Founder @Acceldata
June 28, 2022
The concept of data governance was originally approached from a legal perspective, but today’s modern data environments require a broader, more comprehensive appreciation for the necessity of governing how data operates, not just what it communicates. Viewing data governance through a legal lens is no longer enough to keep data standards and policies consistently accurate. This way of observing data likely originates from regulator compliance, but what if organizations began utilizing AI/ML data observability tools, such as multidimensional observability, to get a single and holistic source of analytics? This answer is simple: data teams will track data health changes quicker so data teams can identify and take appropriate action before issues of data hygiene or system instability might wreak havoc. Insights across data processes, data quality and reliability could all be in one place, ultimately making data governance policies more efficient than ever.
Current challenges of modern analytics include:
- Data quality
- Performance
- Scalability
- Manual effort
- Silos
Data engineering teams are struggling to meet modern-day analytics demands. By utilizing AI/ML and real-time dashboards, these teams can take a more proactive approach to identify, helping them predict and prevent data issues earlier.
Moving Away From Data Scripts
In many cases, traditional data governance methods require data engineers to run through a gamut of writing ETL (or SQL) data scripts to determine the health of data received. If issues are found, it turns into a back-and-forth between organizational teams and vendors to correct errors within data. Not only does this slow business-critical processes, but it requires repetitive manual labor that companies may not be staffed to properly accommodate.
What tends to occur within large enterprises is an overwhelming need to consume and operationalize data. To process and ensure this data is valid and of high quality, data engineers are then tasked to manually create data quality scripts.
For instance, if your company ingests data from third-party vendors, by the time a data engineer or ETL script identifies an anomaly within that data, it may already be picked up by an algorithm. At this point, the company then has to refer back to the vendor and retroactively communicate that their data was bad.
Both of these scenarios can put company data governance policies at risk.
However, with data observability companies can validate data in real-time as the data is being ingested. Any data issues are caught near-instantaneously at the source. This allows enterprises to to take appropriate governance actions instantly and ultimately expedite data team processes.
Is Your Data Governance Strategy Impacting Sensitive Data?
Traditional data governance practices are most common in industries where personal information is highly sensitive and regulated. The healthcare industry, for example, deals with HIPAA regulations surrounding medical patient information. In order to be fully compliant with HIPAA, this data must be valid and secure within approved datastores. Current tools used by companies to support data governance policies are manual and batch-oriented, leaving little to no room for proactive data support. By waiting for manual processes to complete, traditional data governance strategies are putting these companies at risk. Now is the time to make the shift towards automated and proactive methods when it comes to data governance strategy.
By utilizing data observability, traditional data governance can be operationalized and ensure that data accuracy is prioritized above all else. By providing automated and real-time visibility of data throughout its journey, a multidimensional data observability tool eliminates the process of manually going through data and instead, focuses on the source itself. This is achieved through ‘shifting ‘left and to be closer to the ingestion source. This operationalized way of looking at data governance can provide lower risks for enterprises.
Maximizing DataOps’ Productivity
Companies that continue to purchase and use patchwork data observability tools likely see low productivity levels because of how tedious and back-and-forth data checking can be. With an intelligent observability tool, data teams and companies get a boost in DataOps productivity in the following ways:
- Validating data as it flows in
- Understanding pipeline traffic and events
- Identifying root causes
- Solving anomalies automatically
- Continuously optimizing the pipeline process
Operational data governance isn’t difficult, it just hasn’t been the norm. When data teams use intelligent data observability tools, they are always aware of what’s happening without having to manually manage data at all times. The real-time nature of data observability ensures that data governance policies are followed and in turn, that companies see a reduced risk to their own data governance policies.
