DATA QUALITY & OBSERVABILITY CONSULTING

Data Quality programs – where the obsession with creating unnecessary rules meets the classic “undisciplined pursuit of more.”

Our focus is more on offering  meaningful and effective data quality monitoring solutions. Ask any executive what value Data Quality programs bring, and you’ll probably hear the same thing – an overload of Data Quality rules that add little to no real value. Beyond basic metadata characteristics, the program offers nothing.

“ Do we really still need rules that simply confirm dates look like dates or numbers look like numbers In an age of advanced Machine Learning (ML) engineering? ”

Most programs obsess over Consistency and Completeness, but what about the data’s Accuracy from a business perspective? That’s where 1lessclick steps in to bridge the gap. Data must be valid, complete, unique, accurate and delivered in a timely manner.

At 1lessclick, we will establish and implement data quality solutions focused on essential data quality dimensions. Our framework lays the groundwork for a Data Quality monitoring program and effortlessly scales into a full end-to-end solution—spanning profiling, rules engineering, scheduling, integration with critical data assets, and automated data issue alerts.

Data Observability

Data Quality looks at data at rest and Observability monitors the data in motion. Both are two layers that look at the quality of data. Data Observability is a layer or fabric that lets you know the health of pipeline. It has model for anomaly detection.

Data observability begins with the collection of metadata and extends across data ingestion, transformation, and consumption. Essential metadata from Airflow pipelines, Spark processes, Python workflows, and dbt jobs help identify the most critical pipelines and enable the creation of timely anomaly detection alerts, which are distributed to the appropriate stakeholders.

Data Quality & Observability Consulting @ 1lessclick®

  • We will create a library (book) of business rules that will form the business lens on calculating data quality.
  • The data quality rule results are compared against their acceptable limits (thresholds) set by business.
  • We will make sure the data quality rule results are actively monitored on a periodic basis and publish data quality scores in terms of critical data assets that are consumed by business (data sets, business reports, models)
Our methodology emphasizes the strategic use of data lineage to accurately assign data issues to the correct owners and producers, maintain transparency with data consumers, and involve subject-matter experts in the consultative process.