Data Integrity Promotes Data Trust

0 March 24, 2024

Comprehensive data integrity between and within datasets is ensured using Maxxphase’s Data Compatibility, thereby fostering trust in your data. Modern technologies, such as AI, require trustworthy, directly interoperable datasets which can only be efficiently produced using our data compatibility approach.
 

The Disparate Data Foundation – Data You Can’t Trust

 
The dirty little secret about traditional data architectures is that little to no data integrity is enforced between datasets. Each dataset is therefore disparate and offers no direct dataset joins or data unification. Datasets that lack data integrity between them are dataset silos. Because they lack data integrity, you should not trust data combined from multiple disparate datasets!

Beyond the lack of data integrity, disparate datasets, as a group, are disjointed datasets with conflicting metadata and data content. When you transform and integrate disparate datasets, the data from each source dataset is removed from its native data context and transformed into a new ‘integrated’ data context. Is the source dataset now equal to its slice of the integrated dataset? No, it is not. At best, the source dataset has been recast; at worst, it has been corrupted. When someone familiar with the source dataset reviews their slice of the integrated dataset, they often notice discrepancies and no longer trust the integrated dataset. Why should anyone trust the integrated dataset when the resulting recast data is unfamiliar to them?
 

The Compatible Data Foundation – Data You Can Trust

Something very interesting happens when you enforce data integrity among your datasets. These datasets are no longer disparate and, as a group, they become directly interoperable – able to join without data transformations. As a result, you can seamlessly unify these compatible datasets on demand. Tedious and expensive data integration projects are no longer needed as data integration is replaced by direct dataset interoperability. Data transformations, required for data integration projects, are no longer required or wanted to recast and consolidate source datasets.
 
By using Maxxphase’s Data Compatibility Standards to enrich your datasets non-invasively, you ensure that the proper data integrity is enforced between your now Universally Interoperable Datasets. Later, when your Universally Interoperable Datasets are unified, each dataset slice is equal to each source dataset. Since the Universally Interoperable Datasets are not data transformed, no differences are found because no data was recast. So, you can trust data combined from Universally Interoperable Datasets since the datasets have comprehensive data integrity enforced.
 

Data Integrity Across Your Data Architecture

One very important process for creating trusted data is to enforce comprehensive data integrity throughout your data architecture. Each properly designed dataset has internal referential data integrity enforced. Universal dataset interoperability also ensures external data integrity between datasets. Therefore, every dataset of your data architecture should be a Universally Interoperable Dataset. These datasets seamlessly unify your data assets into a Modular Data Fabric. This Modular Data Fabric is filled with trustworthy analytics-ready and AI-ready data, and is the perfect data architecture for your business’s modern technology initiatives.
 
We feel the slogan ‘garbage in, garbage out’ is still relevant in disparate data foundations. If your data is a mess, or the volume of data is too great to handle or too costly, or if you need a Single Source of Truth with high-quality and accurate data to support advanced technologies, the Modular Data Fabric is your only logical solution.