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Last week saw the release of the latest version of CALUMO for SQL Server Analysis
Services, CALUMO 11.4.  This release has rolled up all the features of the CALUMO
11.3 cumulative updates as well as delivering a set of new features and performance
improvements.

Highlights

• Text Writeback architecture update
• Publishable Drop Lists and Date Pickers
• Excel VBA API
• New Installers
• Windows 7 and Windows Server 2008 R2 Compatible
  

k Architecture Update

This feature is a key differentiator within CALUMO 11 and in our recent work we took
a good look at the existing architecture and reworked it for performance and scalability.

Publishable Drop Lists and Date Picker

Our standard CALUMO drop lists have been enhanced to also support data write back.
Now drop lists can be used to enter data from a standard selection list.

Fig 1: CALUMO Drop Lists

The date picker simplifies selection of dates. For example, when budgeting the start
date of a new employee using the date picker, a date is written back. Budgets salary
and on-costs will calculate and budget from this date.

Fig 2: CALUMO Date Picker

Excel VBA API

These API allow automation and control of certain features of CALUMO from VBA macros
within Excel.

New Installers

CALUMO installation is already a very easy process. For example, users can install
or upgrade the CALUMO Excel client with a one button click from the status bar, thereby
synchronising their version with the CALUMO Server. Adding to this, our new CALUMO
installers are now smaller, faster and more reliable and can be automatically built
at any time for any revision of the CALUMO codebase.  This enables us to deliver
patches to the software as robust installers instead of as manually applied patches.

Other Features worth Noting

• Administrator can set a default start page a default view for a Cube.
• Administrator and Users can set default subsets for hierarchies.
• Sort and top/bottom count filters available in cube browse.
• We include a web based calculation engine supporting most Excel functions in CALUMO
  published web pages.
• Conditional formatted cells in CALUMO published web pages
  

CALUMO for TM1 10.3, the latest version of CALUMO for TM1 has been released. 
This release delivers deeper integration with IBM Cognos TM1 and includes many new
features and performance improvements.

Highlights

• Support for TM1 9.4.
• The CALUMO Excel 2007 Ribbon.
• CALUMO Sparks now bundled free with CALUMO for TM1.
• Cool UI updates like (e.g. page loading indicator for published reports making it
  easy to see a large report is actually loading).
• Many other enhancements, fixes and even better performance.
  

M1 9.4
CALUMO for TM1 10.3 supports TM1 9.4 and all other active versions still
supported by IBM. We note that you should install TM1 9.4 FP1 as IBM have identified
and resolved several critical TM1 issues in both the standard TM1 9.4 release as well
as the TM1 MR1 release.

CALUMO Excel 2007 Ribbon

Love it or hate it (and we love it), the “Ribbon User Interface” is one of the most
significant changes in Office 2007 which replaced the old menus and toolbars. The
CALUMO Ribbon is a task-orientated graphical user interface, making it easily to navigate
to the CALUMO capability you need.

CALUMO Sparks

Now part of CALUMO for TM1, CALUMO Sparks provides rich compact visualisations of
large amounts of data in a single cell. By combining charts and data together in a
table, we do away with the need to separately label axes and legends, or the need
to associate the table data with the chart information.

For identifying trends, comparing data and highlighting exceptions (with high density
information), CALUMO SPARKS makes the design of really compelling executive dashboards
easy.

CALUMO SPARKS provides Line, Pie, Bar, Bullet, Grid, Progress, Stripe, Trend, KPI,
Bubble, Heat Map & Harvey Ball charts. CALUMO SPARKS is an add-in for Microsoft
Excel.

Other Features worth Remembering

• Freeze Panes for Web Reports.
• Context Linking in Web Reports.
• SPF Discovery (Decomposition Tree) in Web Reports.
• Save Web Reports to PDF.
• Drill Through to Transaction in Web Reports.
• Dynamic Rows in Web Reports.
• Zero Suppress in Web Reports.
• Charts can now be published as either an image or ActiveX control.

Recent research conducted by the Economist Intelligence Unit on behalf of SAS illustrates both the scope of the proposed reforms and the scale of the challenge ahead in risk management. In March 2009, the Economist Intelligence Unit conducted a global survey of 334 senior financial services professionals, of whom 50 percent were C-level and all have responsibility for risk. The unit than carried out a programme of interviews with high-profile commentators including Alan Greenspan, former chairman of the Federal Reserve; Nassim Taleb, author of The Black Swan; and Peter Bernstein, founder of Peter L Bernstein Inc.

The new report, After the Storm: A new era for risk management in financial services, written by Phil Davis brings these two strands of research together.

When we started discussing the research process in January our main concern was to pluck subjects that would stay in the public domain from the middle of 2009 and beyond. As the popular media became risk experts overnight, this was going to be a challenge. We wanted especially to have Phil Davis interview key players in the financial market who would offer insight on the future direction of risk management, no mean feat in a very uncertain and fluid economic situation.

So at the start, the challenge was not so much operating with a crystal ball on the financial world, rather wondering whether the ball would be in play at all. In the end the survey gave us an excellent understanding of where financial services companies see the present situation and a view of the future.

With the global political focus remaining on re-establishing the creditability and integrity of financial services (at a local and global level), one of the most intriguing elements to come out of the survey was the lack of confidence the financial services community had in those (rating and regulatory) agencies tasked with benchmarking and challenging their approach to risk management. But many risk analysts did not have the culture to challenge their firms’ management to act on any blind spots in their approach to risk management that the risk team had identified. Alan Greenspan did make the point when interviewed by Phil:

The important lesson is that bank regulators cannot fully or accurately forecast whether, for example, sub-prime mortgages will turn toxic, or a particular tranche of a collateralized debt obligation will default, or even if the financial system will seize up. A large fraction of such difficult forecasts will invariably be proved wrong.

So we expect to see, in the near future, the recommendations that will take us in to a new era of global financial services, certainly around areas of (systemic, liquidity, stress testing, firm wide, Credit, Market and Operational) risk and capital management, regulated by the various governments and their agencies. Business confidence will be restored, slowly, but will lessons be learned and practical, appropriate measures implemented, at country, regional and global level?
Continue reading “Reality check: thoughts based on the recent risk management survey”

Data
integration suffers from an image problem. It has become synonymous with
extract, transform and load (ETL). Likewise, ETL has been regarded as a data
warehousing (DW) technology.  Both of
these viewpoints do not reflect current capabilities and greatly inhibit
enterprises in their attempt to integrate data to provide the information their
business needs.

Because of
this short-sightedness companies have lost opportunities to harness information
as a corporate asset. It increases the cost of integrating data, encourages the
creation of data silos and forces business people to spend an inordinate amount
of time filling the information gaps themselves through data shadow
systems or reconciling data.

>>> Continue to the rest of my article, Data Integration: Beyond ETL and Data Warehousing, on the Information Management website.

Back in February when I heard about Lyza, I thought right away of Tableau. Despite each one’s different strengths in data discovery and analysis, each appeals to the same broad group.

It’s an old group that’s getting new attention: creative analysts, or “cowboy analysts” to some. The like their data raw, not aggregated. They ask questions, forage, synthesize, analyze, and publish.

Joe Mako is one of them. Tomorrow, he’s launching a website for people like himself who use both Tableau and Lyza. Makometrics will publish every Monday morning and sometimes more often.

Joe is a network engineer at a Midwest ISP. He started at the tech support desk, where he saw how much help people needed looking at their data. “They didn’t understand exploring data,” he says. “They just don’t care.” But Joe cared enough to help with data analysis, and pretty soon someone gave him a tag line: “Make it happen with Mako.”

Posts he’s lined up so far:

• He’ll walk through data analysis problems from challenge to resolution. “I’ll be practicing something akin to the cycle of visual analysis.” (See the Tableau video “The Zen of Visual Analysis.”)
• Analysis of strengths and weaknesses of Tableau and Lyza
• Analysis of his “Visualizing Rambo Kills”: how he approached the dataset, and how he created the final result.
• Demonstrate sophisticated techniques in Lyza and Tableau. He’ll go into detail on such things as combining Lyza’s “previous” and “if” functions and the basics of summary functions like “sumcolumn” and “avgcolumn.”

Check it tomorrow (Wednesday, July 1): makometrics.com.

I have obtained all three examples from Paul Kedrosky’s blog, Infectious Greed.http://rebis.reidin.com/home.htmlA fake 3D bar chart. And a gradient effect. Two egregious errors in one chart.http://paul.kedrosky.com/archives/2009/06/crude_oil_price_1.htmlAn otherwise useful chart that is marred by the use of distracting gridlines, that overpower the data plotted. The obtrusive gridlines are

In all of the Gemini demos seen so far, a big thing is made of the fact that it can be used to integrate data from outside sources (eg some stuff you found on the web) with internal data (eg data from your data warehouse). This is all very well, but it assumes you have some way of actually capturing the data that’s out there on the web in a usable form and automating the capture of new data from these sources on a regular basis.

For example, if you’ve found a table containing some data you want on a website, what do you need to do to actually use it? You’d need to copy the table and then paste it into Excel perhaps; you’d then need to do some reformatting and editing, copy it again and then paste it to wherever you need it such as Gemini. All very manual; in fact, a right pain and not something you’d want to do on a regular basis. There might be an RSS or an Atom feed, and you might be able to use a tool like Yahoo Pipes (see also Jamie Thomson’s recent post), but there isn’t always one and even when there is it might not contain all the data you need or be in the right format.

Last week I had a call with Kapow Technologies, and they’ve got a really cool tool that addresses this problem:
http://kapowtech.com/index.php/solutions/web-and-business-intelligence
It’s something like a cross between a screenscraper and an ETL tool, and it allows you to harvest data from web pages, do some transformation and cleansing, and then move it somewhere else like a database table (where more traditional ETL tools can take over) or another application. There’s a bit of background on this type of technology here:
http://en.wikipedia.org/wiki/Web_scraping

I got hold of an eval version and had a play with it, and while I’m hardly the right person to give an expert review it does seem very powerful. It’s certainly easy to use: I had a simple scenario working within twenty minutes, and I think anyone with some SSIS experience will find it fairly straightforward. First of all you have to declare a set of objects (which can then be mapped onto relational tables) that will hold the data you want to collect; you then create a robot that will harvest data from web pages and load it into the objects; finally you can run the robot either from the command line or on a schedule. More information can be found in the help:
http://help.kapowtech.com/7.0/index.jsp

In my example, I took a web page from the BBC website that shows the weather where I live:
http://news.bbc.co.uk/weather/forecast/2302?&search=amersham&itemsPerPage=10&region=uk&area=Amersham

I wanted to harvest the time, the basic outlook and the temperature from the first box in the “first 24 hours” section:

Here’s a screenshot of what that page looks like in the RoboMaker tool:

You can see that I’ve selected the tag containing the time in the central browser window, and that it will be mapped to the attribute weather.forecastTime.

I think the thing that I find really exciting about this tool is that, when you think about it, there is a whole load of useful data out there on the web that would be great to use as part of your BI if you can extract it quickly and easily - and indeed legally, because I suspect there might be some legal objections to doing this on a large scale. One of the examples Kapow gave me was of a customer that harvested comments on their products from places like Amazon, then fed that data through a text mining application, to monitor public opinion on their products (today’s Dilbert shows how this information could be used!); I would have thought that this is something a lot of companies would like to do. The weather forecasts I was looking at could also be useful for predicting retail sales in the short term; if you’re an online retailer you’d probably want to compare your prices for certain products with those of your competitors. I’m sure we’ll be seeing much more use of web data in BI, captured using tools like this, in the future…

The Business Intelligence (BI) index continues to outpace the broader market indexes with YTD gains of 28.4%. The attached chart (click on the image to see a larger version) compares these indexes with broader market indexes along with a select set of high tech ETFs. High tech, as we has discussed, is currently the industry leader for the year and is likely to provide leadership with an upward market.

Three companies in the BI/DW reported results last week (see below). Although none of these companies reported great news, they generally beat, albeit reduced, expectations. All mentioned constrained IT spending and currency headwinds negatively impacted their quarterly results. IT budgets appear to be stabilizing, but we are in the summer quarter, which is typically weak (vacation time often slowing sales cycle and project start dates).

It would appear a rebound in IT spending would not happen before Q4 2009 and more likely in 2010, at which point vendors’ performance will also look better due to comparisons with weak year-over-year quarterly comparisons. A quick recap of quarterly earnings:

Progress Software (PRGS) reported, on a GAAP (generally accepted accounting principles) basis, a 9% drop in sales (up 1% on a constant currency) year-over-year led by a 14% decline (-7% in constant currency) in new software license revenue. Software license revenue is a closely watched metric because it is an indicator of future revenues such as upgrades, maintenance and services. GAAP operating income decreased 44%, however, excluding restructuring and other costs, the company earnings beat the average forecast from analysts in a Thomson Reuters poll. Progress Software’s CEO stated that “Our results reflect the difficult economic environment and unfavorable year-over-year currency rates.”

Tibco Software (TIBX) reported revenue declining 5% (-1% on a constant currency basis) over the prior-year quarter with a 13% drop in license revenue (-5% on a constant currency basis.) With significant expense reductions, Tibco’s operating margins increased to 9.8% from 2.7% and its second-quarter profit nearly tripled.

Oracle (ORCL) beat Wall Street expectations for sales and profits when announcing their recent quarterly results. Sales declined 5% and profits fell 7% (on a constant currency basis sales increased 4% and profit was up 5%). New software licenses sales were down 13% (but -4% on a constant currency basis). Its margins, like the others reporting, increased because of expense management but also because of a shift to a higher percentage of revenue coming from maintenance contracts. These annual fees accounted for 44% of Oracle’s revenue and boasted its operating margins to 51%.

fyi: The business intelligence (BI) index is calculated on an equal-weight representation based on closing prices as of 12/31/07.

Disclosure: The analyst or his family does have current stock positions in the following companies listed in this index: INFA, NZ, ORCL, TDC, TIBX, SAP

Another excellent feature that was introduced as part of the EPM 11 release was the support for varying attributes. Varying attributes help in providing different perspectives of multiple attributes of a dimension over time. One can visualize this as SCD 2 in a relational world. For example, lets take the Sample –> Basic cube and look at the various attributes that have been defined.

As you see, this cube has 5 different attributes defined on Product and Market dimensions. As of 9.3.1 release of Essbase one can have only static attributes defined. If varying attributes had to be defined, they had to be modeled as separate individual dimensions. Static attributes are those attributes that remain constant for a specific member. For example, lets assume that a product X has a static weight of Y ounces when it was introduced. If the manufacturing company decides to repackage the product X with a weight of Z ounces, then as of 9.3.1 release all the sales data mapped to Y ounces would switch over to Z ounces(similar to SCD-1 in a relational DW world). But in most cases, we want to see the sales data corresponding to their weights so that one can analyze the sales drop or increase due to the new product packaging. This is called as varying attributes over time. Sometimes, the same product X can be packaged with Y and Z ounces depending on Market. In this case, the weight varies over Time as well as Market.

With the advent of EPM 11.1.1.0, one can enable tracking of metrics over varying attributes as well. In the above outline, lets take the example of Pkg Type attribute. This has 2 values

1. Bottle
2. Can

This packaging can vary for products across multiple Markets. Also the packaging can vary for a product in a specific market over time. To enable varying attributes, one would have to first enable this feature while creating an outline or later.

In order enable the attribute Pkg Type on the product dimension, one would have to choose the attribute and the set of independent dimensions. Independent dimensions are those dimensions over which the attribute varies like Market and Time.

Continuous dimensions(in the screenshot above) are typically those dimensions like Time where there is a chronological order and where we can specify a range. After this has been enabled, while setting the attribute for each product, the Market and Time would have to be chosen as well. For continuous attributes, one can specify a range.

For each product set the Time range over which it is planned to be sold and also the market. In the above example, the product 100-10 is sold in New York Market from Jan till Dec in Bottle Type and in the Massachusetts Market from Jan to Dec in Can type. The same kind of association would have to be done for each and every Market for the corresponding Pkg Type.

The above basically provides a relational visualization in a multi-dimensional cube. This also provides analysis of data in multiple perspectives. For example, we might be needing a report wherein we would like to analyze the sales for the list of products that were sold as Bottles in New York from Jan-Dec with a perspective of the attribute setting that we had in July. These kind of queries can be answered pretty easily by Varying attributes. In a future blog entry we shall see how these varying attributes can be leveraged from BI EE. As of the current release in BI EE, one can only use the default query context/perspective. One cannot alter the perspectives due to the limitation of modifying certain parts of the MDX query in BI EE. Having said that, it is possible to leverage the varying attributes using the default perspective. I will cover this in the coming weeks.

We are announcing the availability of an Enterprise-Ready MapReduce Data Warehouse Appliance.

The appliance is powered by Dell hardware and Aster’s nCluster SQL/ MR database, with optional software for BI platform from Microstrategy and data modeling software from Aqua Data Studio.

Our product portfolio now allows our customers to get the benefits of our flagship Aster nCluster SQL/MR database in the packaging that they are most comfortable with - on-premise software, in-cloud service, or pre-packaged appliance.

The appliance offering packs a lot of punch compared to other data warehousing appliances in the market - it has the highest ratio of compute & memory to data sizes, allowing you to run rich queries on the appliance without breaking a sweat.

We are especially proud of the open nature of our appliance - the hardware is from Dell built from industry-standard components, the BI server is from Microstrategy, and the data modeling tool is from AquaFold (Aqua Data Studio). The appliance brings together industry-leading components of a full data warehouse stack together - all pre-tested and configured for optimal performance.

Even the programming of our appliance is open - our SQL/MR framework allows applications to push computation into the appliance using industry standard SQL augmented with MapReduce in the language of your choice (Java, C#, Perl, Python, etc.).

We have been approached by a number of customers seeking a get-started-quickly system, especially those groups of users and departments seeking a Hadoop framework to build their solutions upon.

In response to the requests, we are proud to announce an Express Edition of the appliance that is designed to work for upto 1TB of user data. And it comes in an even more attractive price - that of $50K only - complete with hardware and software!

Give us a call - we’ll get your warehouse setup on our appliance to ensure that the time-to-first-query is measured in hours, not months!

I’ve read and heard many stories in the past several months about credit card companies that are reacting to the economic downturn by reducing credit limits and hiking interest rates without first warning the cardholders. This latest article in Bloomberg Markets gives a good explanation of the institutions’ reasoning. The news alarms me as a prospective card holder, and it saddens me because I believe that financial institutions should be analyzing and using their customer data to enhance the trusted advisor relationship.

The trusted advisor relationship that I’m referring to is a combination of customer relationship management and risk management. In some ways, it’s a return to sound lending practices from the past. These practices are enabled by deep analytic insights into a customer’s behaviors and state of mind. The key point is to leverage all of the customer information you have to win the customer’s loyalty and additional business by truly understanding critical events within the customer life cycle. This deeper relationship with enhanced insights goes beyond a simple view of a change in the FICO score and is a much better way of enhancing profitability for the financial institution. If done correctly, analytics and data mining can help target customers with the right offers to gain more business.

We have seen numerous situations where our clients have used data mining and predictive modeling techniques to see a much greater lift in response to marketing offers while reducing marketing spending. Additionally by combining proven marketing techniques with proven credit scoring and credit risk management techniques, banks can balance customer relationship management with credit relationship management to target the right offer with the appropriate credit line, payment cycle and repayment schedule. Increased ROI is achieved by targeting the message to the individual, and the cost savings results from eliminating the blanket offers to the entire customer base.

So what is causing all the fuss?
Continue reading “Stop! Business analytics can ruin your company’s reputation!”

So as you probably know SPM 2.x requires the use of MySql as the backend database to store all the SPM structure and data.

By default MySql creates a single file on the server for the database.  Not bad if you are on Unix but if you are on windows (and with SPM you are probably on Windows) then when SPM gets large the single database file gets large.

Having a single file on windows  that is over 2GB is a bad idea (in my opinion anyway and especially if you are on 32 bit windows and of course i you are on SPM 2.x you are on 32 bit windows)

One of the other challenges with MySQL is unlike some other databases it just seems to grow and grow and never reclaims any of its internal space.  In fact the only way to reclaim space ans shrink the database file is to export the entire database, delete the database, recreate it and reimport the exported file.  Time consuming and fraught with risk.

However there is an answer…. (well a suggestion anyway)

You can tell MySql to create a number of smaller files to effectively partition the database files.  How I hear you ask…..

Well in the my.ini for the database added these lines:

innodb_data_home_dir=drive:/dir1/dir2/dir3/ibdata

innodb_data_file_path=ibdata1:2000M;ibdata2:2000M;ibdata3:2000M;ibdata4:2000M;ibdata5:2000M;ibdata6:2000M;ibdata7:2000M;ibdata8:2000M;ibdata9:2000M;ibdata10:2000M;ibdata11:2000M;ibdata12:2000M;ibdata13:2000M;ibdata14:2000M;ibdata15:2000M;ibdata16:2000M:autoextend

innodb_log_group_home_dir=drive:/dir1/dir2/dir3/iblogs

So the first line says where tostore the db files.

The second line says to create 16 2GB files and then automatically extend the last one to infinity (and beyond if the MySql database needs to go over 32 GB.

One downside is that MySql will create all the files when it starts up, so you will suddenyl have consumed 32GB of disk.  You might want to tune the number of files based on your environment.

Lastly before you do this you will need to backup your MySql instance, stop the Mysql service, remove the old MySql DB files  and then restart MySql, which will recreate the DB files with the new partitonedversons automagically.

Then reimport the backup and wahoo you are away (plus ypu have just regorg’d the db and reclaim any lost space as well).

And  of course you will ask, yes we asked SAS Tech Support and yes they ‘indicated’ this was not unsupported whichmeans it is supported.

Partition away!

Here is the presentation I gave on the topic of the Parent Child Dimension at the BI User Group meeting. I tried to summarize most of what I could find on the subject and I hope it helped you learn something new about how this sort of design can aid you in your work.
You can download the presentation here: The Parent Child Dimension PDF

For more details on future meetings and presentations that were already given, please check Ronen Chenn's blog, Yossi Elkayam and Eran Sagi's blog or the Israeli SQL Server site.
It was a pleasure seeing you all and I hope to see you again.

I was writing about the difference between operational systems like ERP and informational systems like BI. One of the most obvious differences is that ERP systems support operational or business processes. The information that is captured is very explicit. It is almost binary. Data latency is minimal.BI systems are often supporting decision making processes on a more strategic level. There is no yes or no. Often it is about maybe or what if. The information is much more implicit. Data latency is often a problem. Looking at all this made me think.BI has always been more tactical or strategic. But it has also never been the big success that we BI people claim it can have. Is the reason that we have been looking in the wrong direction. Maybe Operational BI has much more value? What do you think?

Well we’re back now from two weeks in the States, the first week of which was vacation in San Francisco and Yosemite, the last was at ODTUG Kaleidoscope 2009 in Monterey. Well done to the ODTUG team and to the Oracle ACE program for an exceptionally-well organized conference, as I’ve said in the past ODTUG Kaleidoscope is big enough to draw in the best speakers but small enough to get to meet everyone, and Mike, Kathleen, Crystal and the rest of the team make us all feel very welcome. Thanks again for the invite over.

As I had the family with me I spent a bit less time at actual sessions this time, but still made it to the key events. Tim Tow and Edward Roske did an excellent job of organizing the Hyperion stream and gave me a few ideas for the BI stream for next year; on reflection, although we had some good BI speakers this year the BI stream itself was a bit disjointed and didn’t really have the coherence that the Hyperion stream did. Next year our plan is to take a much more proactive approach to the BI content and try and organize it in a similar way to the Hyperion sessions, with streams dedicated to ETL + DW, OBIEE and so on. If anyone reading this is likely to go next year and wants to help shape next year’s agenda, drop me a line and we’ll make a start soon.

Rittman Mead had a great turnout at the conference, with our names on the lanyards and Stewart Bryson kicking off the sessions with a deep look into the functonality of Oracle Warehouse Builder. Stewarts’ presentation is on our Articles page and here’s a quick video clip of him presenting:

In case you’re wondering, that’s Joe Leva (our US MD) and Lewis Cunningham (Oracle ACE Director) that I pan to towards the the middle of the video.

My two sessions were on Optimizing Oracle BI Enterprise Edition, and a joint paper I wrote with Venkat on Creating Hybrid Relational/Multi-Dimensional Data Models using OBIEE and Essbase. Both papers are on are articles page, including a white paper that accompanied the Essbase paper that goes into a number of different integration use-cases. Now that Venkat has joined us you can expect him and I to be authoring a bunch of other papers together, and to start things off we’ve had an updated version of the Essbase and OBIEE paper accepted for Open World 2009 in San Francisco.

Unfortunately as Venkat has only just left Oracle and joined us he couldn’t make it in person to Monterey, however I had a good stand-in as my six year-old son Scott was able to introduce me instead … we’d talked about this the evening before and he was up for doing a short introduction, and here he is introducing me at the start of the talk:

Well done to Scott! I’ll have him doing the demos next time…

Well for now it’s a quick turnaround and repack, as I’m off to Istanbul tomorrow to deliver a two-day Oracle 11g Data Warehousing seminar for Oracle University. After that it’s back up to London for a couple of days of client work, and starting to write the presentations and papers for Open World 2009.

Oracle has something new. It is called EPM TV.  See: http://www.epmtv.eu/Home. It is all about Enterprise Performance Management. Somebody on twitter said: “Cool, Frank Buytendijk now has his own TV channel”.It made me think. If Frank has his own TV channel, I want to have my own movie. So therefore I proudly present to you: BI the Movie. Now on any computer near you.Just follow this link: http://www.xtranormal.com/watch?e=20090628071326228

As part of one of our projects we have been rolling out the SAS Strategic Performance Management suite to a large number of users.

Our goal was to deliver scorecards to individual employees to assist with the performance management process.

Its been an interesting roll out so know I have some time I will post some of our experiences over the next few weeks so anybody else that is contemplating a similar rollout can leverage what we learnt.

Out of interest  if there are any readers out there that are using SPM let me know.

Reading TIME magazine on the plane yesterday, I came across the article How to cut healthcare costs and recognized right away the Dartmouth data it cites heavily throughout the piece.

For example, chronically ill patients in Los Angeles visited doctors an average of 59.2 times in the last six months of their life, vs. only 14.5 times in Ogden, Utah; they still ended up just as dead. Medicare now pays three times as much per enrollee in Miami as in Honolulu, and costs are growing twice as fast in Dallas as in San Diego. Patients in higher-spending regions get more tests, more procedures, more referrals to specialists and more time in the hospital and ICU, but the Dartmouth research has found that if anything, their outcomes are slightly worse.

Why was this research familiar to me? Because Dartmouth is a SAS customer, of course. And I remember interviewing the researchers about their use of SAS for this success story: Dartmouth College researchers report healthcare disparities.

I wrote the original success story at least six years ago, and they were doing the research at Dartmouth long before that with the same long-standing results:

What Dartmouth researchers found is the hospitals with higher usage rates have more specialists and beds, but they don’t have better outcomes – or better patient satisfaction – than those hospitals with much lower utilization.

And:

The project’s researchers don’t just look at usage, but also at the quality of care, showing over and over again that quantity doesn’t produce quality.

For more on the subject of healthcare reform, be sure to read Jason Burke at A Shot in the Arm.