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If so, check out our public baseball project.



If you want to create your own report based on baseball data since 1871, click the link at the bottom of the report.

And don't forget to invite your baseball friends!

Ted Mosby, architect

Every time I introduce myself, I have to explain: "Petr Olmer, evangelist." "Oh, what? I mean, what do you do?"

Well, I try to prove that many of us can profit by using business intelligence in a light, agile, Web 2.0 way. Now I have a better sentence: I look for yes.

Seth Godin wrote:

If you're out to provide a service, or organized to deliver a product, then look for a yes. At every interaction.

That's what I do. If you want to analyze your data and you're not sure how, if you think it's difficult or even impossible, I'm here to find a solution. Petr Olmer, looking for yes.

Register for Good Data beta version!

Last weeks were hectic. You have probably noticed that Good Data secured the initial funding totaling $2 million (read more about it at BusinessWire). We have also released so-called private beta version of our business intelligence tool. The feedback was pretty good, so we have switched to the public beta today - anybody can request an invitation at our registration page. You should receive your invitation within several days, or several hours, if you're lucky.

What can you expect from our beta? We have prepared a warehouse you could be familiar with (it was used in our first video) - Foodz.com is a fictitious retailer (check out the schema). We have prepared sample reports and you can try how easy it is to create one yourself. (Read more about our beta.)

Release early, release often

We want to release often. I'm very happy that our users can cooperate with us on Good Data improvements at our GetSatisfaction forums. Please share your ideas, report problems, or just write us what's your feeling about Good Data.

Good Data team is developing according to the Scrum model, and we started another sprint yesterday, so new features are implemented almost every day.

On a personal note, I like musicals. Julie Andrews sings the following lyrics in The Sound of Music:

Let them bring on all their problems
I'll do better than my best
I have confidence they'll put me to the test
But I'll make them see I have confidence in me

I can tell you we'll definitely do better than our best.

False reasons why enterprises aren't interested in SaaS

Image via WikipediaAccording to a Forrester Research survey (source: CIO.com), these are the top reasons to say no to SaaS:

1. Integration issues.
2. Total cost of ownership concerns.
3. Lack of customization.
4. Security concerns.

I an convinced these reasons are wrong. APIs and microformats speak for better integration. In my experience, TCO is one of the main reasons why companies want to try Good Data platform. Customization depends on application, no matter whether it's SaaS or not. And security? Your data are more secure in a cloud out there than in your own house.

Mike West, VicePresident and Senior Strategy Consultant with Saugatuck Technology, comments the survey:

I hope everyone realizes that Forrester polled only IT managers. [...] SaaS is really primarily a business solution that disintermediates the IT department, shifting workloads to the cloud. If IT is concerned about SaaS -- and Cloud Computing, as well -- it may be because more SaaS means smaller, more management-oriented IT. Defending the IT department's technical turf by resisting the considerable business benefits of SaaS is a disfunctional (but completely understandable) response to this burgeoning phenomenon.

He's very right. And the four reasons aren't reasons why companies are not interested in SaaS. They are the reasons why IT managers are scared of SaaS, scared of change.

Related articles

• Enterprise 2.0 To Become a $4.6 Billion Industry By 2013 [via Zemanta]
• HP sells cloud vision amidst economic downpour [via Zemanta]
• 2008: The Year Web 2.0 Hits the Enterprise, Says Forrester [via Zemanta]

New twist on data analysis

BI failures tend to be at least as spectacular as the successes. Some companies spent the GNP of several small countries a few years back producing "decision support" systems and data warehouses that never matched up with rapidly changing user requirements.

Things have changed. A multitude of products and tools to build BI applications are available today, and their cost is plunging. The whole BI and online analytical processing market changed irrevocably.

BI is one of the fastest growing segments of the software business. The fact that BI vendors are flourishig, despite the distractions and budget drains caused by Y2K preparations -

Stop! Y2K? Well I have something to admit. The previous paragraphs were cited from the Enterprise Development magazine, September 1999.

A lot of interesting stuff is written there: Newest releases appeal to a diverse user base and feature an adaptable architecture for deploying BI solutions. Decision support for the masses, finally. OLAP goes on the Web. Put the spreadsheet out to pasture.

Wayne Eckerson, director of research and services with TDWI, noted that for the past 10 years BI has targeted the technological-savvy employee—the super-user. He pointed to TDWI data showing that a mere 24 percent of users actually access BI tools. "It’s a huge problem [underscoring] why BI is not invasive," Eckerson told the crowd.

However, a common theme projected throughout Information Builders keynotes and sessions is the idea that BI is no longer just a back-office tool.

Lauren McKay: Business Intelligence Comes Out of the Back Office

These words were written today, and you can find the same thoughts in the 9-years-old magazine. The magazine does not exist anymore, Y2K is over, and the BI issues, mmm still the same "new twist", n'est-ce pas?

Don't improve things you're not asked to

Image by 96dpi via FlickrRecently I worked as a BI consultant for a big bank. I was responsible for their metadata warehouse solution.

We would like to integrate our data dictionary with your solution, they told me.

Data dictionary? I asked and they explained.

Oh I see, I replied, you're talking about business nomenclature. And I explained what business nomenclature means in terms of metadata warehouse, the CWM standard etc.

Then there was a presentation where I explained everything once more. I believed my presentation was quite good but I missed a point. I was strict in using "business nomenclature" because hey, I was right, wasn't I?

I understand you but where is our data dictionary? That was the first question.

So don't try to narrow paths that are given. Especially when you're not asked to do it.

Mark Madsen writes about the same issue although his issue is the ideology of bad non-centralized Excel data in BI:

We're facing the incomplete data problem because of another piece of BI ideology: all the data must be centrally managed. This is unrealistic. We can't possibly house every last bit of data. Because of this reality, BI tools like Business Objects added the ability to bring outside data into reports. Other vendors moved the BI processing to the PC.

Our ideology has failed us by setting up a paradox. If we do use these features or tools, then we contribute to our biggest complaint about Excel — manipulation of data outside the centrally integrated view. If we don’t use them then users will continue to circumvent BI tools.

Mark Madsen: Spreadmarts and the Ideology of BI

Mark is right, people use and will use Excel. Don't try to convince them they're wrong because they are not. Just take it as a fact and build on it. Put the twisting path to use.

Reaching out for good data

When do you talk about good data? Google returns sentences like these:

• "We don't have really good data..."
• "Once we have good data..."
• "It's hard to make good policy decisions when they're not grounded in good data."
• "If you have good data..."
  
• "Do you have good data to validate your opinion?"
  

These are not positive statements but there's a hope the world will be better (once, if).

There is a difference between good data (data quality) and Good Data BI platform. However, I cannot resist to convert the sentences:

• "We don't have really Good Data..."
• "Once we have Good Data..."
• "It's hard to make good policy decisions when they're not grounded in Good Data."
• "If you have Good Data..."
  
• "Do you have Good Data to validate your opinion?"
  

It makes sense, doesn't it? Well it's not enough to have good data, you need a good analytical tool too.