Chutzpah has gotten a bad name.  Merriam-Webster online describes it as “supreme self-confidence”, which sounds good, but then “nerve, gall”, which does not.  Urban Dictionary, in character, calls it “unmitigated effrontery or impudence; gall”.  Not good press.

Aristotle famously defined courage as the mean between cowardice and foolhardiness, both demarcating its edges and establishing a general principle that virtue is a moderation between extremes.

Pirke Avot, a section of the Jewish Talmud and (I believe) the first self-help book of all time, says: “If I am not for myself, who will be for me?  If I am for myself alone, what am I?  If not now, when?”

The first clause offers scope for chutzpah as a virtue, for what else is the elemental stuff of standing for oneself if not chutzpah?

I think we are on safe ground by taking a leaf from Aristotle’s book and defining chutzpah thus:

Chutzpah is the golden mean between self-effacement and shamelessness.

That’s the row I’ll try to hoe this year.  Breaking out of self-effacement while steering clear of shamelessness.

I’m kicking off a new project, the Chutzpah Project.

Well, I was going to kick off a new project on Jan. 1, but, like many great projects I’ve worked on over the years, it slipped.  Only by a month.

I loved Gretchen Rubin’s Happiness Project.  Her idea was to document – via blog and eventually book – her attempts, over a year, to get happier by trying stuff.  She drew up a list of stuff – four things a month – to try.  Things like “be physically active”, “be nice to others”, “try new things”.  She did four in January, four in February, etc.  And tried to do all 44 (11×4, if my arithmetic is correct) in December.

Of course, she didn’t succeed.  She couldn’t do everything, and some days she couldn’t do anything.  But she never gave up.  And she learned a lot about happiness along the way.

I want to do the same thing for chutzpah.  Give it a year.  Try four things a month.  Maybe even try 44 things in January 2014 (which will be a year).  Document lessons learned along the way.

I hope you’ll join me for the ride.

Dan G.

(Next Post: Chutzpah Redefined, Rescued from its Detractors)

I’ve been looking for a “tickler” app that would periodically remind me of people in my personal information cloud that I hadn’t pinged in a while, and help me to ping them.

Xobni did that a bit, I heard, but it was always a bit heavyweight for me.

Now there’s Contactually, a DC-based startup who snarfs up your contact and email databases and has you organize them into “buckets”.  Each bucket (e.g., “Peeps”) has a timeframe (e.g., 10 days) after which the contacts in that bucket start to “age”.  You are urged to ping your aging contacts, on the site and via email.  The software tracks when you have done so.

Neat, simple, elegant.  I’m a big fan after only a week of use.

(Full disclosure: I thought the company did some oafish marketing and “game mechanics”-style “engagement” that nearly brought me to blows with them, and almost got me to stop using the product.  But the product is terrific.  So, if, like me, you hate marketers getting in your shorts, please bear with it, because the product is worth it.)

Since a lot of people grumble about the “Big Data” meme — “what is this _big_ data anyhow” — I thought an analogy might help.

Big Data:Data::turkeys:chickens

A turkey is “really” just a big chicken.  Same limbs.  Same white and dark meat, same spices and herbs, similar taste.

But the scale of the turkey introduces new problems and requires new solutions:

• Will it fit in your oven?
• Will anything else fit in your oven if the turkey is there?
• Where will you cook the others things if they won’t fit?
• Do you have a roasting pan and rack big enough for a turkey?
• Can you muscle the turkey up and down-stairs to brine it in the cooler (the only place it will fit)?

Ok, I won’t belabor the point: Big Data is different from data because the scale means your old techniques won’t always work.

Have a great holiday.

Valhalla has been looking over the Big Data market, trying to answer the question: “how far along is the market?”  Are there really only four or so Big Data users — the likes of Google, Yahoo, Facebook, and Twitter — or are there more?  Is it an Early Adopter (or even merely a Tech Enthusiast market), or has it crossed the chasm?  What are the use cases?

Here are some of our findings:

1.The Big Data market is an Innovator/Early Adopter market overall, with possible Early Majority beachheads in web analytics and adtech

Although our interviewees described a larger number of use cases – “voice of the customer” analytics in marketing, M2M sensor processing, fraud and risk analysis, predictive analytics of various types – there was no hard evidence for widespread uses of Big Data today in these use cases, and many of the interviewees described them as “nascent” or “near-future” use cases.

There was, however, agreement that web analytics and adtech platforms were much further along in terms of using Big Data techniques for projects which were important to the customers’ businesses and mainstream today.

·         AdTech users employ Big Data technologies for real-time bidding (RTB) and managing and matching 3^rd-party data to ad inventory or online user data (this area seems to be called “data management platforms”, an area where DemDex (which was acquired by Adobe for $xxxM) is perhaps the poster child.

·         Web analytics users employ Big Data technologies for indexing web pages and extracting performance indicators from raw weblogs.

2.     Informants believe that Hadoop and its stack is likely to remain the central platform for the Big Data market, but there is contradictory evidence

I don’t personally agree with this finding, but our interviewees all said, implicitly and explicitly, that the Hadoop stack was going to be the basis for Big Data technologies going forward.

One very thoughtful analyst said explicitly that the MapReduce/Hadoop stack would evolve over time, and that new technologies – like Dremel or Storm or Spanner and so forth – would be incorporated into the Hadoop ecosystem rather than creating new ecosystems of their own.

The only problem with this point of view is that “legacy” Big Data techniques – data warehousing, RDBMS, classic Business Intelligence suites – have a vast market share and a long history of productive use cases.   How these platforms will interoperate in the future is unknown, and whether an approach like Hadapt’s (where a “classic” RDBMS or BI technology suite runs within the Hadoop stack) will prevail is still too early to call.

3.     Wikibon’s analysis sizes the Big Data market today at $5B

A quantitative Wikibon analysis, which is quite thoughtful, concludes that $480M of this revenue comes from what they call “pure play” vendors (i.e., Hadoop infrastructure vendors and some other NoSQL or NewSQL) and the balance from legacy players.

Very curious about your thoughts on this.

Derrick Harris has written the last couple of days a great deal on SQL front ends for MapReduce platforms.  This is a particularly meaty post.

What does it all mean?  That SQL support is a must-have for a self-respecting MR implementation, and everyone is rushing to provide it.

I’ve posted here, here, and here about the function that SQL plays in the legacy data fabric — a fence separating data management from data analysis, for example — and wondering out loud what will take its place in a NoSQL or PostSQL world.

This motion suggests that SQL may have some life in it yet.  Despite its RDBMS-ism, it is a rich data-analysis language, and it is the canvas upon which millions of data-analysis paintings have been painted.  It’s asking a lot to just throw that away and go back to writing software in what are really still 3GLs to get at data.

In any case, it’s an admission that the data fabric will be more PostSQL (including and building upon SQL) rather than NoSQL in the future.  And suggests that we need an expressive model of PostSQL data before we’ll have an expressive interface language for it.

Your thoughts?