DataOps: Data management is also agile

DataOps: Data management is also agile

While DevOps is at home in software development, DataOps is about orchestrating existing data streams. […]

Although various companies had to quickly get used to the new normal due to the pandemic, digitization also offered the opportunity to use the wealth of new data and data sources for innovation. However, there are two major challenges in managing data:

  1. Big Data is diverse: Companies are literally drowning in a sea of , which relate to many different aspects of business. One day, the focus is on transaction data and the next day, for example, on predictive analytics. In addition, there is still a dose of subjectivity in play, depending on who creates, manages and interprets the data. Accordingly, it is difficult to make objective, overarching decisions.
  2. Relevant insights can only be gained in real time: Big Data is not only diverse, but constantly changing. For meaningful business intelligence, the time of access and analysis of data is the decisive factor-especially in times of unpredictable market developments. But in fact, according to a study by Dimensional Research, commissioned by Fivetran, 41 percent of data analysts work with data that is two months old or even older. If analysts do not have access to up-to-date data, the insights gained are either already obsolete or unusable.

To address these two challenges, it makes sense to be agile using DataOps. DataOps, short for Data Operations, refers to automated, agile methods to improve the quality, reliability and speed of data analysis. The process attempts to break down the silos between development and operations and fosters a collaborative data management practice for integrating and automating data flows across the enterprise. Data demokratization benefits Chief Data Officer and data users in the various departments alike.

The implementation also promotes the cooperation of data engineers and analysts. According to the above study, more than 60 percent of data professionals lose valuable time waiting for engineering resources. These inefficiencies often force analysts to get access to the data they need in a roundabout way – which in turn means that they can spend only half their working time analyzing data. The democratization of data and business intelligence enables a better within the company in terms of customer behavior and for fundamental decisions.

* Alexander Zipp is a startup, Sales & marketing expert with more than ten years of professional experience in specialist and management roles. His professional career includes LinkedIn and Asana. For both companies, he co-opened the Munich location and drove the development of both brands in the German-speaking region. The author is currently responsible for Fivetran’s activities in the DACH region.

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