Data Quality
Reliability and transparency about your data. Confidence in your insights with trusted data quality management.
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Do you know that low-quality data impacts revenue growth, customer churn and business deals? As per Gartner, poor data quality costs organizations an average of $12.9million every year. Poor data quality leads to unreliable data, which is the main obstacle in monetizing data. If data is not accurate, complete, and consistent, it may lead to many lapses in business decisions.
Data Quality Management is now the top priority for all organizations in the wake of these changes. It is not about the right validation controls to check the quality but also about the right people, processes, and technology in every business system to ensure high-quality data. Moreover, our experts ensure that your data quality management is continuous and does not have any end date. As new data constantly flows through the business systems, quality is also essential for leveraging business intelligence and analytics powered by AI&ML.
How do you Measure Data Quality?
The definitions for data quality management may vary across organizations, but we look at the following dimensions to ensure data quality
Integrity
Accuracy through the lifecycle and sustenance to pre-established standards
Validity
Conformance of data according to the set values in the entire data set
Accuracy
The extent to which data is portraying the actual scenario of the values in the data set
Uniqueness
Measuring the number of duplicates, as only one data entity can exist in a data model
Consistency
Same data to hold the same value in different datasets
Completeness
Existence of null fields in the data and their redundance
How do we Implement Data Quality Management?
Data quality impact assessment
Our experts qualitatively review your data and assess the possible operational impact on the major business processes. Data quality dimensions vary across businesses, and we set them up according to your data sensitivity and impact analysis. Our team also assesses the way your teams create, use, and organize data in the database. We deploy proven statistical techniques like data profiling to understand more about your data. Data profiling is leveraged to explore a data set’s content and characteristics– like structure analysis, relationship analysis.
Define rules and metrics
Our experts consult with your data users to correlate the business impact with data flaws; this can help establish acceptable thresholds for different data quality metrics scores. We then integrate the thresholds with measurement methods to define the data quality metrics that we aim to monitor. The rules and metrics are bound to change in a time frame with constant changes in the source data.
Introducing InsightBox
data to actionable intelligence.
InsightBox is designed to address your organization’s data needs, connecting any data sources and translating them to an organized data environment, better visualization, prediction, and decision-making. Industry and function-specific library of reports/dashboards help you get started with your dashboard in just a day.
Efficiency at the nucleus, this platform is designed to reduce time to data, increase data visibility and facilitate better decision making.
