Corporate  Data  Quality Problems

Datasets containing records about people and companies are a foundation of corporate master data. Typically, quality of such data is of utmost importance for companies which maintain large databases, such as: banks, insurances, government, telecoms, utility companies, middle-sized and large enterprises.
Errors and inconsistencies in datasets are inevitable. Duplicates, erroneous or incomplete records are the start of chains of poorly or incorrectly represented data, miscommunication, and finally, wrong business decisions. Addressing wrong people unintentionally, missing actual clients, and all kinds of activities and assumptions based on erroneous data, inflict damage to credibility and efficiency. In multitude of ways, gain vs. cost declines.

Nowadays, in 2022, there are good data indexing and search technologies available, with good performance and data throughputs. However, solutions based on customizations of such technologies tackle the problem, providing solutions up to a certain level, or worse – only nominally. Being more tools than solutions, they do not really help in terms of data quality per se.

Standard Expectations and Issues

Improving data quality above certain levels is usually experienced as a cumbersome and expensive venture. This effectively imposes artificial thresholds to approaches and expectancies regarding data quality today.

Our Process – a ROI Winning Approach

Our solutions have been powering enterprises’ mission-critical systems and most demanding data matching use-cases for more than a decade now, saving millions both organizationally and consequentially due to quality of master data achieved and made available to business processes.

Thanks to our proprietary technologies and experience, for some of the most ubiquitous use-cases we are able to raise the bar considerably above industry standards. Our approach to master data quality alleviates some of the most painstaking data quality issues, and improves all affected business processes, as a primary goal. These primary improvements are immediate, as our process is effective with intact data, without the need to label, change or correct the underlying master records. In the long run, reliability of such data improves confidence in data-dependent processes, removes process errors and paves the way to better data insigths and reliable business intelligence.

Contact us to arrange a presentation.