Business Intelligence (BI) is todays ‘MANTRA’ chanted by almost every business. Companies want to outsmart the competition. Companies are ready to invest big bucks and human power to build a sophisticated BI system so that they can have the knowledge that others don’t and seize on the opportunities in the market before others do. BI shows the Future Value of Your Business.

BI systems need DATA and every business has terabytes of real data which can provide them with the information and knowledge they need to make the right decisions on time. But the key is to turn that data into information in a timely, efficient and effective manner once the WHAT AND WHY questions are answered i.e., what information is needed, what matters and why that is required.  In today’s market, every business is in a RACE. The race to conquer others. The race to generate more gains/profits. The race to foresee the risks early on so that they can be avoided.  So time is of the essence here.

An optimized BI system integrates large volume of external and internal near real time data to allow management to create opportunities by making intelligent decisions after performing predictive analysis of their approach on the business. A good BI System is like a GPS. An effective GPS is one that not only shows you a route to your destination but also guides you when you hit roadblock, gives up-to-date external conditions (constructions / traffic) information, provides multiple routes to choose from, suggests you with alternatives for shorter and fastest routes, predict the total time based on your driving behavior, tells you what to expect next etc. Just knowing the path to your destination is not sufficient. You need to know many other factors during the whole ride to reach destination on time and without any hurdles.

For a good integrated BI system, a good Data warehouse architecture needs to be in place.  Data warehouse architecture is “an integrated set of products that enable the extraction and transformation of operational data to be loaded into a database for end-user analysis and reporting”. Below are the pictorial representations of different “flavors” of DW architectures.

Methodologies used by different architecture:

Kimball’s DW Architecture – Is based on ‘Bottom-UP’ methodology.

Inmon’s DW Architecture – Is based on ‘Top-Down’ methodology.

Dan Lindstedt’s Data Vault DW Architecture – Is based on ‘HYBRID DESIGN’

The first two design methods have some limitations for Data Warehouse layer such as inflexibility and unresponsiveness to the changing departmental needs during the implementation phase, insufficient auditability of data back to its source system, inability to integrate unstructured data, inability to rapidly respond to changes (organizational changes, new ERP implementations) or difficult to load type 2 dimensions in real time. This is where DATA VAULT came in to rescue. Data Vault follows a ‘HYBRID DESIGN’ methodology which follows ‘TOP-DOWN ARCHITECTURE WITH A BOTTOM-UP DESIGN’.

The model is a mix of normalized modeling components with type 2 dimensional properties. In this model, the DW serves as a backend system that houses historical data which is integrated by the business keys. All data ‘good, bad, incomplete’ gets loaded into the data vault and all the cleansing and application of business rules takes place downstream i.e., out of DW. This means that Data Vault model is geared to be strictly a data warehouse layer, not as a data delivery layer which still requires physical or Virtual star schemas or cubes for Business Users or BI tools to access.

Bill Inmon in 2008 stated that the “Data Vault is the optimal approach for modeling the EDW in the DW2.0 framework.”

In Part 2 and 3, I am going to explain different components of Data Vault and it’s power with the help of some examples.  That will clearly explains why the Data Vault should be a preferred “flavor” for different businesses.


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