Data Observability for Operations
Table of Contents
7 Ways Ops Uses Data Observability
For non-engineers that work with data, the literature around data quality can get intimidating quickly. What’s the difference between a “kernel panic at the OS level” and a panic at the disco?
Highly technical data issues are important to monitor and address, but operations teams are more concerned with questions like:
“Is the data I’m analyzing up to date?”
or…
“Are we missing key data fields in this spreadsheet?”
In industries like financial services, which handle massive volumes of personally identifiable information, the ability to answer these questions and quickly address the issues can impact customer satisfaction, compliance status, and the sophistication of digital products.
Read: Data Observability for Financial Services
However, most data observability and data monitoring solutions are too technical for operations teams to use effectively.
Your quality assurance teams shouldn’t need an IT specialist in order to compare spreadsheets from different systems. Nor should your subject matter experts be three steps removed from data issues that they could easily correct.
With operations-focused data observability tools, enterprises can bridge the gap between data stewards and data consumers.
Here are seven ways ops can use data observability tech to enhance their day-to-day:
1) System Comparison
Analysts often compare data they receive from various databases and third parties to their system of record. This typically manual task can often be accomplished through data observability tools that feature transformation capabilities.
Reconciling databases from even the same platform can cause huge roadblocks in your analysis pipeline. Here’s how BaseCap users break through this bottleneck.
2) Risk Management
You only know what you know about your data. Through total observability, risk managers and internal auditors can gain a higher degree of confidence in their findings.
3) Root Cause Analysis
When issues arise within data pipelines or systems, data observability tools can facilitate root cause analysis by providing visibility into data lineage, dependencies, and transformations. Operations teams can trace the flow of data across different components and identify the root cause of issues to expedite troubleshooting and resolution.
4) Change Impact Analysis
Imagine if a business analyst could visualize the impact of data schemas, configurations, or code on downstream systems and analytics. By analyzing metadata and lineage information, teams can assess the potential risks associated with changes and ensure smooth transitions without disrupting operations.
5) Anomaly Detection
Operations teams can set up alerts or automated actions with observability tools to notify them of unusual behavior, which could indicate data errors, infrastructure issues, or potential security threats.
6) Capacity Planning
By analyzing historical data and workload patterns, teams can make informed decisions about resource allocation, infrastructure upgrades, and scaling strategies to meet growing demands.
7) Spreadsheet Standardization
Your business uses a million spreadsheets, and every employee has a different style. Simple incongruences like column names or table structures can make consolidation and analysis impossible. Data observability tools can help standardize spreadsheets before they’re studied by operations teams.
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About BaseCap
BaseCap is the data health platform that helps operations teams prevent and correct bad data. Top US banks use BaseCap for quality control, process automation, and compliance management.
"I think the tool is great because it's an out of the box solution where you can give a business admin, or someone that's knowledgeable enough from a tech perspective and a business perspective, to really drive and make the changes and really own the administration of the tool."
