WP_Data Management Best Practices

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6.3.2 Cloud Deployments

As mentioned in section

above, deployment to the cloud does not change the fundamental data management

processes: this includes of course data quality and data governance, which still need to be applied to information that

is warehoused in the cloud.

Data quality transforms on cloud integration tools are conceptually the same as their on-premises counterparts.

However, be attentive to the fact that, even within the same vendor, cloud tools do not always offer the same

capabilities as on premise tools. One area is transformations; another is workflow management. As an example, on

the former, per our experience, this is particularly true for cleansing and matching transforms: cloud integration tools

are generally simpler. This simplicity is sometimes beneficial in terms of ease of use, as cloud tools are more modern

and have benefited from the on premise tools maturing process, but some tools may lack functionality.

6.3.2.1 Cloud Application Integration

As mentioned in section

above, IT departments are now expecting that hybrid system landscapes connecting

cloud and on premise applications will become the standard way in which they will manage the organization’s IT

assets in the future. We also mentioned that our experience is that the best architecture concept to handle this list of

requirements is a data governance and integration platformavoiding point-to-point interfaces between applications.

To provide governance and high data quality, this platform converts data from applications to a global model, reducing

the O(n2) possiblemappings between applications to 2Nmappings. It must store reference data comprised of (i) basic

identities of master data elements of the connected systems/applications, (ii) reference metadata as for capturing

domain mappings, i.e., the relationships between valid values from source applications to the conformed dimension

and fact values, and (iii) retrieve the metadata models of the connected systems and their subsequent transformation

flows (which are to be modeled through the platform). It may also use enrichment services (see next section) to

complete data elements.

6.3.2.2 Data quality services

Cloud architectures allow developers to take advantage of real-time web services so that data transformations for

improving data quality can be performed as a service. Common as-a-service use cases include:

1. Several vendors now expose public cloud services to perform data validation for customer, vendor or

employee master data, for example, address cleansing and enhancing (correcting or enriching empty data

elements, for instance unknown postal codes).

2. There are vendors that allow to enrich specific types of data: Axciom for individual customer data and Dun &

Bradstreet dataset for organization data.

3. Geocoding and reverse geocoding are also enrichment services available from vendors.

4. Another private cloud or on premise well known use case is for organizations to expose a service to perform

matching against their master data to ensure that they are preventing duplicates.