AI Use Cases

GenAI Accelerator

Nav­ig­at­ing safely through the sea of data


The amount of stored data is increas­ing rap­idly these days. Data qual­ity man­age­ment is there­fore essen­tial to main­tain an over­view and ensure the accur­acy of the data. This allows you to work in a data-driven man­ner and achieve the greatest pos­sible added value from your wealth of data. 

Descrip­tion


The amount of stored data is increas­ing rap­idly these days. Data qual­ity man­age­ment is there­fore essen­tial to main­tain an over­view and ensure the accur­acy of the data. This allows you to work in a data-driven man­ner and achieve the greatest pos­sible added value from your wealth of data. 

Ser­vices

Choos­ing the right maps




In order to make the cor­rect decisions, you need reli­able inform­a­tion. It is there­fore not just a mat­ter of col­lect­ing data, but of ensur­ing its qual­ity through an act­ive pro­cess. In a data ware­house, data from dis­par­ate sources is usu­ally merged, which is a fre­quent source of error. How­ever, the sources them­selves also usu­ally need to be checked and prepared. 

Com­pon­ents

The path to the fin­ish line


These cri­teria are neces­sary to achieve high Data Quality. 


Accur­acy – the data matches the sources and reflects the real world.

Com­plete­ness – all decision-rel­ev­ant data is present and available.

Con­sist­ency – data from dif­fer­ent sources does not con­tra­dict itself, ensur­ing there is only one truth.

Actu­al­ity – the data is always up-to-date at the time of a decision.

Valid­ity – the data com­plies with the defined busi­ness rules and is within the valid range.

Unique­ness – the data does not exist more than once and can be clearly identified.

Advant­ages

Our main advant­ages


With 30 years of exper­i­ence in data ware­housing, synvert has mastered the entire data qual­ity pro­cess. In addi­tion to data qual­ity man­age­ment, our com­pre­hens­ive strategy also includes metadata and mas­ter data man­age­ment as well as data catalogs. 



Exper­i­ence


We have the know­ledge and exper­i­ence to estab­lish effi­cient data qual­ity man­age­ment in your com­pany. This includes the devel­op­ment of DQ rules, DQ cri­teria, DQ meas­ure­ments, DQ score­cards and dashboards. 


Use Cases


We have mastered the vari­ous DQ use cases, from address cleans­ing of cus­tomer data to ensur­ing DQ for IoT data. We know the DQ require­ments of dif­fer­ent indus­tries, from receipt data in retail to par­tic­u­larly sens­it­ive data in the health­care sector. 


Tools


Over the years, we have gained in-depth exper­i­ence with a large num­ber of com­mer­cial tool man­u­fac­tur­ers such as Inform­at­ica, IBM, Oracle and Talend, as well as open-source products and cus­tom­ized DQ solutions. 

Tools

Our tools




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