Predictive Analytics – Optimization – Performance Management

Anaplan Data HUB

Topic: Implementing an Anaplan Data HUB model to centralize and standardize source system Master Data and Data

The approach of centralizing source system Master Data and Data is not new or unique to Anaplan, and has been a common, proven approach with Data Warehouses for decades.

The Anaplan Data Hub is an approach where Master Data (ie. Hierarchy, Products, Accounts etc.) and Data that will be used across multiple Anaplan Models are loaded and maintained in a single Anaplan Model (“Data HUB”). The Data Hub is then a single, central model (repository) for development & maintenance of source system integration, validation of data loads, transformation of data, standardization of naming all resulting in a streamlined and efficient Anaplan Implementation. Anaplan automation synchronizes Master Data and Data across Anaplan Models, as well as validating if any models Master Data and / or Data is “out of sync”.

Though centralizing source system data and metadata in a Data HUB model is the preferred approach, a Data HUB will create data redundancies as Master Data and Data will be retained across two or more models. There are two primary considerations when assessing the impact of data redundancy, first how much space will the data redundancy require? This is essentially determined by calculating the amount of space required for the Anaplan HUB, which itself represents the totality of potentially redundant data times the number replications of the data. Second, what is the System Administration effort and Organization risk to maintain redundant Master Data and Data vs Decentralizing Master Data and Data to their applicable models? Depending on an organization’s business processes, the System Administration efforts, with efficient use of Anaplan Automation likely will be the same as using a Data HUB.

The Data HUB is the preferred approach, but every organization is unique and the Anaplan Platform, as with any multi dimensional modeling tool requires efficient and effective storage of Master Data and Data to ensure performance.