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So Thermo Fisher Scientific CIO Ryan Snyder and his colleagues have built a data layer cake based on a cascading series of discussions that allow IT and business partners to act as one team. Martha Heller: What are the businessdrivers behind the data architecture ecosystem you’re building at Thermo Fisher Scientific?
Previously, there were three types of data structures in telco: . Entity data sets — i.e. marketing datalakes . There were many of these structures across the business, multiples for each acquisition, each line of business, and each executive structure. and — more worryingly — “how can we be sure?” .
Lessons about data modeling, modernization, and automation include the following: Focus on fundamentals Companies place the highest priority on data quality, ease of use, analytics performance, and datagovernance. The lakehouse, data fabric, and data mesh have 8-12% usage each.
I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently DataLakes and Analytics , constantly building experience and capability in the DataGovernance , Quality and data services domains, both inside banks, as a consultant and as a vendor.
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