Sygnature Discovery
Data Consolidation, Transformation
and Migration to Oracle NetSuite
Client Challenges
Data migration from
multiple sources
Sygnature needed a full and complete dataset on NetSuite, yet their legacy data required significant validation and cleansing. This required the team to manually prepare CSV files for mapped or final data loads from varying sources and upload them individually into
Oracle NetSuite.
To preserve the correct relationships between entities such as customers and projects, data uploads had to be carefully staged. Additionally, the system relied on NetSuite to generate unique IDs for each entity. These constraints added complexity, heightened the risk of data errors and ultimately slowed the process, increasing the likelihood of project delays.
Unaligned data
Relationships between data entities were not inherently maintained due to data being stored in different formats across multiple systems, along with high levels of Redundant, Obsolete or Trivial (ROT) data. As a result, significant manual effort was required to identify and establish these relationships across various file formats and structures, ensuring data alignment and
proper utilization.
Proventeq's Solution
Discovery workshop
To fully understand the data Sygnature held, where it was located and what would need to be migrated, we organized a data workshop which allowed a deep dive into their vast, scattered data holdings so a plan could be designed to clean their library of duplicates and any redundant data sets. The outcomes of which were a data strategy and data migration plan.
Streamlining data uploads
Proventeq created client-specific data templates which could be loaded onto NetSuite as a one-click solution using Azure Data Factory, which allowed the project team to create, schedule, move and transform data, generating all respective input files, so iterations in loading them were reduced. The final product was much smoother and manual efforts were reduced drastically.
Ensuring data alignment
To help align the data, we implemented a relationship between various datasets in a staging database using SQL Views. While not a universal solution, this significantly reduced the time and effort needed to obtain source data in a specific file format from the client, which greatly improved the process.
