Introduction to MSSQL Server Analysis Services: Mastering Enterprise BI: Create Aging “Buckets” in a Cube

About the Series …

This
article is a member of the series Introduction to MSSQL Server Analysis
Services. The series is designed to provide hands-on application of
the fundamentals of MS SQL Server Analysis Services, with each
installment progressively adding features and techniques designed to meet
specific real – world needs. For more information on the series, as well as
the hardware / software requirements to prepare for the exercises we
will undertake, please see my initial article, Creating Our First Cube.

About the Mastering Enterprise
BI Articles …

Having implemented, and developed within, most of the major
enterprise BI applications for over ten years, and having developed an
appreciation for the marriage of ease of use and analytical power
through my background in Accounting and Finance, I have come to appreciate the
leadership roles Cognos and other vendors have played in the evolution of OLAP
and enterprise reporting. As I have stated repeatedly, however, I have become
convinced that the components of the Microsoft integrated BI solution
(including MSSQL Server, Analysis Services, and Reporting
Services) will commoditize business intelligence. It is therefore easy to
see why a natural area of specialization for me has become the conversion of
Cognos (and other) enterprise BI to the Microsoft solution. In addition to converting formerly
dominant enterprise Business Intelligence systems, such as Cognos, Business
Objects, Crystal, and others, to the Reporting Services architecture, I regularly
conduct strategy sessions about these conversions with large organizations in a
diverse range of industries – the interest grows daily as awareness of the
solution becomes pervasive. Indeed, the five-to-six-plus figures that many can
shave from their annual IT budgets represent a compelling sweetener to
examining this incredible toolset.

The purpose of the Mastering
Enterprise BI subset of my Introduction to
MSSQL Server Analysis Services series is to focus on techniques for implementing features in
Analysis Services that parallel those found in the more "mature"
enterprise OLAP packages. In
many cases, which I try to outline in my articles at appropriate junctures, the
functionality of the OLAP solutions within well-established, but expensive,
packages, such as Cognos PowerPlay Transformer and Cognos PowerPlay,
can be met – often exceeded – in most respects by the Analysis Services /
Reporting Services combination – at a tiny fraction of the cost. The vacuum of
documentation comparing components of the Microsoft BI solution to their
counterparts among the dominant enterprise BI vendors, to date, represents a
serious "undersell" of both Analysis Services and Reporting Services,
particularly from an OLAP reporting perspective. I hope to contribute to
making this arena more accessible to everyone, and to share my implementation
and conversion experiences as the series evolves – and, within the context of
the Mastering Enterprise BI articles, to demonstrate that the ease of
replicating popular enterprise BI features in Analysis Services will be yet
another reason that the Microsoft solution will commoditize Business
Intelligence.

For
more information about the
Mastering Enterprise
BI articles, see the section entitled
"About the Mastering
Enterprise BI Articles" in my
article Relative
Time Periods in an Analysis Services Cube, Part I.

Introduction

In
this article, we will look at a common business need, the aging of
values. Aging is typically a process by which the enterprise determines the
length of time that has transpired since a transaction (usually financial) has
taken place within an account. Examples of common subject areas for agings in
the business environment include accounts receivable, accounts payable and
inventory, among many less common uses.

Regardless
of the specific type of aging that we need to enact, we can apply the
principles we will examine in this article to reach a solution that works
within the OLAP environment. Moreover, and more to the point of our Mastering Enterprise BI series, we can replicate the
functionality provided in many accounting and financial applications, as well
as a plethora of "pre-fab" reporting solutions on the market, within
the integrated Microsoft BI solution.

In this article, we will:

• Discuss
  general aging concepts, and their pervasiveness in the business environment;

• Prepare for
  the exercise by creating a clone of the FoodMart Analysis Services
  database, within which we will age customer transactions, simulating accounts receivable
  inside the sample Sales cube;

• Enact Source
  Table Filters (dimension and cube) to limit our cube to a specified
  transaction date range.

• Create an Aged
  Periods shared dimension within the sample cube to provide "buckets"
  for date-based transactional data;

• Establish drillthrough
  capability for use in our verification process later.

• Verify
  adequacy of our solution by demonstrating the use of the new capabilities from
  the perspective of the Cube Browser in Analysis Manager;

• Look forward
  to a subsequent article where we create aging buckets through an alternative
  method;

• Generally discuss
  the use of our new structures in the ultimate reporting application. We will
  also look forward to a subsequent article where we employ the aging buckets
  that we create in this article within Reporting Services.