6 Must-Have Features Your Data Observability Tools Should Have

The incomplete, outdated, and excessive data impedes the overall performance of a cloud-based enterprise data stack. Eventually, data quality teams spend many tedious hours troubleshooting data issues manually.

On top of that, companies struggle with data downtime that blocks the work operations of downstream business users. According to Atlassian’s cross-industry statistics, such downtime is a genuine productivity and revenue killer, as businesses might lose from $1M to $5M hourly.

Data observability¬†practices can eliminate the root causes of data downtime. Let’s consider what must-have features your observability system should have to ensure high-end and consistent performance.

What is Data Observability?

Simply put, data observability implies proactive prevention of data issues. It utilizes machine learning algorithms that scan, analyze, and recognize the normal behavior of data artifacts throughout their lifecycle.

Modern observability tools provide a comprehensive, user-friendly interface where data engineers can access detailed reports revealing data issues. The analytical power of data observability software allows it to provide actionable insights users can follow right away. For instance, the system can point to the excessive use of a particular data set and notify you of restricting access to it so you can cut cloud data warehouse (CDW).

Why Can’t Enterprises Do Without Data Observability?

The fact is that most companies rely heavily on solid and transparent data. As the data ecosystem grows more complex year after year, the chances of data inconsistencies and anomalous behavior get tremendously high.

Considering that data observability is no longer optional, it’s a must. Investing in observability tech pays in spades, as businesses can achieve:

  • Data Quality Consistency. Data engineers backed up with modern observability tools can backtrack the data lineage and prevent data issues from occurring. Thus, they ensure data integrity and correctness throughout its lifecycle.
  • Improved CDW ROI. Excessive data is a common problem when enterprises don’t purge their datasets from duplicated, outdated, or irrelevant data. Observability software can flag such excessive records so data engineers can fine-tune ETL performance and eliminate overspending on CDW storage. The use cases confirm that proper data observability might cut 10% to 30% of CDW cost.
  • Reliable Data-Driven Decision Making. Incorrect or incomplete data inputs can skew the results of integrated BI and analytics tools. By eliminating these issues in advance, companies preserve their analytics as conclusive, correct, and actionable. Therefore, they can plan and strategize their development and grow revenue.
  • Increased Operational Efficiency of Data Team. Actionable observability insights can cut multiple productivity of the data team as root cause analysis will take minutes instead of hours. Instead of diving headfirst into debugging, data engineers can switch to more essential strategic tasks. For instance, you can involve them in discussions to develop and establish an enterprise-wide data governance

Must-Have Features of Data Observability Platform

Investing in data observability demands in-depth evaluation. Hasty decisions won’t do. We’d primarily recommend you research purchase options on your own. Delving into case studies and first-hand experiences is surely worth it. By doing so, you’re likely to understand better what data quality issues should be mapped, how they are connected with the high CDW cost, and how soon you can see the outcomes of data observability tools use.

Once you have learned enough, check whether the observability product you’re interested in provides the following features:

  1. Immediate Launch with Zero-Touch Installation. Focus on choosing a plug-and-play solution that launches without code interventions or connecting existing data pipelines manually. Zero-touch installation features are a must-have for every business, as data engineers and dev teams are already preoccupied with their regular responsibilities.
  2. Automated Pre-Programmed Monitors. Automated deployment of monitors is another time-saving feature to look for. The advanced data observability solutions automatically map key data sources, ETL and reverse ETL pipelines, and cloud storage. It also recognizes dependencies and destinations of data assets so it identifies monitoring objectives autonomously.
  3. Data Issue Prevention Focus.A proactive response to anomalous data behavior is the key to eliminating flawed data points before they affect downstream processes. It’s backed up with predictive machine-learning algorithms that model usual data behavior patterns and flag the outliers whenever they appear.
  4. Human-Readable Data Health Alarms. The natural language prompts can foster a more inclusive data quality culture as they are understandable for non-tech employees. Plus, data stewards can get those comprehensible notifications directly in workplace messengers like Slack if the observability product integrates with third-party apps.
  5. No Additional Data Sampling or Extraction. That’s important because the observability tool doesn’t reserve additional data storage capacity on your CDW and doesn’t consume its computing power.
  6. Regulatory Compliance. Data security compliance is achieved through a minimal-indexing approach: observability monitors access-only metadata and connects to the CDW vendor’s server-side through an encrypted connection. Most trusted data observability products are SOC 2 compliant.

 

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