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
Accuracy through the lifecycle and sustenance to pre-established standards
Conformance of data according to the set values in the entire data set
The extent to which data is portraying the actual scenario of the values in the data set
Measuring the number of duplicates, as only one data entity can exist in a data model
Same data to hold the same value in different datasets
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.
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Establishing metadata management standards and data validation rules
Metadata management standards
For any successful data governance and analytics initiative, metadata management is critical. They are grouped into three categories – business, technical and operational. Business terms, definitions, data privacy settings, data storage formats and rules, metadata usage, and rules in the ETL process are a few standards that are established in the system.
Data quality standards implementation
Documenting the standards and educating the teams to follow the established standards is necessary for implementing any strategy. Our experts ensure that information about the data quality tools and data business glossary are provided to the teams with necessary training. All this is to enable data quality across each system seamlessly.
Data validation rules
We evaluate your data against any inconsistencies; data validity rules are integrated into applications so that any errors can be identified even during data entry. These rules are a must for proactive data quality management.
Ensuring data quality while performing data preparation can be done either manually or automatically. We include techniques such as Root cause analysis, Parsing, Matching, Enhancement, and Monitoring for data cleansing without any quality issues.
Automating data quality with AI and ML
As our experts manage the data quality management and validation checks, we also leverage machine learning techniques to re-evaluate the data cleansing rules and workloads. We portray the need for automatic data cleansing and adjusting any data management rules while recommending process efficiencies based on the analysis.