Trends in Data Quality.  Already aware?  - Trends Information Services

Trends in Data Quality. Already aware? – Trends Information Services

Do you know that on average, a Belgian company fails to reach 6.75% of its active customers due to bad data? Data quality is therefore a need for any business. Because “Garbage in is Garbage out”! However, technological developments have profoundly changed the way we work. Lynn Van Avermaet – Solution Leader DQ at Black Tiger Belgium, tells us about the new trends that make data quality even more important for companies.

“Most companies deal with data quality in a ‘rough’ way, but would benefit from a more structural DQ approach,” says Lynn Van Avermaet. “Based on more than 30 years of experience in Belgian and international personal data, Black Tiger Belgium has not only developed powerful tools, but has also refined the methodology for implementing a data quality strategy. data.”

These DQ tools have recently evolved significantly to respond to certain clear trends in the processing of (personal) data. An overview.

Reference and AI files for better data

Conventionally, reference files are mainly used, such as a street reference file. But to take recognition rates to the next level, or when such lists are not available, Black Tiger Belgium is fully committed to AI.

AI also often offers good solutions in the context of internationalization. In Belgium, we can look back on a long tradition of high-quality DQ reference files, enabling first-class processes in terms of standardization, normalization, identification and deduplication. But what if your business goes international? So the combination of open data and AI provides the solution.

DQ ‘on premise’

Along with the digital transformation of businesses, the speed of availability of clean and correct data has become essential. Batch processing is therefore preferably replaced by online web services. We also find that companies prefer to keep these data quality processes entirely within the company and opt for a live internalized feed (eg ESB or message queuing). In this case, the integration of a number of specialized software components in the DQ provides support, to enable this “DQ on premise”.

Even when the master data is stored in different systems (and must remain there), such a set of easily integrated software components is the solution to guarantee the right level of data quality.

GDPR and data quality, hand in hand

Businesses increasingly understand that GDPR and DQ must go hand in hand. However, privacy legislation can only be complied with if it is technologically integrated into business tools and processes and applies to all personal data in real time and continuously. With the right software, however, GDPR should not be a punishment, but a real opportunity, to go 100% for consumer rights!

Want to know more about Black Tiger Belgium’s data quality tools and services? You can find all the information here.

“Most companies deal with data quality in a ‘rough’ way, but would benefit from a more structural DQ approach,” says Lynn Van Avermaet. “Based on more than 30 years of experience in Belgian and international personal data, Black Tiger Belgium has not only developed powerful tools, but has also refined the methodology for implementing a data quality strategy. data.” These DQ tools have recently evolved significantly to respond to some clear trends in the processing of (personal) data. An overview. Traditionally, reference files are mainly used, such as a street reference file. But to take recognition rates to the next level, or when such lists are not available, Black Tiger Belgium is fully committed to AI. AI also often offers good solutions in the context of internationalization . In Belgium, we can look back on a long tradition of high-quality DQ reference files, enabling first-class processes in terms of standardization, normalization, identification and deduplication. But what if your business goes international? So the combination of open data and AI provides the solution. Along with the digital transformation of companies, the speed of availability of clean and correct data has become essential. Batch processing is therefore preferably replaced by online web services. We also find that companies prefer to keep these data quality processes entirely within the company and opt for a live internalized feed (eg ESB or message queuing). In this case, the integration of a number of software components specialized in DQ provides support, to enable this “DQ on premise”.Even when the master data is stored in different systems (and must remain there), a such a set of easily integrated software components is the solution to guarantee the right level of data quality. Companies increasingly understand that GDPR and DQ must go hand in hand. However, privacy legislation can only be complied with if it is technologically integrated into business tools and processes and applies to all personal data in real time and continuously. With the right software, however, GDPR should not be a punishment, but a real opportunity, to go 100% for consumer rights!

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