The ever-growing volume of enterprise cloud data and its critical role in informing strategic business decisions demand thorough monitoring of data assets. Data quality issues, anomalies, and inconsistencies can derail analytical insights or affect business users, making enterprise-level observability systems essential.
Let’s consider why implementing those systems is now critical for enterprises and why the automation-first approach matters.
What is Data Observability?
What is data observability in practice? A data observability platform offers a dashboard that helps data analysts and engineers assess the current health of a company’s data. Observability layers integrate with existing cloud data warehouses and inform users about data volume, schema, utilization, and issues that arise throughout the data lifecycle.
Detecting data issues takes primary focus in monitoring groups. Issues can skew analytical insights, impact the accuracy of data-driven decisions, or, in worst-case scenarios, cause data downtime. Such downtimes are highly undesirable for any business, as revenue loss due to data downtime can amount to five-digit figures annually, depending on the business size and industry.
By implementing a viable data observability solution, businesses can significantly reduce the risks of data downtime. This allows them to detect and rectify flawed data sets before any actual damage occurs.
Common Data Quality Issues to Look out for
Proactive monitoring of data health aims to identify the following data quality issues:
- Incompleteness: Key fields might be missing due to a flawed data collection process.
- Irrelevance: Data points are non-essential and don’t add any business value to data operations.
- Inaccuracy: Accuracy-sensitive systems might fail due to a lack of precise inputs.
- Outdateness: Depending on the type of operation, a particular data set should be refreshed at a specific rate; otherwise, the outputs might be incorrect.
- Duplicate data: This racks up the consumption of Cloud Data Warehouse (CDW) storage capacity, increasing total data costs.
- Hidden data: In the age of big data, a considerable share of collected data remains obscure. Data quality teams need to reveal such data sets and decide whether they can serve business purposes or should be deleted.
- Inconsistently formatted data: This is a typical problem when data is transferred between different applications. To avoid this, data professionals ensure that there are organization-wide standards for exchanging data with standardized formats.
To detect and eliminate these data issues, human expertise must be augmented with reliable and high-performing automation. One approach is to use a data observability platform with an integrated AI co-pilot.
4 Benefits Unlocked Through the Use of Data Monitoring AI Bot
Observability powered by AI bots yields huge benefits. Here are the most notable:
- ML-powered detection of data anomalies and issues: An observability bot runs on machine learning algorithms. It analyzes and maps data use patterns 24/7. Therefore, it can automatically detect pattern deviation or a data anomaly that could have been overlooked otherwise.
- Human-readable notifications: AI bots deliver AI insights in a form that is easy to read for non-tech employees. This makes insights on data quality, cost, and utilization more understandable. Data specialists can easily share their observability findings with CEOs and justify necessary improvements.
- Automatic detection of data deviations: The data quality team doesn’t need to set alert thresholds manually. The benchmarks will be set automatically and customized by the observability software provider on demand.
- Increased time and cost efficiency: AI co-pilots reduce debugging and root-cause analysis significantly. This high-end automation saves data engineers time and increases the ROI of data issue troubleshooting.
Regarding cost efficiency, such an AI bot is a go-to solution for enterprise-level data observability due to its scalability. It will continue contributing to enterprise data ROI as your data operation grows more complex.
Bringing Data Quality, Performance, and Cost Together with Cloud Data Management
Cloud data management encompasses strategic monitoring of data quality and optimization of performance and cost. It is the new reality for businesses aiming for smooth, supervised, and cost-efficient data operations.
Automated AI-powered data observability providers like Revefi unlock promising capabilities for data-driven companies that plan to grow consistently. These services foster:
- Enterprise-wide data adoption: Proactive data monitoring and faster troubleshooting ensure a smooth user experience across all departments. Managed cloud data becomes more reliable and trustworthy.
- Higher returns from CDW use: CDW management yields impressive returns, as you can automatically detect the main overspending culprits. On average, it’s possible to cut CDW costs by 30-50%.
- Effective use of human capital: Automate dull and tedious routines to unburden your data team. With automated monitoring, they’ll have more time to derive insights on how to optimize data schema and internal infrastructure.
