Before we get started, here's a Wikiedia entry for OLAP Cubes.
The Method :-
Aim for an iterative Approach with substantial User involvement.
Review results after each major Iteration.
Choose between a Star Schema or a Snowflake Schema?
Star Schemas give better performance, and are therefore recommended when volumes are high.
Should dimensions be shared or private ?
Usually choose shared to ensure widest usage later. Only choose private to protect
restricted or secure data.
What is “Dimensional Reporting ?
Dimensional modeling utilizes "measures" and "dimensions".
Measures include a variety of key performance indicators, and may
include "simple" measures (amounts paid to providers, patient days, etc.) as well as
computed measures or ratios, such as cost per member per month.
Measures can be presented at various levels of summarization or drilldown, depending on
how the dimensions of the analysis are displayed.
For example, a calculation of a cost PMPM ratio for a specific primary care physician for
a specific month would automatically limit the cost numerator and the member-months denominator
to the specified PCP and time period.
Dimensions represent the variables by which measurement is performed, such as date, location,
product code, etc.
Dimensions can be arranged in hierarchies, allowing users to drill down through the data.
For example, the “service date” dimension contains the hierarchy of YEARS drilling down to QUARTERS,
and then to MONTHS.
Careful design of the hierarchy in a dimension facilitates drill-down reporting by designing the hierarchies to be
intuitive and to follow the thought process of the analyst.
A dimensional design model allows the user to view the measures in any reasonable combination of the dimensions.
In Microsoft Excel pivot tables this is done by “dragging” a dimension name to a row or column heading in a table,
which will automatically arrange the measures in the table according to the dimensions displayed.
This provides a practically limitless set of "reports".
Users are not limited by predetermined capabilities of the system, since each dimension can be used as a row or a
column in a report, or as a "filter” to select certain data to appear in that report.
Dimensional reporting is not only useful in designing interactive tools, but also in design of static reports.
By designing Reports using common definitions for these headings, such confusion can be avoided.
In addition, using dimensional design for static reports greatly facilitates the ability to drill down to
those reports using the reporting tools described below.
What is an OLAP cube ?
An OLAP cube is a specially designed database that is optimized for reporting.
While most databases designed for online transaction processing such as those used in claims processing are
designed for efficiency in data storage,
OLAP cubes are designed for efficiency in data retrieval.
This means that the data is stored in such a way as to make it easy and efficient for reporting.
Regular “relational” databases treat all data into the database similarly, however
OLAP cubes categorize data into "dimensions" and "measures".
Measures represent items that are counted, summarized or aggregated, such as costs or units of service.
Dimensions are variables by which measures are summarized, such as hospitals, physicians, or dates of service.
This organization of data greatly facilitates the ability to formulate data requests based on real-life situations.
In addition, many of queries that could be posed to the data are "pre-aggregated" in the database such that
the answers have already been precalculated and can be reported without delay.
The term "cube" comes from the geometric object that has three dimensions.
OLAP cubes can have many more dimensions than 3, but the term continues to apply.
What is a pivot table ?
A pivot table is a Microsoft Excel feature that allows access to data that is organized into dimensions and measures.
It acts as an "OLAP client" to allow users to interact with data stored in OLAP cubes using the familiar Excel
Can I use other programs to access OLAP cubes ?
Many other programs can be used as OLAP clients.
Microsoft Data Analyzer allows arranging dimensions and measures in a highly graphical format, while
Crystal Analysis capitalizes on its ability to create Web-based templates.
Any of these clients can be used with any OLAP cube, including those designed by DGA Partners.
We've found that Excel generally provides the best blend of free-form analysis with exceptional functionality.
Why are OLAP cubes important ?
Before OLAP technology was well developed, data had to be extracted from databases using "queries".
This meant that the analyst had to structure a request to the database for the information desired, and then
submitted this query to the database server.
That server would processing query and return the results.
Depending on the size of the database and the data requested, this query could take minutes or hours to complete.
In this sense, the "online" aspect of this type of reporting is questionable.
OLAP cubes are fundamentally different in that they "pre-aggregate" the data used to answer many of queries that
This pre-aggregation occurs when the cube is built, which means that this process is already completed when the
user queries the data.
In addition, the size of an OLAP cube depends on the number of measures and dimensions and contains --
it may have no relationship to the side of the initial data set.
Therefore, a claims data set having millions of members can be consolidated into a relatively small OLAP cube
that can return data almost instantaneously.
OLAP technology has been around since the mid-1990s, but has developed significantly over the last two to three years.
Prior to that, the need for efficient storage of data took priority over the need for efficient reporting, simply because
disk space was so expensive.
In addition, the availability of faster CPUs has made it possible to compute aggregations on huge data sets within a
reasonable period of time.
Finally, the tools necessary to design OLAP cubes have become much more powerful over last few years
Analytical products designed more than two years ago are likely to be based on the older query-based reporting technology.
They can never be as fast or as user-friendly as those based on OLAP data sources.
Does an OLAP cube allow me to "drill down" into my data?
Yes, this is a unique feature that is part of the OLAP database structure.
When designing an OLAP database, dimensions are structured into "hierarchies".
For example, service dates can be arranged in a hierarchy of days, months, quarters, and years.
Similarly, diagnoses can be arranged by major and minor categories, drilling down to the individual diagnosis code.
The OLAP cube "knows" the hierarchy, so if the analyst issues the command to "drill down", the cube knows the
next level of data to be presented.
Our claims data is already contained in a data warehouse. Why do you have to create another one?
The term "data warehouse" has been used to describe many different types of databases.
Frequently, it is used to describe a collection of tables containing claims data in a highly normalized form, meaning that
the data is organized for maximum storage efficiency.
While reports can be generated from such a database,
it is often cumbersome to do so because of the organization of the data.
In addition, to meet reporting needs it is often necessary to include information that is not contained in the
Claims data warehouse, such as IBNR factors, administrative adjustments, or other similar data that is found in
spreadsheets or desktop databases.
Before efficient reporting can be done, this data must all be brought together, relationships built, data integrity
verified, dimensions and measures identified and made consistent, and the whole structure optimized for use in
developing OLAP cubes.
How can we can access OLAP cubes without servers on our site ?
Several alternatives are available.
First, we can make the data available to you over the Internet using a standard Internet Explorer interface.
In this system, you can connect to one of our servers and open a "terminal server session".
From the point on, you'll be working on our server, even though the desktop appears on your computer.
You can use Excel, or any other OLAP clients, and print reports and analyses to your local printers.
You can also copy cells from the server’s desktop into a spreadsheet located on your own desktop;
however the local spreadsheet will not have OLAP functionality.
This approach is especially useful for clients whose computers may be several years old, and might be
too slow for effective OLAP analysis.
This allows you to utilize our state-of-the-art servers without upgrading your own hardware.
It also avoids the need for you to install or set up any software -- we can create a desktop for you
that contains everything you need.
This alternative offers the advantage of "instant updating", in that data that we have process
is immediately available for your use, without having transfer it from our site to yours.
It also require a full-time connection from your computers to the Internet.
For more information about this service, see Using the DGA Partners Secure Data Server below.
Is there a way that we can bring the OLAP cubes "in-house", so that we don't need constant Internet access?
Yes, we can install the OLAP cubes on servers located your site. Actually, a "server" is unnecessary --
most computers built within the last year, and populated with sufficient memory, are sufficiently powerful
to maintain an OLAP server and client.
In cases where portability is necessary, we have utilized laptop computers to provide both the OLAP server
and Excel client functions.
This allows the user to take the databases "on the road" to meet with physicians, payors, or others.
This solution requires you to obtain and install Microsoft SQL Server 2000 on each computer to be used as a server.
Where can I find more information about OLAP and pivot tables?
See the DGA Partners OLAP and Data Analytics Resource page here
How does DGA Partners approach a data reporting project ?
DGA Partners structures data reporting engagements into two major phases.
In the first phase, we transfer, edit, and load data from its original source into an "analytical data warehouse",
located on our data servers.
The second phase is tailored to the needs of the client, and can involve
(a) the preparation of tabular reports,
(b) development of OLAP cubes that the client can use either on their own computers or through the Internet to
DGA Partners servers,
(c) the transfer of the analytical data warehouse to the client servers,
or (d) additional consulting services, including financial and clinical analysis of client data.
What types of standard reports do you produce ?
DGA Partners has a library of standard report formats that other clients have found useful.
However, it is important to realize that the ad hoc reporting capability of an OLAP-based reporting system allows
users to access any information that is available in the database.
By carefully structuring the dimensions and measures in an OLAP cube, we can allow users to access any possible data.
Thus, the concept of "standard reports" becomes less meaningful.
August 14th. 2004
Database Answers Ltd.