Data Observability: Safeguarding Business Trust in Your Data Products

Continued trust of the business on the data products is crucial to ensure their confidence on the insights being presented. The whole objective of analytics is to create data driven decisions and actions in order to propel the organization towards its strategic objectives.

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Significant investments are made to deliver the data supply chain and the ensuing data products. This is where I feel that rather than reactionary approach we should be leaning towards proactive assessment and determination of issues in the data supply chain and its quality, so that the same can be highlighted and/or resolved way before any business stakeholders raises the flag.

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I can see that there are lots of good products available on the market, ensuring that the data products remain reliable and trustworthy for the decision makers. I also observed significant investments being made by key cloud data platforms such as Snowflake, Databricks etc to ensure that this aspect gets delivered along with their other data services.

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Data Observability enables you to have a handle on aspects like freshness, volume, schema drift, columns etc. by using standard or custom rules. Furthermore its also lets you carry out the impact analysis on the downstream data flows and products to triage effectively. ?Alerts and/or Incidents can be raised thereby spurring the Data-Ops team to take appropriate action.

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Bottomline is that while the ownership of the data and process quality resides with the business, as organization leaders, we need to ensure that Data Management/Ops team is equipped, to rather play a proactive role in detecting the shifts in the data quality, data propagation patterns or any other slips in the data operations. Such a functionality will go a long way to ensure that the significant investments, made earlier, in creating the data products, remain effective, productive and continue to deliver the value as promised to the business. ?

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Therefore, I believe the Data Observability should be one of the crucial parameters, to be considered in data platform selection. Alternatively, organizations can also evaluate the various independent data observability (or more comprehensive governance products) solutions as per their needs.