Data Quality Assurance
Data quality assurance is the process of identification and elimination of any data anomalies via the processes of data profiling and cleansing. Since 1989, ScienceSoft provides data quality assurance services to ensure that our customers have clean, complete and up-to-date data.
Need Accurate and Business-Ready Data?
- Handles duplicates and inconsistencies in your data.
- Designs key metrics to control data quality.
- Advises on and implements data governance procedures.
- Data analytics expertise since 1989.
- 16 years in data warehouse services, design and implementation of business intelligence solutions.
- Big data services since 2013.
- ISO 9001 and ISO 27001-certified to assure the quality of the data quality assurance services and the security of the customers' data.
- Deep expertise in 30 industries and working experience with industry-specific standards and regulations (HIPAA, PCI DSS, etc.)
Data quality consulting
Our data quality assurance team advises on:
- Fixing the problems with data quality in the required software systems.
- Relocating data to a new system during migration.
- Integrating data from several software systems.
- Identifying data quality improvement opportunities, etc.
Data quality assessment
For your reports and dashboards to be accurate and data-dependent processes to run as intended, we:
- Define data quality thresholds and rules.
- Evaluate data quality based on the defined rules and thresholds.
- Report identified data quality issues and conduct root cause analysis.
- Design data quality rules and practices to establish the data quality management process*.
- Implement data quality management*.
- Monitor and control data quality*.
* - optional.
Managed data quality assurance
For a monthly subscription fee, you get:
- Data quality rules and standards definition.
- Regular data quality monitoring and control.
- Data quality variations monitoring and reporting.
- Data quality issues resolution.
- ERP (data from Finance, Accounting, Human Resources, Supply Chain and Manufacturing, Sales, Marketing, and other modules).
- SCM (general information about suppliers, inventory, shipping, manufacturing and procurement data, etc.).
- CRM (customer profiles, data about leads, accounts, entries on the progress in communication, and more).
- Ecommerce (web-behavior activities, customer data, transaction logs).
- HR (employee data, applicant data, payroll, and more).
- Industry-specific data (EHR for healthcare, network data for telecom, etc.).
- Specialized departmental systems (Marketing, Sales, Maintenance and Support, etc.).
It’s easy to get lost in random quality issues and miss the big picture of overall data quality. We introduce data quality metrics to present the entire picture in one report.
No data contradictions within one data store and across different data stores.
The information your data contains is reliable and error-free.
Data is sufficient for answering your business questions.
Data is accessible, and it is possible to trace the introduced changes.
Data has the required format and structure.
A data record with specific details appears only once in a database, no data duplicates are reported.
Data represents reality within a reasonable period or in accordance with corporate standards.
To protect your business information, we practice a three-level approach to security:
Signing an NDA.
Working within secure infrastructure tested by our information security experts.
Following ScienceSoft’s information security policy that covers security measures for internal and external information assets.
Data consolidation during mergers and acquisitions. M&A require merging ERP, CRM, HR, and other data-heavy systems of 2+ businesses, which may result in duplicates, outdated or incomplete data. We can help you to go through the process of M&A with reduced data quality pains by designing standardized data structures and setting data governance procedures, setting quality metrics, integrating data from multiple systems, providing a toolkit for managing the change, and more.
Big data nature. With big data, it’s not possible to achieve all the usual data quality criteria by 100%. Our team will find a good balance among data consistency, accuracy, completeness, auditability, and orderliness so that your big data is of good enough quality at a reasonable cost, within a reasonable period, with no hindrance to your systems’ performance.
Hard-to-fix quality issues. When data quality issues keep coming up, it’s necessary to deal with their root cause rather than the aftermath. We do root cause analysis in close collaboration with IT specialists responsible for a particular system (CRM, ERP, CMS, and more).
Our Data Analytics Portfolio
Make Business Decisions Relying on Quality Data
ScienceSoft’s team will help you remedy the existing data quality issues and implement effective data quality assurance practices to sustain the required level of data quality.