Today’s enterprise data landscape is mainly formed by how organizations manage to keep their data up-to-date, correct, and actionable. Neglecting data quality issues inevitably undermines the sustainability of the enterprise’s data ecosystem. Erroneous or incomplete data sets can impede internal workflows and skew strategic decision-making. They might also affect the performance of client-side applications.
As you can see, the price of flawed data is fairly high. Therefore, data quality monitoring and proactive prevention are a must these days to grow the ROI of data use, foster teams’ trust in your eternal data, and safeguard your business reputation.
Data Observability for Automated Data Issue Detection
The complexity of modern enterprise data stacks and data evolution cycles poses a severe challenge to data specialists. To maintain data quality integrity, they must determine what exact anomaly or inconsistency to detect. They also must understand why and when something went wrong in the normal data lifecycle to connect error occurrence with specific data workflows.
Building and deploying a fine-tuned and responsive data quality scanner might take many months and demand a huge human capital investment. That’s already too much of a workload, not to mention that data engineers will benefit your business more if invested in improving data infrastructure and analysis.
Harnessing an autonomous scanning of critical data instances by AI data observability tool is the way to go. Products like Revefi Data Operation Cloud reimagine the concept of “what is data observability” as they offer a zero-touch deployment and can identify abnormal data behavior patterns based on ML predictive algorithms. No manual touch is required.
In addition to 24/7 awareness of data health, stewardship teams will get the full picture of data usage and real-time notifications on underused or overused assets. These updates can prompt data teams’ next step in optimizing cloud data warehouse (CDW) performance and minimizing data stack spend.
5 Common Data Quality Issues to Tackle First Place
Managing data quality issues should always pursue definite business goals. That’s why, before dealing with the root cause of poor data quality, it’s worth understanding how it impacts the essential workflows. You might notice that some data-driven activities contribute more to an enterprise’s productivity and profitability than others.
Thus, data engineers should set those revenue-crucial aspects of data use above the rest. Those are usually master data assets that demand strict quality control and maintenance. Stay aware of the following issues that can hurt data reliability and accessibility.
1. Irrelevant Data
The common problem with CDWs’ integrated SQLs is that they absorb a lot of data debris that is irrelevant to enterprise business processes. As this irrelevant data accumulates, it eats up quite a chunk of cloud data storage volume and excessively consumes CDW’s computing resources.
To combat such overprovision of SQL instances, data engineers should determine what attributes might indicate irrelevant data and adjust verification rules for data collecting algorithms.
2. Incomplete Data
Data incompleteness hurts observational BI data the most. It also makes statistical representations and analytical outcomes fuzzy, often misinforming strategic planning and long-term predictions. The machine learning models are sensitive to missing data points, too.
Data specialists typically prevent incomplete data from getting into database by setting:
- Obligatory fill-ins for fields in contact and survey forms
- Filling in missing values with mean or median ones if your data is linear
- Cross-matching the tables with similar fields for automatic fill-in
- Establishing data pre-processing and sampling rules for ETL
3. Outdated Data
Data points decay or become obsolete unevenly across industries and within a single organization. The playbook example of perishable data is the financial data on quotes and transaction statuses. On the other hand, contact data on suppliers in commerce typically stay durable for many weeks and months.
If monitoring reveals that the data set failed to update on time, you most likely have poor-quality external data sources, a lag of sensor signals, or a flawed SQL validation method. We also recommend checking SQL logs for failed queries.
4. Duplicate Data
To prevent duplication on the scale, modern ETLs move external data inputs to the staging layer and deduplicate them. ETL-level deduplication cleanses bulk rewrites of unstructured data by creating signatures for identical sections and replacing them with a token value that refers to the location of the matched section in the existing file. If there’s no match, the section will be copied to the destination. Otherwise, ETL will ignore it.
In the case of relational databases, data specialists commonly use query-based (scrip-based) deduplication. This method purges the already reposited data to remove backups and reduce processing overheads.
5. Orphaned Data
Orpahed instances commonly occur due to incompatible formatting of the input data or if ETL’s auto-converting wasn’t properly set. Eventually, the system might source tons of deadweight data that will remain unactionable and inaccessible.
Data teams should first test ETL tools with data samples to see whether they handle converting properly. Next, we recommend you monitor the utilization of the entire data volume regularly with observability tools. Those tools will instantly detect and specify which data set is rarely used and are likely orphaned.
