Maintaining Operational Data Quality encompasses all business practices that ensure the accuracy, consistency, and reliability of operational data within an organization. Ensuring high data quality is crucial for informed decision-making, process optimization, and regulatory compliance.
Inaccurate or incomplete data can lead to ineffective business processes. In the Healthcare sector, accurate patient records are essential for effective treatment, precise diagnoses, and correct medication. If you operate in Supply Chain Management or Retail, high-quality data ensures efficient inventory management and timely deliveries. Inaccuracies can cause stockouts, overstocking, and higher costs. Accurate data is crucial for risk management and fraud detection in Financial Services.
Across all sectors, compromised data can lead to financial losses, legal issues and a bad customer reputation.
Poor data quality can critically undermine all spheres of industry and services, hence high operational data quality is the foundation of accurate analysis and data-driven strategic and operational decisions, leading to higher customer satisfaction and increased sales.
Data practitioners face a number of challenges in maintaining integrity and quality of data channeled through multiple sources within business operations, such as:
Aperture Data Studio is designed to provide a comprehensive response to the diverse data quality challenges your organization faces daily. Its standardization, validation, and automation capabilities are tailored to effectively tackle the most critical data quality issues.
Identify your sources and consolidate your data. Turn it into a unified format through a variety of transformation steps and then build out a data pipeline. Automation of workflows scales these processes to improve efficiency.
Aperture Data Studio’s capabilities to profile data and analyze relationships allow you to identify gaps in data or deviations from the established data standards.
Profiling helps to identify any data deficiencies such as incomplete values or those lacking uniqueness, inconsistent formatting and standards, or duplication of records.
Any Dataset or View (a dataset excerpt allowing to create different data representations) can be analyzed, using the Relationships functionality to discover previously unknown relationships in your data, or find bad data where the expected relationships do not hold.
Transformation involves building workflows that consist of various actions and steps. These workflows allow you to manipulate, clean, and configure your data in a structured manner using a large variety of native or custom Functions.
For example, transformation can be used for joining datasets or for transforming a column value by applying functions. You can convert all text in a column to uppercase, remove unnecessary spaces, or hide sensitive PII data.
Once the data has been profiled and transformed, it’s important to verify its accuracy and ensure it is consistently and correctly formatted. Data profiling, transformation for consistent formats, and validation together help ensure the highest quality data is maintained for future use.
Aperture Data Studio offers recommendations for data-driven validation rules leveraging insights from data profiling activities. Rules are checks created to identify and report specific data quality issues. You can also build your own customized rules and reference them wherever needed as single rules or a Ruleset to perform consistent data quality validation.
Automation allows setting up actions, such as running workflows, to happen automatically when specific events occur. You can schedule workflows to automate data cleansing, validation, enrichment, and deduplication.
Furthermore, Aperture Data Studio AI and machine learning capabilities in data-tagging and duplicate detection help optimize data quality processes by learning from data patterns and improving accuracy over time.
Data monitoring capabilities allow you to keep track of workflow execution progress and interactive dashboards enable visualization of data quality trends, identifying issues and tracking improvements.
Incorporating our licensed Pushdown Processing module will amplify your data efficiency and optimize business workflows involving very large amounts of data. Profile, validate, and transform billions of records in minutes, at data source while maintaining visibility over the results in Aperture Data Studio.
Data processing directly at the source minimizes the need to transfer large datasets across systems ensuring high security. It is a cost-efficient way to perform repetitive data quality tasks directly at source. Pushdown processing in Aperture Data Studio finds its best application in processing data coming from a single source.
Realtime Workflows is an Aperture Data Studio licensed module that allows you to validate incoming records immediately upon receipt, apply your business wide validation rules and then correct (transform) them into the required format before they enter the CRM system. This ensures that data quality is applied from the start.
A further potential step to elevate operational data quality is establishing a powerful governance framework that leverages and regularly monitors data as an asset. Integration of our Data Catalog and Governance module unlocks these capabilities. It enables users to catalog their data assets, create a common understanding of terms, set ownership and accountability and monitor data quality results in the context of business impact.
Our Data Catalog and Governance module enables you to create a logical overview of your data to help you understand and control what data assets your organization holds and how they are governed. It plays an essential role in maintaining compliance and efficiency of data quality processes.
