Buy versus Build — Data Warehouse

Broad Coverage of Pre-Built Business Intelligence Content for Oracle E-Business Suite

Speed Your Time to Benefit, Reduce Risk and Gain the Lowest Cost of Ownership

Data warehouse projects are complex and demand a variety of skills to build and maintain. The upfront work: gathering reporting requirements, defining data models, and creating the metadata that catalogs the content of the data warehouse is a huge undertaking. It’s not uncommon for users to have difficulty articulating their reporting needs, so just getting a project off the ground can be a hurdle.

One of the most technically challenging and labor intensive parts of a custom data warehouse project is writing the data extraction, transformation, and loading (ETL) code — mapping tens of thousands of columns from various source systems into the data warehouse. The ETL architecture should be able to not only load historical data, but also efficiently identify changes and deletions in order to quickly update the data warehouse on a timely basis.

While many reports may only need data that is current as of last night, an operational data store may be necessary to address near real-time reporting requirements to avoid bogging down the transaction system.

Integrating a custom data warehouse with an organization’s BI platform involves the time-consuming process of defining the semantic layer metadata that enables users to build reports and dashboards.

There really is no end date to a custom data warehouse project.

Keeping up with changes to the database structures in the source systems introduced by application patches and upgrades is often overlooked in the resource planning phase of a project, but can require significant ongoing development effort. This is perhaps the single largest source of ongoing cost savings from using a packaged data warehouse offering like Noetix Analytics.

Noetix Analytics can speed time to value while dramatically lowering costs and risks associated with custom development projects. Noetix is able to do this by providing pre-built data models, ETL maps, automated semantic layer metadata generation, and dashboard and report templates* in a system that is integrated with several leading BI platforms which can maximize your existing BI platform investments. Noetix Analytics is unique among the packaged solutions for Oracle E-Business Suite customers providing broader coverage for more applications and deeper coverage with mappings from nearly 30,000 fields – almost 10 times as many as competing solutions.

Optimized for Oracle E-Business Suite, Noetix Analytics automatically integrates two dozen key flexfields and hundreds of descriptive flexfields. Furthermore, Noetix Analytics is based on a metadata-driven architecture, which allows customers to be more agile when extending the system and to integrate data from third-party or custom applications.

KEY QUESTIONS

As you evaluate a buy versus build decision or even when comparing packaged alternatives, get answers to these key questions:

  • Does the vendor offer pre-built solutions for the Oracle E-Business Suite we are using? (Noetix Analytics Coverage)
  • Does the vendor’s solution require manual customizations to integrate flexfields, HR Extended Information Types and Special Information Types, and other application configuration options?
  • What is the vendor’s strategy for performing incremental updates?
  • Can incremental updates be performed multiple times per day?
  • Does the vendor support our existing or future BI platform(s)?
  • What is the total cost of acquiring, implementing, and maintaining the packaged solution, including prerequisite software and hardware components?
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“We eventually determined that a packaged analytics data warehouse solution would be best for our needs and selected Noetix due to their metadata-driven technology. Noetix also offered the ability to provide content that integrates with the Oracle Business Intelligence platform and other BI reporting tools we have in place, which was critical to our enterprise data management strategy.”

Kevin Loo
Director of Data Architecture
ARAMARK Uniform Services

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