Solving the Data Mess with Data Compatibility

To put it simply most data is a mess. How did we get here and how can we fix it?   

The truth is we got here by relying on a cottage industry approach to data projects: 

• Data modeling methodology lacks standardization  
• The project scope is limited to fix a certain need, we don't think beyond the original project 
• Datasets are not designed to be easily integrated down the line 

This leads to inherently disparate source datasets, that traditionally rely on data transformation projects to resolve the dataset disparity problem. While this approach helps to treat the symptoms, it does not correct the source of the problems.  

This direct dataset interoperability is achieved by establishing master data commonality between datasets. This commonality is achieved by:

• Creating Consistency in the database table structures through standardization 
• Standardizing the Master Data Content Values  
• Using globalized Primary Keys the extend beyond the dataset 

These methods create master data commonality between datasets. With this new shared master data commonality these dataset become directly interoperable. This approach allows for the sharing of data across datasets without transformation or aggregation. This results in a distributed data architecture wherein the datasets collectively function in a manner indistinguishable from a single consistent dataset. This approach significantly reduces the complexity of sharing data in order to support business needs. By attacking the root of the problem, we can start cleaning up the messy state of data.  

To learn more please feel free to reach out or visit www.maxxphase.com