AI Use Cases

synvert Accel­er­at­ors
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Com­mu­nic­a­tions, Media & Telecom  - Oper­a­tional Data Lake




Com­mu­nic­a­tions, Media & Telecom


synvert sup­por­ted a Ger­man media com­pany to adapt to new GDPR reg­u­la­tions by cre­at­ing an oper­a­tional data lake using Apache Spark, Kudu, and Oozie, which enabled effi­cient data query­ing and ensured data pri­vacy through anonym­iz­a­tion and an updated Opt-In/­Opt-Out system.

Ini­tial situation


The cus­tomer, a Ger­man media com­pany, had to adapt their sub­scribers to a new ser­vice respect­ively new GDPR reg­u­la­tions and they were hav­ing ser­i­ous issues with a Siebel database.

Quer­ies could not be made dir­ectly, as the data­base was already at capacity.

Archi­tec­ture


For the oper­a­tional data lake tech­no­lo­gies such as Apache Spark, a Kudu data base and Oozie are used.

Gen­er­ated bene­fits


A react­ive data­base is imper­at­ive when try­ing to extract busi­ness intel­li­gence from data.

With a low latency query sys­tem, insights can be pulled up imme­di­ately across huge volumes of data.

Addi­tion­ally, GDPR con­sid­er­a­tions, such as data anonym­iz­a­tion, are imper­at­ive to avoid hefty audit­ing penalties.

Ser­vices accom­plished by synvert 


Inform­a­tion had to be taken from the table and mirrored to a Kudu data­base where quer­ies could be effi­ciently performed.

In addi­tion, synvert elab­or­ated the data, and vari­ous data pri­vacy con­cerns were addressed.

Not­ably, data were anonym­ized, and synvert updated an Opt-In/­Opt-Out system.

Categories

All

Banking

Communications, Media & Telecom

Consumer Goods & Retail

Energy & Resources

Health & Life Science

Industrials

Insurance

Public Sector


Indus­tri­als

Vehicle Data Store


Indus­tri­als

Soft­ware­Fact­ory at a lead­ing auto­mot­ive soft­ware company


Con­sumer Goods & Retail

DevX Impact at a HR-Tech Company


Con­sumer Goods & Retail

Driv­ing Inter­na­tional Expan­sion for a Lead­ing Online Car Seller


Indus­tri­als

Accel­er­at­ing Cloud-Nat­ive Devel­op­ment for a Car Manufacturer


Com­mu­nic­a­tions, Media & Telecom

Retail Com­mis­sion Dia­gnostics Anom­aly Detection


Energy & Resources

Set­ting up a Reli­able and Power­ful Platform


Com­mu­nic­a­tions, Media & Telecom

Pre­dic­tion of cus­tomer prob­ab­il­ity to churn


Indus­tri­als

Early Detec­tion of Mech­an­ical Issues


Indus­tri­als

NLP​ in Auto­mot­ive Reporting


Com­mu­nic­a­tions, Media & Telecom

Oper­a­tional Data Lake


Con­sumer Goods & Retail

Real-Time Mar­ket­ing


Indus­tri­als

Auto­matic clas­si­fic­a­tion of cars by their model


Pub­lic Sector

Dies­el­tech Program


Com­mu­nic­a­tions, Media & Telecom

Data Recon­cili­ation


Indus­tri­als

Cent­ral­ized Data Warehouse


Indus­tri­als

Data as a Ser­vice – BI & Big Data CC


Com­mu­nic­a­tions, Media & Telecom

Oper­a­tional Work/Support in Data Warehousing


Indus­tri­als

Erosion Detec­tion with Com­puter Vision


Com­mu­nic­a­tions, Media & Telecom

Devel­op­ment and Imple­ment­a­tion of a Fully Auto­mated MLOps Platform


Con­sumer Goods & Retail

Pre­dic­tion and Alloc­a­tion Optim­iz­a­tion for sta­tion­ary Channels


Com­mu­nic­a­tions, Media & Telecom

Build­ing a Near Real-Time Recom­mend­a­tion System


Con­sumer Goods & Retail

Migra­tion AWS data plat­form to Google Cloud


Com­mu­nic­a­tions, Media & Telecom

Design and Devel­op­ment of a Google Cloud-Based Data Platform


Con­sumer Goods & Retail

Refact­or­ing, shift and new devel­op­ment of Data Ser­vices in Google Cloud


Estab­lish­ment of a Mas­ter Data Man­age­ment Foundation


Indus­tri­als

Cent­ral Data Lake


Replace­ment of a com­plex IBM Data­stage structure


CRM ana­lyt­ics and lead management


Stand­ard­iz­a­tion of eval­u­ation processes


Devel­op­ment of an ERP data hub


Estab­lish­ment and fur­ther devel­op­ment of a DWH


Devel­op­ment of a group-wide BI platform


Estab­lish­ment of a new sales report­ing system


Source data migra­tion with auto­ma­tion of the source data connection


Cloud self-ser­vice BI for data-driven cor­por­ate management


Devel­op­ment of vari­ous ML use cases with AWS


Replace­ment of an in-house soft­ware development


Com­pany-wide data integration


Imple­ment­a­tion of real-time data links


Con­sumer Goods & Retail

Smart Data Catalog


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