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Happy New Year, BI Bake Off, and Data

by Cindi Howson  |  January 5, 2018  |  5 Comments

Happy New Year! Baby, it’s cold outside, but it’s hot in data in analytics!

I’m in New Jersey, a toasty -11 Fahrenheit today (that’s -23 in Celsius) so I already have visions of a Sydney beach dancing in my head. I hope you have registered for one of our global summits.  The early bird registration for the U.S. ends on January 12.

NewYearcrop

 

 

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I will be hosting Gartner’s fourth annual Analytics and BI Bake Off on Sunday, March 4th.  Vendors participating live on this year’s panel include Microsoft, MicroStrategy, Qlik, and Tableau.  It is a fun, informative, but stressful event for all of us. I mean, seriously, competitors showing you their goods in front of one another?

There is also another angle to this event in using data for good. I am also working on track session on this topic (Doing Good with Data: How to Impact Society and Create a “Data for Good” Culture) that is such a wonderful movement within our industry. So here is where I’d like your help. What data do you want to see used in this year’s bake off?   In past years, we’ve analyzed:

There are so many interesting data sets, but the challenge is that we have to keep it somewhat upbeat, nonpolitical, and clean data. Is the Opioid crisis too depressing?  I could happily analyze college football scores and stats, but that might bore some people. I suspect concussion data would be messy. If you have any good ideas, please let me know!

Also, just published today is an update review of Tableau, based on their 10.4 release. You might recall that Gartner is piloting more frequent updates to the Critical Capabilities notes across several coverage areas.  Seeing this pilot launched was one of my 2017 New Year’s goals. I’m still ruminating on this year’s goals and resolutions.

In the meantime, I’m wishing everyone a happy, healthy, exciting 2018!

I can dream of a beach but I'm really looking at snow

I can dream of a beach but I’m really looking at snow

Category: big-data  business-analytics  business-intelligence  

Cindi Howson
Research VP
1 years at Gartner
25 years IT Industry

Cindi Howson is a Research Vice President at Gartner, where she focuses on business intelligence (BI) and analytics. Her work includes writing about market trends, vendors and best practices and advising organizations on these subjects. Read Full Bio


Thoughts on Happy New Year, BI Bake Off, and Data


  1. Danielle says:

    Hey Cindi!
    Happy New Year! Will we see you at the MSTR World Run this year?! We are excited for the second running, we have over 250 registrants for the streets of Vegas!

    As for the BI bake-off, what about some smart city data? I actually don’t cover state and local but I’m guessing there are some great datasets being collected now that could yield some interesting visualizations?

    Hope all is well!
    Danielle

    • Cindi Howson says:

      Thanks, Danielle! Good ideas! Sadly, I have to miss MicroStrategy world this year. I think it’s my first time missing in like 10 years. It’s a busy MQ and event preparation time so just a clash of dates. I’ll be following on twitter and youtube. Have a great, great run! Cindi

  2. Hey Cindi, to give one man’s opinion – I would be interested in the opioid dataset! I’d also wonder if next year the requirements for the data being clean and stagnant should go away – perhaps slightly bigger, messier, and more frequently updated data would prove interesting as well.

    • Cindi Howson says:

      Thank you, Scott. Truth be told, all the data sets are messy. It’s more a question of how messy. I don’t want anyone’s participation in the bake off to be a consulting effort to build out the demo. We ask a lot of the vendors as is, so I want to be respectful of the time invested while also making it a useful demo. I haven’t yet looked at how difficult or messy this data set is. Hope to see you there. Regards, Cindi

  3. Bruno Aziza says:

    Hey Cindi – Happy New Year. I think that a good idea would be to go after a very large data set and host it in the cloud – something like RedShift or Google BigQuery. What do you think?

    Analytically Yours,
    Bruno



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