Data Vault 2.0 Modeling and Cost Optimization

INTRODUCTION In this whitepaper, we will analyze the case of a gas provider that is undergoing organizational restructuring, including a remodel of their data warehouse to improve time responses and insights. CASE DESCRIPTION Faced by an ongoing transformation, this company was in need of a solution capable of dealing with technological adjustments, which also helps to build a strong foundation to confront future changes. Even though their current settings, architecture and developments were working properly, the current challenges they were facing made them struggle in order to keep up with the business requirements: better performance and faster availability of information. Therefore, a flexible data warehouse (DWH) with the capability of adapting to all of these changes, and upcoming ones, is fundamental to ensuring sustainability in the long term. But not only the DWH is an important aspect, but also the different factors around it, like the DWH team’s agility to respond to these changes, with enough team coordination to apply to the day-to-day project’s task.

So in this search, the referenced energy enterprise reached out to Scalefree to get help and find a suitable course of action. The proposed solution was first to implement a work methodology that allows the data warehouse team to focus on adapting to changing business requirements within two week boundaries. Long-term project planning is also relevant, but the main focus exists on iterative and incremental development instead. This allows the DWH team to stay agile and confront any business requirements quickly. On the other hand, the implementation of a Data Vault 2.0 architecture was also a key part of the solution, since it allows users to quickly absorb changes to the data model and other design characteristics. Therefore, the closing or opening of source systems can be quickly absorbed with a minimum impact. Besides, integrating data becomes easier thanks to the multi-tier architecture, which also improves data quality by having centralized repositories.

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