For 5 years, Qrapp has been providing data quality assurance to keep our customers safe from the disastrous effects that low-quality data can cause. To ensure that data is clean, complete and up-to-date, we develop and implement data governance procedures, design key metrics to control data quality, handle duplicates, inconsistencies, and outliers.
We offer three models of cooperation so that you could choose the one that suits your goals best.
To ensure that your reports and dashboards are accurate and data-dependent processes run as intended, we evaluate your data to find incomplete records, duplicates and triplicates, outdated or unreliable information, late data entries or updates and other examples of low-quality data.
Once data quality assessment is complete, we prepare a comprehensive report describing the problems identified. If you don’t have an in-house team to fix data issues, our team is ready to step in and solve them. For example, we may set rules that automatically correct records to meet the accepted standards (as in case of MR. and Mr., when the acronym MR. is automatically transformed into Mr. thanks to the established conversion rules).
Our experienced consultants will bring in their experience and best practices to:
Under this model, our team monitors your data on a regular basis, keeps track of its quality, reports variations, and timely addresses issues as they arise. For example, we can check if data standardization procedures work as intended or tune merging so that this procedure runs according to predefined rules. Our data analytics team is ready to clean the data and establish data governance procedures to ensure your data-reliant solutions power informed and valuable business decisions.
We’ve developed expertise in 10+ domains with the special focus on:
To protect your business information, we practice a three-level approach to security:
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 M&A require merging ERP, CRM, HR, and other data-heavy systems of 2+ businesses, which may result in duplicates, outdated or incomplete data.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.
Blurry big picture. 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.
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).
Business specificity. Business data largely depends on the industry the enterprise is in. Our testing specialists have professional knowledge in 10+ domains, including healthcare, banking and financial services, retail, manufacturing and more, to address the specifics of your data solution.