BI Blogs

Last week I downloaded and installed the Enterprise Edition BI suite from Oracle’s e-delivery website.

Installation of the components that I wanted to try was a breeze and getting the admin tool up a running and pointing at a real database was straight forward. I had decided to go with one of our development databases (around 1 TB in size) and with a snowflake design. I decided to use OC4J as a

Remember virtual warehouses? Is that what enterprise information integration is about?

Microsoft grew its share of the relational database market in 2005—even though its BI-laden SQL Server 2005 release didn’t ship until late last year.

Some experts worry that EII—which helps enable painless connectivity to disparate data sources—has the potential to be easily abused.

We take a look at Microsoft’s business intelligence aspirations and try to separate fact from FUD. First in a series.

The other week I went through a
walkthrough of Siebel Answers and Siebel
Intelligence Dashboards, and I mentioned at the time that it was possible to use
reports generated by these two tools as the basis for an alerting mechanism.
Within Siebel Analytics (and Oracle BI Suite Enterprise Edition) alerting is
provided via Siebel Delivers, and in this posting I’ll go through how you set it
up and put an alerting mechanism together.

First of all, assuming you’ve installed Siebel Analytics 7.8 and your license
key includes Siebel Delivers, you need to configure a database account to store
data that the Siebel Scheduler uses. This can be in an Oracle database, and
therefore I created an account called S_NQ_SCHED (the recommended account name)
and ran some provided scripts to create the necessary tables:

SQL> conn system/password@ora10g
Connected.
SQL> create user s_nq_sched
  2  identified by password
  3  default tablespace users
  4  temporary tablespace temp
  5  quota unlimited on users
  6  /

User created.

SQL> grant connect
  2  ,     create table
  3  ,     create view
  4  ,     create procedure
  5  to    s_nq_sched
  6  /

Grant succeeded.

SQL> conn s_nq_sched/password@ora10g
Connected.
SQL> @c:SiebelAnalyticsSchemaSAJobs.Oracle.sql

Table created.

...

Commit complete.

SQL> @c:SiebelAnalyticsSchemaSAACCT.Oracle.sql

Table created.

Index created.

Index created.

Index created.

My database, Siebel Analytics and Siebel Analytics Web installations are all
on the same (virtual) machine, by the way, running Windows XP SP2.

Next I need to set up a TNSNAMES entry to point to the user account I’ve just
set up, like this:

Now, I set up an ODBC System Data Source to point through to
this user, using the TNSNAMES.ORA entry I just created.

The final configuration step is to go into the Siebel Analytics
Administration tool, and choose Configuration Options from the Job Manager:

Using this screen, I entered the name of the data source, passed
across the username and password, and that was more or less it.

Now we’re at the point where we can put an alert together. The
next step then is to go into the Siebel Analytics Web client, and create an
exception report, in my case one that returns all those Warehouses that have
sold less than 20,000 units.

This report tells me which warehouses have sold less than 20,000
units; I’m now going to set up an alert that runs this report, and if any
warehouses fall below the 20,000 limit, have the scheduler send the report out
to anyone who subscribes to the alert.

The next step then is to click on the "Delivers" link at the top
of the page, and create what’s called an iBot. An iBot is a process that runs to
a schedule and performs a task, which can be as simple as running my alert, or
as complex as anything I can put together as a script. iBots are also the way
you pre-seed the cache - you run iBots that then run commonly accessed reports
after you’ve done a data load, and then the data your users will require will be
in the cache when they come to run their reports;

Once Delivers is running, it’s time to create the iBot. The
Delivers home page gives you a list of availalble iBots on the left-hand side,
and options to create new iBots, customise subscriptions and so on in the main
area of the page.

The obvious next step then is to create a new iBot, and so I
click on the first link. I’m then presented with the elements that make up an
iBot process.

For this simple example, I leave the priority as standard, and
the data visibility as personalised, so that the alert subscriber will only see
the data they’re scoped for, as opposed to the data the iBot creator can view.
The interesting bit is the "Conditional Request" section though, as this is
where we create the test condition for the alert.

The way you accomplish this is to select a Siebel Answers report
(or a "request" in Siebel Analytics jargon) which will be run when the iBot
executes. If the report returns one row or more, the iBot condition is satisfied
and the process runs. Therefore, like the report I picked earlier, you want one
that runs and has a filter that corresponds to the alert you want to create.

Once the request has been defined, the next step is to define
the iBot schedule, the recipients (the default is just you), the delivery
content, which is usually the request that you selected earlier, and the
destinations.

Then, after waiting for the iBot to start up, I check back on my
dashboard page, and yes, the alert is there, just under the tabs and also up on
the top right-hand side of the page, along with the other Siebel Analytics
application names:

When I click on the alert, I first get a page that lists out all
of my current alerts, and then when I click on one, I can see the figures that
triggered the iBot.

If I’d got a mail server set up, I could have delivered the
alert as an email, but for the time being, this will do. It wasn’t all that
difficult to set up (once you’ve gone through the scheduler configuration
process) and as long as you’ve got the reports on hand on which to base the
alerts, it doesn’t take all that long to get up and running.

Remember the ad for the Sony Bravia with superballs bouncing down the street in San Francisco? Re-imagine it with fruit. “Returning from church hall last Friday (17th) we witnessed the aftermath of a destructive TV commercial film crew. The damage (smas

The Oracle team has authored a popular Data Mining tutorial! It was the most popular software item in the latest KDnuggets News, a premier Knowledge Discovery in Databases (KDD) web site. See this link.

If you follow the link all the way it takes you to the older 10.1…

[ This is a content summary only. Visit my website for full links, other content, and more! ]

As Web services is becoming more popular and in fact the de-facto standard for exposing functionality over the Internet and corporate intranets, there are a multitude of applications that require real-time access to their business critical S/390 data through such an approach. While there are a host of Interactive Development Environments (IDEs) within the desktop environment, few companies have an efficient means to provide access to this important legacy data from their development environment. Select’s Business Intelligence products access data from multiple production or legacy systems and makes this business critical information available to key personnel for real-time decision making.

Vodafone New Zealand is the country’s leading mobile telecommunications carrier, providing second- and third-generation mobile services to more than 1.9 million New Zealanders. In view of Vodafone’s 54 percent market share, one of the company’s key challenges is to remain a market leader. To achieve this, it needs to understand its customers’ needs and wants in order to develop services that enable New Zealanders in their everyday life. In 2004, Vodafone recognized that its pace of growth had outstripped the ability of its server-based data-warehousing system to manage the level of activity. Vodafone and HP assessed the options and selected two HP Integrity servers, which were installed in June 2004.

GfK provides information services in five business segments: One of these, retail and technology, which is represented in Germany by GfK Marketing Services, specialises in non-food markets. GfK Marketing Services decided to accumulate all existing data into a central data warehouse. GfK Marketing Services first checked if there was an application on the market which provides such sophisticated linking and analysis options as required by a central data warehouse. As the result of this market analysis was negative the application development department of GfK Marketing Services received the order to develop a system of their own. GfK uses two HP Integrity servers as database servers, which cover various areas of the production system.

Motorola University is an integral part of Motorola, Inc., a company that evolved from a manufacturer of automobile radios in the 1930’s to become a $28 billion technology leader with a significant presence on six continents. Launched in 1985 to help track training hours, the Motorola Training Information System (MTIS) is written in NOMAD, and is critical to the firm’s employee development program. In January 1996 the Business Systems Development and Support for Motorola University and a consultant with Select’s Professional Services Division, began investigating the feasibility of web-enabling the existing MTIS system. In less than five months, the consultant had implemented a web-enabled version of MTIS using software he had developed, later to be dubbed, “UltraQuest”.

The Client is a world leading pharmaceutical company. The Product Management Group was interested in conducting research on the effects of different combinations of drugs. This required them to extract information around popular drug prescription patterns, and the effects of drug combinations on patients over different periods of time. In order to successfully meet the above business goals, a data warehouse had to be created. A master data warehouse was designed to support the analytical needs of the Market Research and Product Management groups.

A recent quite non factual discussion at slashdot.org exaggerates what has been a problem since the invention of IT.
ZombieLine asks: “The IT department at my company (approximately some 500 people) is showing signs of incompetence, and has been ignoring knowledgeable user input for about a year. Additionally, they haven’t been able to sell needed changes […]

Busiest graph I’ve seen, depicting the history of major social and political trends over the past 250 years and projecting into the future. Interesting to see them overlaid at a high level like this.

Apart from a

talent for producing criminal geniuses, the UK seems to be good at producing
data warehousing experts, and Kalido’s Andy Hayler is one of my favourites. His
blog is always a good read, and I particularly enjoyed a couple of his recent
posts, on

data warehouse architectures, and the

business payoff of a data warehouse project.

The data warehouse architectures posting is particularly well timed as it
looks at the inevitability of federated data warehouse architectures, something
that’s now possible with an all-Oracle setup when you use the new Siebel
Analytics-derived BI Suite. Up until now, the mantra from Redwood Shores has
been to build a single, consolidated data warehouse for the entire enterprise,
but in Andy’s opinion, this is never going to happen due to the sheer scale of
the undertaking, and in his opinion:

"… for any large corporation it seems to me that a federated
warehouse approach is what you will end up with, whether you like it or not.
Few companies will have the energy or resources to deliver the single giant
warehouse, and even those few that do will, in reality, have a series of
skunk works data marts/warehouses dotted around the corporation since such a
behemoth warehouse will be a bottleneck, hard to change and inevitably slow
to respond to rapidly changing business needs.

The most pragmatic approach would seem to me to acknowledge this reality and
architect for a federated approach, rather than staying in denial. It is
practical to build a warehouse for either a country-level subsidiary (or
groups of countries) or each business line, let that deal with the needs of
that particular country or business line, and then link these together to a
global warehouse which deals at the summary level. The global warehouse does
not need to store every transaction in the enterprise; at that level you
need to know what the sales were in Germany yesterday by product, channel
and perhaps customer, but not that a particular customer bought a specific
item at 14:25 at a store in Rhine-Westphalia. The detailed information like
this is the domain of the country-level warehouse. Because the transaction
detail is not needed at the enterprise level, you avoid the problems of
technical scale that may otherwise occur, and only deal with the data that
makes sense to look at across the enterprise as a whole."

The second posting, entitled "Putting a little glitz into data warehousing",
looks at how data warehouses are rarely the first thing you think about building
after an acquisition, and yet they can be a perfect way to start delivering
synergistic cost savings fairly soon after the merger.

"For example, when HBOS merged, one of the key areas for quick savings
was identified as procurement. Yet to just pick one of the existing
procurement systems, switch off the other and convert all the data from one
to the other was estimated at taking well over a year. Instead, what they
did was to implement a packaged data warehouse, map the two sets of data
from each bank, and in this way get a single view of the procurement spend
across torganizationion without having to convert all the data in the
underlying systems. This was achieved in just three months, giving an
immediate view of post-merger procurement that allowed huge business savings
to achieved. For more on this award-winning project click

here."

A pretty interesting couple of postings, and there’s another one

here about explaining Master Data Management to a business audience. Andy
Hayler’s got a wise, experienced perspective on data warehousing and you’d do
well to bookmark his blog if you’re interested in the subject.

Well, an interesting question. I just gave a presentation at Informatica World 2006 on Data Administration, the roles, responsibilities, and possibilities that the DA must undertake and manage. If you’re in a DA role today, I’d be very curious to hear some of the types of “dirty jobs” that you’ve had to do in this role. Coming from a government background, the DA I had on the team had an interesting time in life. In this entry I’ll try to describe my impressions of the DA role, and how it’s expanded today - and what they should be thinking about.

A while back I blogged about appliances, and where I thought the market is headed. Please bear in mind that I frequently like to place myself into the future and attempt to see what will happen overall. Also bear in mind that frequently my definitions are slightly different than the common industry. As it so happens, I had the opportunity to look at and research Appliances going forward. I’d like to draw attention to the appliances in the market space and try in a couple entries or so, to help define the terms more clearly, and level the expectations of what customers may be seeing out there.