DataOps: What You Stand to Lose — Part 1

The solution is to apply automation and self-service to delivery and operations in order to eliminate the data friction causing delays.

Adam Bowen

Sep 18, 2017

Some of the world’s largest companies are making huge strides to adopt DataOps to eliminate the data friction that is killing innovation in their organizations. DataOps is the alignment of people, process, and technology to enable the rapid, automated, and secure management of data. Its goal is to improve individual and team outcomes by bringing together those that need data with those that provide it, eliminating friction throughout the data lifecycle. In this five part blog series, I want to examine some notable things I have witnessed companies lose as a result of adopting DataOps in their organizations.

First up is Delays

Delays can be crippling across all industries, even down to seconds. A great example of this is Formula 1 racing where an “average” pitstop is around 3 seconds, and a great pitstop in under 2 seconds. When races are decided by hundredths of a second, a delay in the pitstop can be the difference between winning and losing. In the data-driven economy, even smaller delays can incur costs that are far more severe. In a report published by TABB Group in 2008, they estimate that a 5ms delay in a trading system costs a broker $20M.

While often not as dramatic, all businesses have experienced the effects of data delays. Businesses that experience delays in getting timely information into their analytics and business intelligence (BI) systems run the risk of making critical decisions based on expired or incomplete information. Perhaps even more common, application projects that experience delays in getting the required data for development and testing (DevTest) often suffer from missed release dates, dropped features, or sacrificed testing cycles.

The solution is to apply automation and self-service to delivery and operations in order to eliminate the data friction causing delays. The decades-old approach of submitting tickets and then waiting for a bucket brigade to eventually make data available to the consumer is no longer a viable strategy. Companies should be embracing technologies and policies that empower their data consumers to get the data they need, when they need it; eliminating manual human interaction wherever possible.

These same focus areas benefit the data operators by allowing them to codify their best practices and controls into a automated data delivery pipeline. This frees up data operators from the mundane, trivial, and costly tasks of escorting data from point a to point b, and allows them to focus their talents on the important business challenges. When DataOps is employed, companies can achieve Continuous Data Delivery and enable Data Democratization. The end result is that business analysts always have the fresh, or real time, data feeds required to make the correct decisions for the business, your application teams are no longer hostage to data delivery times into their sprints or schedules, and your IT and InfoSec teams are no longer inundated with ad hoc/emergency data-related requests.

Coming tomorrow, Defects