By Gary Allemann, Managing Director at Master Data Management

Planning for change would be easy if organisations had all their data in one properly managed system. However, today's companies are inundated with data from multiple sources, running through complex pipelines, and undergoing transformations at countless touchpoints.

Unplanned, poorly communicated, and never-ending changes across the increasingly complex data environment threaten data analytics’ ability to drive meaningful progress and deliver business value. Analytics teams often get caught up in the twilight zone of two extremes—trying to move fast but breaking things or putting in the time yet falling behind. 

Introducing DataOps

DataOps is a collaborative practise that brings together diverse teams to streamline and automate the delivery of data across an enterprise.

It leverages data management technology and practises to ensure:

• Data Integration: simplifying the process of connecting to and consolidating disparate data sources
• Data Integrity: testing and improving data to ensure that business decisions are supported by accurate, complete data
• Metadata Management: maintaining and communicating a clear picture of the data estate, origin, dependencies and changes over time

This sound foundation engages both business and IT stakeholders in an agile way to deliver high quality, well-understood data to support analytics on demand.

DataOps unlocks data for employees across the company – from executives and middle managers to business analysts and operational staff. Each knowledge worker is empowered by access to trusted data and insights, enhancing productivity and competitiveness at every level. 

One step forward, two steps back

When DataOps teams are tasked with manually tracing data pathways across complex systems and hundreds of touchpoints, accuracy, and agility can be the first casualties. By automating data lineage discovery, DataOps can transmute data chaos into data clarity and deliver a continuous, reliable stream of insights that bring value to the enterprise. 

With a lineage map of all the technologies, platforms, and tools that form the data pipeline, teams can end the vicious cycle of “one step forward, two steps back” and ensure that the security and integrity of their company’s data remain uncompromised across the entire pipeline. 

Key challenges for DataOps 

Here are the most common challenges that DataOps teams encounter:

• Complexity and continuously shifting requirements can prevent data teams from establishing a sustainable pace and project continuity.
• Inconsistent coordination and a lack of clear communication amongst stakeholders make building, deploying, and maintaining data pipelines unnecessarily difficult. 
• Data teams frequently struggle with increasing delays in operationalising models due to a lack of quality data lineage. Without automated data lineage, analysts can spend hours manually cleaning and preparing data. Data lineage solutions still require technical proficiency to leverage, making self-service impossible for business users.
• The lack of trust in data keeps efficient data availability and data democratisation out of reach.

The way to successful DataOps in 2021 and Beyond  

Automating data lineage can empower DataOps teams to handle the challenges imposed by the growing complexity of the data environment. Lineage helps cut manual processes, break data silos, and bridge the knowledge gap between business and technical users to keep your pipeline healthy and your organisation happy.