#82 DataOps shortens the time to data-driven value

The world around us requires companies to act faster and provide solutions faster and faster. This is what "time to market" term has come to measure. In short, we are measuring the time to market timespan and want to make it as short as possible to enter markets and start sales as fast as possible.

Businesses are looking for rapid turnaround insights into their data, which is structured, easy to understand and reliable.

To meet these seemingly impossible demands, data and analytics teams have had to evolve and develop new methodologies – enter DataOps.

Many organizations are adopting a discipline of DataOps where the ability to iterate and to fail fast are core tenets.

DataOps is not DevOps for data; rather, it is a combination of technologies and methods with a focus on quality for consistent and continuous delivery of data value, shortening the time to value of data analytics, data science, and machine learning pipelines.

Organizations that have deployed DataOps practices have reported a 49% reduction in the number of late-delivery incidents, according to IDC's DataOps Survey. Cloud-native solutions not only support iterative and agile approaches to data integration but also, more importantly, provide an opportunity to fail and recover fast.

DataOps can reduce the problematic data prep time

The 80/20 rule has long been touted in the world of data and analytics: Analysts spend 80% of their time looking for and preparing data and only 20% of their time doing analysis and getting insight out of the data. In 2017, IDC ran a survey that validated the 80/20 rule. This underscores the continued importance of data preparation, including cloud-native data transformation, as part of the data value chain. A 2021 survey from 2021 indicates that the ratio is closer to 70/30 now, suggesting that efficiency has improved, perhaps because tools and practices (DataOps) are improving.

The benefits of DataOps for business in a nutshell

  1. Faster process (faster value creation)

  2. Realtime insights

  3. Focus on import issues

  4. Catch errors immediately

A lot of data’s value for a company is not uncovered because there is a lack of understanding. A company can invest in new technologies; roll out a full DataOps strategy; and engage in a ‘new collaborative culture’ internally, but if the data scientists, business users, and decision-makers can’t derive proper meaning from the data, business outcomes will get lost in translation.

Remember, DataOps is a process, not a tool itself.