Mastering Enterprise BI: Time Intelligence Pt. I

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 (“Analysis
Services
”), with each installment progressively presenting features and
techniques designed to meet specific real-world needs. For more information on
the series, please see my initial article, Creating Our First Cube. For the software components, samples and tools
needed to complete the hands-on portion of this article, see Usage-Based Optimization in Analysis Services 2005, another article within this
series.

About the Mastering Enterprise BI Articles…

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 – or outstrip – 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 integrated Microsoft BI
solution to their counterparts among other enterprise BI vendors, to date,
represents a serious “undersell” of both Analysis Services and Reporting
Services
, particularly from an OLAP reporting perspective. I hope, 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 integrated 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

As I stated in my article
Introduction to SQL Server 2000 Analysis Services:
Handling Time Dimensions
, it is a rare thing to
encounter an instance of an OLAP cube that does not require a Time dimension.
Throughout years of business intelligence consulting, I have only witnessed
this scenario a handful of times within a production environment. Although
there often seems to be no shortage of people to argue any side of any
statement, few of us would disagree that the measurement of activity over
time
– and, hence, the Time dimension that supports this capability
– is important to both analysis and operational management in general.

As an aside, I refer to
the dimension as a “Time” dimension because my preference is to name
dimensions after the generic concepts they represent – thus “time”
versus “date.” While I can certainly live with “date” as the name of the
dimension that represents the concept of time, I do not agree with the argument
advanced by some that “date” is the more appropriate choice because, after all,
we are working with “date” hierarchies that may not subanalyze to “time” – as
in “time of day.” My response is that “date” itself is a subordinate member
within the larger concept of time, and typically a level within the Time
dimension, hence my choice of “Time” as a dimension name.

(I hope that not too
much angst is aroused by Microsoft’s decision to use terms like “time
intelligence,” “server time dimension,” “time periods,” and the like,
throughout Analysis Services and its documentation, for those who might confuse
time to mean “time of day …”. Moreover, I heartily encourage substituting
“date” for “time” as a dimension name when the latter leads to undue stress,
justified or not. This is one of the beauties of working within semantic
layers…)

The Time dimension
has several unique characteristics, relative to other dimensions within our
cube models. Among these is the fact that all businesses employ the same core calendar
time hierarchy
of days (and sometimes lower levels), weeks, and months,
together with quarters and years (with various subdivisions included to meet
local business and reporting needs) – even though treatment of these various
levels can vary widely within the alternative considerations of fiscal
years and periods. Moreover, the pervasive nature of time – within and
surrounding all organizational activity – means the universal juxtaposition of
the Time dimension and the other dimensions within our cube models.
Another characteristic of time is its incremental continuation, like a
ray in geometry, from a fixed beginning to a typically indefinite end.

The Time dimension
has received special focus within the design of enterprise business
intelligence applications. Common features include capabilities ranging from
the recognition of date fields with minimal intervention to the automatic
generation of members of the Calendar time dimension as a part of cube design
and / or creation. Most of the dominant applications have even offered support
for the dynamic creation of various “relative” time periods and aggregations. (For
a discussion of some of the specific support provided by leading applications,
as well as the Analysis Services 2005 approach to meeting and exceeding
these features, see other articles within my Introduction
to SQL Server 2000 Analysis Services
series here at Database
Journal
).

As we
shall see in this article and its successor, Analysis Services 2005 witnesses
further enhancements with regard to supporting the Time dimension.
Moreover, in addition to these extended features, support for the creation of
virtually any “custom” relative time aggregation that we might need remains
readily available to assist developers. In this article, we will gain some
familiarity with creating a Time dimension within Analysis
Services 2005
, focusing upon enhanced features as we encounter them. In
Part II
, we will examine new features that support the easy addition of Time
intelligence
within our cube models. Our examination of working with the Time
dimension within Analysis Services 2005 includes:

  • An introductory
    discussion of the Time dimension, focusing on unique characteristics that distinguish it from other
    dimensions within our cube models;

  • Mention of the
    special focus that has been given to the Time dimension within the
    design of enterprise business intelligence applications, and features that
    have been added to the applications to provide an “assist” with the Time
    dimension as a part of cube design and / or creation;

  • A look ahead
    to our sequel article, wherein we discuss the support, offered by most of the
    recently-dominant applications for the dynamic creation of various “relative”
    time periods and aggregations, and how this support has been enhanced in Analysis
    Services 2005
    ;

  • Creation of a
    new Analysis Services Project in preparation of our practice session;

  • Creation of a target
    database
    within SQL Server Management Studio for the schema
    generation procedure
    within our practice session;

  • Ascertaining connectivity
    of the relational data source, along with other preparatory procedures,
    within the new Analysis Services 2005 Project;

  • Creation of a rudimentary
    cube, via the “top down” approach (whereby no underlying data source is in
    place), containing a Time dimension, upon which to base our general
    examination;

  • Examination of
    the structure of the new Time dimension;

  • Generation of
    the underlying schema for the new cube
    model, including the generation of a Time table design, as well
    as its subsequent population, from within the cube model that it is designed to
    support;

  • Review of the new
    Date dimension within the Designer; and

  • Review of the generated
    schema, and the populated table supporting the Date dimension, within SQL
    Server Management Studio
    .
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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