6 Key DataOps Challenges Your Team Needs to Consider

Key DataOps Challenges Your Team Needs to Consider

A curated list of key DataOps challenges teams should be aware of, from the creators of an industry-leading data lineage platform.

DataOps (short for data operations) is an automated, process-oriented methodology that is used by data and analytic teams to improve the quality of and reduce the lifecycle to adequate analytics. DataOps was originally utilized as a set of best practices before maturing to become its own independent approach to doing data analysis. Applying to the complete data lifecycle from data preparation to report creation, DataOps takes advantage of statistical process control for monitoring and is not tied to any particular technology or framework.


Key DataOps Challenges

Complexity and Shifting Requirements

This factor prevented data and analytics teams from establishing a sustainable pace and can be a major factor in thwarting project continuity.

Inconsistent Coordination

Paired with lackluster communication among key stakeholders, poor coordination can make building, deploying, and maintaining data pipelines more difficult than it needs to be.

Increasing Delays

One of the main reasons teams continue to struggle with increasing delays in operationalizing models is due to a lack of quality data lineage.

Lack of Automated Lineage Data

In addition to poor or lacking data lineage, the absence of automation means analysts cannot scale data qualification procedures. This forces them to spend hours manual cleaning and preparing data instead of doing more worthwhile tasks.

Technical Data Lineage Tools

Most data lineage tools require users to leverage some form of technical proficiency, which makes self-service difficult at best and impossible at worst, especially for business users.

No Lineage

Without lineage to provide a verified source of truth for the end-user, the lack of trust in data keeps efficient data availability and data democratization out of reach for many.

Read MANTA’s whitepaper Automated Data Lineage: The Cornerstone of Effective DataOps to learn more.

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Timothy King

Senior Editor at Solutions Review
Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.
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