposals and deploy analytics with little interruption. DataOps, as you will soon learn, promises to accomplish just that.
Because in reality, data processes within organizations are typically inefficient and error-prone. Data scientists spend upwards to 80% of their time cleansing rather than mining or modeling data. This leads to long “data pipelines” before releasing necessary data and analytics to other members of the organization.
This need for speed, if you will, has prompted the advent and rapid growth of DataOps, a framework that aims to solve many of the all-too-common data problems faced by today’s organizations. The ultimate goal of DataOps is to help data teams deliver higher quality data faster.
DataOps, short for “data operations”, is an automated, process-oriented methodology to improve the quality and reduce the end-to-end cycle time of data analytics. Analytics and data teams are increasingly using the DataOps framework to produce high-quality data faster.
DataOps applies the rigor of software engineering to the development and execution of data pipelines. It aims to speed up the delivery of data and analytics to organizational stakeholders, leading to better, cheaper, and faster access to data.
According to Wayne Eckerson, a thought leader in the business intelligence and analytics community and founder of Eckerson Group, “DataOps promises to take the pain out of managing data for reporting and analytics.”
The DataOps methodology aims to maximize the business value of data and improve customer satisfaction via:
Data teams can accomplish using the DataOps framework using DataOps tools. There are many such tools available growing market today. These DataOps platforms help foster the collaboration that is critical to scale development teams. They also help facilitate data pipeline orchestration, testing and production quality, deployment automation, and data science model deployment/sandbox management.
There are numerous types of DataOps tools available today. These DataOps tools span from all-in-one tools that bundle most of the components necessary to build, test, monitor and deploy data pipelines to component-specific tools that satisfy a single component required to create, execute and manage data pipelines.
Because DataOps is an ever-evolving framework with a relatively immature marketplace, there are a plethora of new tools being introduced. Older data tools and tech are also being revamped with newer technology. In fact, International Data Corporation (IDC) predicted that through 2020, “spending on self-service visual discovery and data preparation tools will grow 2.5x faster than traditional IT-controlled tools for similar functionality.”
Finding the right tools for your data team involves undertaking a multi-step evaluation process. These technologies are not cheap, so it’s crucial to take a deep dive into each prospective vendor’s offerings when evaluating tools for your organization.
At Temberton Analytics, our first step involves conducting a needs assessment with the client. This is where we gain an in-depth understanding of what problems our client aims to solve with DataOps processes. Then we consult with vendors, ask for a proof of concept (POC), then finally implement the tools and train our clients on their use.
Ready to simplify your data team’s processes with DataOps? Contact Temberton Analytics today to learn more about how we can help build the right solution for your organization.
Temberton Analytics has over 25 years of proven experience providing cutting edge data management, data mining, and statistical modeling techniques to measure, understand, and grow businesses.