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I am no longer with Valhalla Partners.

My thoughts on tech innovations will appear at www.dangordontech.com from now on.

Please come visit me there.

@Contactually is a great piece of software

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.)

Big Data and Turkeys

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.

Big Data, Big Dreams

We've got to be at or near the Peak of Inflated Expectations in the Hype Cycle for Big Data.  It's the point where the meme seems so powerful that everyone wants to associate themselves with it.

But, as happened with data mining, unstructured data mining, and other fevered dreams of extracting ponies from the manure heap of raw data, what if the insights we all believe are lurking in our data... aren't lurking, or can't be lured out of hiding?

I ran across a couple of posts this week that bear on the issue.

A post from Jeff Jonas. who can always be relied on to smash false idols, deals with this question.  As Jonas says:

The problem being; often the business objectives (e.g., finding a bomb) are simply not possible given the proposed observation space (data sources).

Dan Woods re-posts another variation on this theme:

...the data created and maintained outside your company is becoming much more important than the data that you can acquire from internal sources. Yet, few companies realize this and fewer are taking action. Instead, they are suffering from the Data Not Invented Here Syndrome.

In other words, there's a difference between Big Data techniques and magic.  Sigh.

Your thoughts?

Where is the Big Data market at today?

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.

 

Maybe SQL is the SQL of NoSQL

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?

The (Increasingly Worthless) Network Effect

The other day I did what I do with increasing frequency: I wanted to meet an exec (call him "Exec A") at a startup company (call it "Company X") where Valhalla might invest, so I looked in LinkedIn to see who was connected with them.

An old friend from Palo Alto days was indeed one degree of separation from Exec A, but when I contacted my friend -- and, by the way, it was great to catch up with him on all kinds of things -- he said, "I hardly know A and I know nothing about X".  He had LinkedIn with A because they had worked together once, but it was not a meaningful connection.

There are pressures to make meaningless connections: pressures on LinkedIn, on Facebook, on Twitter.  And a kind of Gresham's Law takes over: the bad links drive out the good.

I've watched it happen with UseNet, with email, with the Web, with portals, with Quora, with the social sites cited above.

So maybe there isn't an absolute "network effect".  Maybe above a certain size the debasement of links takes over and the value of network declines.

I'm certain that smarter folks than I have worked this problem.  I'd welcome any links to discussions.

But, please, only the good links.