Data Warehouse Maturity Assessment (DWMA)
Does your Data Warehousing need guidance from our experts?

Designing and building a Data Warehouse (DW) system can all too often seem like a long, expensive and risky journey before business benefits are finally delivered. However, this need not be the case. With a wealth of knowledge and real-world experience accumulated over 20 years, VLDB Solutions can act as a guide whilst you embark on your Data Warehousing journey, with the aim of maximising your ROI. As a data warehouse system moves through the Data Warehouse lifecycle, challenges appear in the form of managing increases in the following:

» query complexity
» number and complexity of workloads
» data volume
» data history
» schema breadth/subject areas
» number of users
» number of tools used to access the system
» user expectations

The most successful Data Warehouse systems do not stand still – they move through the data warehouse lifecycle stages over the short/medium term. The clearly definable stages within the ‘Data Warehouse lifecycle’ all bring their own specific challenges, and rewards.

At which stage is your data warehouse in the data warehouse lifecycle?

Stage 1 - Data Warehouse - Report

A data warehouse system at Stage 1 is able to measure ‘what happened’ primarily through batch processing and some ad-hoc reporting capability.

An example of ‘what happened’ might be a static report of sales by region and product group for the previous week.

Stage 2 - Data Warehouse - Analyse

A data warehouse system at Stage 2 is able to understand ‘why it happened’ through batch processing and a moderate ad-hoc reporting capability.

An example of ‘why it happened’ might be the ability to understand sales by store within a region and by product within a product group through an ad hoc reporting capability.

Stage 3 - Data Warehouse - Predict

A data warehouse system at Stage 3 is able to understand ‘what will happen’ through a mixture of batch processing, ad-hoc reporting and predictive analytics.

An example of ‘what will happen’ might be the ability to predict which customer segments will respond to particular marketing campaigns such as cross-sell, up-sell etc.

Stage 4 - Data Warehouse - Operationalise

A data warehouse system at Stage 4 is able to understand ‘what is happening’ in real-time through a mixture of batch processing, ad-hoc reporting, predictive analytics, continuous updates and short (‘tactical’) queries.

An example of ‘what is happening’ might be the retailer that understands what is selling via real-time basket analysis, which leads to automated stock replenishment activity via the supply chain systems.

Stage 5 - Data Warehouse - Active

A data warehouse system at Stage 5 is able to ‘make it happen’ through a mixture of batch processing, ad-hoc reporting, predictive analytics, continuous updates, short (‘tactical’) queries and event-based triggers.

An example of ‘make it happen’ might be the knowledge that a customer has just made a balance enquiry via an ATM. As a result, there is a small window of opportunity to promote a relevant product or service to the customer before the transaction completes. A potential response to the balance enquiry event is the time-critical retrieval of a targeted offer via the ATM screen or receipt.


















To maximise the ROI and minimise the risk as you embark on the Data Warehouse journey, call us today and speak with one of our experienced consultants on 0845 247 0137.