6 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.
DataOps teams are responsible for ensuring end-user data is clean and free of errors. As data environments continue to grow in complexity, it’s becoming more difficult for DataOps to achieve this visibility, which increases the risks of data errors going unnoticed. With this in mind, our editors bring you a short list of key DataOps challenges your team needs to consider from MANTA’s latest whitepaper entitled Automated Data Lineage: The Cornerstone of Effective DataOps. We also secured some guest expert commentary from the company’s Senior Vice President of Products Ernie Ostic below.
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.
Ernie Ostic: “The data environment is the enterprise’s Wild West. Stakeholders struggle with shifting requirements and unexpected changes to increasingly complex data pipelines. Complexity with limited visibility and poorly communicated changes to requirements give rise to data incidents, declining trust in data, and decreased performance and agility of data teams.”
Paired with lackluster communication among key stakeholders, poor coordination can make building, deploying, and maintaining data pipelines more difficult than it needs to be.
Ernie Ostic: “When working on multiple data projects involving different teams, it is imperative to have strategic alignment. By streamlining procedures and establishing standards for communicating changes in advance, such as agreeing upon approved and appropriate data flows, teams can boost efficiency and avoid costly mistakes that slow everyone down.”
One of the main reasons teams continue to struggle with increasing delays in operationalizing models is due to a lack of quality data lineage.
Ernie Ostic: “Without observability across the pipeline at any given time, even minor unexpected bottlenecks can hinder the efficiency of data operations. But speeding up and scaling processes without maintaining data quality isn’t workable either. Mapping their environment with data lineage allows teams to improve pace and boost operational efficiency without compromising on data quality.”
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.
Ernie Ostic: “Every enterprise has ‘black boxes” hiding across countless databases, warehouses, processes, and BI tools involved in data operations. Using metadata to automate data pipelines, engineering teams can shift their focus away from manual, time-consuming tasks like adjusting models or transformations to make room for experimentation and innovation to take place.”
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.
Ernie Ostic: “Today’s enterprise business users need lineage as much as their technical counterparts. Without business users’ ability to leverage self-servicing features, the knowledge gap of data operations will continue to widen and hinder the company’s ability to compete on data-driven insights and business intelligence.”
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.