Departmental Data Warehouse Options

Are you a data chaser?  Are you spending hours of your time grabbing data from various data sources, throwing the data in Excel, massaging the data to clean it up and correlate location or client numbers so you can see the total picture and finally analyze the data?  Do you love data chasing?  I didn’t think so.

There are solutions you can implement that will allow you to focus on analyzing your information for better decision making instead of data chasing.  Your options include:

  • Enterprise Data Warehouse (EDW)
  • Data Mart
  • Data Aggregation Cube
  • Continue Data Chasing and working harder not smarter

Let’s take a moment to explore your options:

Enterprise Data Warehouse

An EDW is typically owned and managed by IT.  The EDW is a staging area where data from multiple sources has been brought together and cleansed to make sure all data has integrity.  A properly built EDW will be a collection of Data Marts that have been well planned to work together to seamlessly tie the information from multiple sources together for the benefit of the business.

Data Mart

A Data Mart is a purposely built repository that typically serves a specific business area.  Often, clients will have a number of separate data marts that were built in silos, but it serves the particular business unit (like a financial data mart).

Data Aggregation Cube

For clients that do not have IT resources and their Finance departments aren’t knowledgeable in building Data Marts, they may decide to leverage tools to build a Data Aggregation Cube in a solution that uses cube technology like IBM Cognos TM1.  A TM1 cube is like an excel pivot table on steroids.   A TM1 cube can have up to 256 dimensions and handle large amounts of data.  A TM1 cube leverages the Turbo Integrator to pull both structure (like the Chart of Accounts) and data from source systems.  This allows Finance to build a staging cube that can aggregate data from multiple sources so the data can be leveraged for reporting, budgeting, forecasting and what if scenarios.

Advantages of an EDW

A properly built EDW will provide rapid access to data, providing one version of the truth, and can adjust to meet the changing business needs.

Disadvantages of an EDW

Building an EDW can be costly and time consuming.  If IT is responsible for the EDW, they must work closely with the business to understand the requirements or the result will not be successful.  To get funding for an EDW, you must be able to clearly articulate the business use and ROI.  To be successful, the business must communicate their requirements and uses and partner with IT.  The EDW must be designed and built to maximize performance and be flexible enough to meet the changing needs of the business.

Advantages of a Data Mart

A data mart can be a stepping-stone to realizing the benefits of an EDW; however, the various marts must be built considering the needs and the data of other data marts.  A proper plan for integrating multiple marts together must be established early on in the development phase, with constant communications between the builders of the various marts.  Data Marts can be built fairly rapidly, allowing the business to achieve quick results.

Disadvantages of a Data Mart

A data mart will typically still require IT resources to build the mart.  If not designed considering additional marts, an organization can end up with too many copies of the data, as multiple marts could contain the same data.  There is a risk of multiple data marts displaying different results for the same data if updated at different times.

Advantages of a Data Aggregation Cube

A data aggregation cube built in a cube-based technology like IBM Cognos can empower Finance to maintain their own cleansed copy of the data and leverage it within Finance.  They can update the data with ODBC connections to the data sources and have the flexibility to work with the data in a multi-dimensional structure for better analysis and what if modeling.  Data aggregation cubes can be built rapidly to meet the immediate needs of Finance.  Typically, data aggregation cubes are significantly less expensive to build than a Data Mart or EDW.

Disadvantages of a Data Aggregation Cube

Data aggregation cubes are not as scalable as a data mart or data warehouse to meet the needs of the business beyond the Finance organization.   The cubes will need to be maintained by Finance and typically do not contain as much drillable information as a mart or EDW.

So depending on your resources’ haves, needs and pains, you have a number of options that can help you escape the data chasing world often seen in Finance departments.  So, isn’t it time to get your life back and enjoy analyzing data instead of chasing it?  If so, Lodestar Solutions would be happy to discuss your goals and options to help create a plan to realize the benefits of better data, leading to better decision making.

If you would like to learn more, contact Lodestar Solutions 813-254-2040.

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