Introduction to MSSQL Server 2000 Analysis Services: Semi-Additive Measures and Periodic Balances

About the Series …

This
article is a member of the series Introduction to MSSQL Server 2000
Analysis Services
. The series is designed to provide hands-on
application of the fundamentals of MS SQL Server 2000 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.

Note: Service Pack 3 updates are assumed for MSSQL Server 2000, MSSQL
Server 2000 Analysis Services
, and the related Books Online
and Samples. Images are from a Windows 2003 Server
environment, upon which I have also implemented MS Office 2003, but the
steps performed in the articles, together with the views that result, will be
quite similar within any environment that supports MSSQL Server 2000 and MSSQL Server 2000 Analysis Services ("Analysis
Services"
or "MSAS"). The same is generally true,
except where differences are specifically noted, when MS Office 2000 and
above are used in the environment, in cases where MS Office components
are presented in the article.

Overview

Most of the measures
with which we work in our daily Analysis Services environments are additive,
and include various options for easy aggregation, comprised of the
ever-familiar SUM, MAX, MIN and COUNT. Most base
measures involving transactions, such as sales or direct expenses, are
inherently additive. We typically find additive measures simple and useful in
our work within analysis and reporting systems, because there are no inherent
restrictions on how they are used in our cubes. Such measures can be sliced and
diced in any "direction," for example. Using the four aggregation
types to derive aggregates from previously aggregated results is only one
example of how we can easily leverage the power of OLAP as implemented in
MSAS. With additive measures, aggregation is applied consistently to all
dimensions: the measures roll up equally well, within the same aggregation type,
across all.

However, as most of us
are aware, semi-additive measures exist in the business environment, as
well. Periodic measurements, such as account balances (for example, the
daily balance of a bank account), level measurements (such as on-hand inventory
quantities or personnel headcounts), and the like, do not share the qualities
of fully additive measures. Semi-additive measures are additive across some
dimensions within the cubes they inhabit, but are not additive across
one or more of the dimensions of the cube.

As an illustration, an inventory
level
might be additive along the Product, Store and Warehouse
dimensions of a cube, but would be non-additive across the Time
dimension of the cube. Alternatively, a daily bank account balance might
certainly be aggregated usefully in an average over Time
(a common case would be an average daily balance), and perhaps in minimum
and maximum contexts, but summing the daily balance over time would
present a meaningless result.

In
this article, we will explore the management of semi-additive measures,
creating a calculated measure (a calculated member that belongs to the Measures
dimension) that is not fully additive, to meet the business requirements
of a hypothetical group of information consumers. Within our exploration of
the semi-additive
measures
, we will accomplish the following:

  • Create a copy
    of the Warehouse sample cube for use in our practice exercise;

  • Prepare the
    cube further by processing;

  • Perform a
    practice exercise, using an illustrative set of business requirements as a
    specification for creating a semi-additive measure (a calculated measure)
    in our practice cube;

  • Explore an
    initial approach to creating the simple inventory balance calculated measure,
    and explain its shortcomings as a fully additive measure;

  • Modify the calculated
    measure
    to cause it to exhibit the appropriate semi-additive behavior;

  • Discuss the
    results datasets obtained within the steps of our practice example.
William Pearson
William Pearson
Bill has been working with computers since before becoming a "big eight" CPA, after which he carried his growing information systems knowledge into management accounting, internal auditing, and various capacities of controllership. Bill entered the world of databases and financial systems when he became a consultant for CODA-Financials, a U.K. - based software company that hired only CPA's as application consultants to implement and maintain its integrated financial database - one of the most conceptually powerful, even in his current assessment, to have emerged. At CODA Bill deployed financial databases and business intelligence systems for many global clients. Working with SQL Server, Oracle, Sybase and Informix, and focusing on MSSQL Server, Bill created Island Technologies Inc. in 1997, and has developed a large and diverse customer base over the years since. Bill's background as a CPA, Internal Auditor and Management Accountant enable him to provide value to clients as a liaison between Accounting / Finance and Information Services. Moreover, as a Certified Information Technology Professional (CITP) - a Certified Public Accountant recognized for his or her unique ability to provide business insight by leveraging knowledge of information relationships and supporting technologies - Bill offers his clients the CPA's perspective and ability to understand the complicated business implications and risks associated with technology. From this perspective, he helps them to effectively manage information while ensuring the data's reliability, security, accessibility and relevance. Bill has implemented enterprise business intelligence systems over the years for many Fortune 500 companies, focusing his practice (since the advent of MSSQL Server 2000) upon the integrated Microsoft business intelligence solution. He leverages his years of experience with other enterprise OLAP and reporting applications (Cognos, Business Objects, Crystal, and others) in regular conversions of these once-dominant applications to the Microsoft BI stack. Bill believes it is easier to teach technical skills to people with non-technical training than vice-versa, and he constantly seeks ways to graft new technology into the Accounting and Finance arenas. Bill was awarded Microsoft SQL Server MVP in 2009. Hobbies include advanced literature studies and occasional lectures, with recent concentration upon the works of William Faulkner, Henry James, Marcel Proust, James Joyce, Honoré de Balzac, and Charles Dickens. Other long-time interests have included the exploration of generative music sourced from database architecture.

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