January 30, 2015

Growth in machine-generated data

In one of my favorite posts, namely When I am a VC Overlord, I wrote:

I will not fund any entrepreneur who mentions “market projections” in other than ironic terms. Nobody who talks of market projections with a straight face should be trusted.

Even so, I got talked today into putting on the record a prediction that machine-generated data will grow at more than 40% for a while.

My reasons for this opinion are little more than:

I was referring to the creation of such data, but the growth rates of new creation and of persistent storage are likely, at least at this back-of-the-envelope level, to be similar.

Anecdotal evidence actually suggests 50-60%+ growth rates, so >40% seemed like a responsible claim.

Related links

January 27, 2015

Soft robots, Part 2 — implications

What will soft, mobile robots be able to do that previous generations cannot? A lot. But I’m particularly intrigued by two large categories:

There are still many things that are hard for humans to keep in good working order, including:

Sometimes the issue is (hopefully minor) repairs. Sometimes it’s cleaning or lubrication. In some cases one might want to upgrade a structure with fixed sensors, and the “repair” is mainly putting those sensors in place. In all these cases, it seems that soft robots could eventually offer a solution. Further examples, I’m sure, could be found in factories, mines, or farms.

Of course, if there’s a maintenance/repair need, inspection is at least part of the challenge; in some cases it’s almost the whole thing. And so this technology will help lead us toward the point that substantially all major objects will be associated with consistent flows of data. Opportunities for data analysis will abound.

Read more

January 27, 2015

Soft robots, Part 1 — introduction

There may be no other subject on which I’m so potentially biased as robotics, given that:

Still, I’m solely responsible for my own posts and opinions, while Kevin is busy running his startup (Pneubotics) and raising my grandson. And by the way — I’ve been watching the robotics industry slightly longer than Kevin has been alive. ;)

My overview messages about all this are:

Read more

January 19, 2015

Where the innovation is

I hoped to write a reasonable overview of current- to medium-term future IT innovation. Yeah, right. :) But if we abandon any hope that this post could be comprehensive, I can at least say:

1. Back in 2011, I ranted against the term Big Data, but expressed more fondness for the V words — Volume, Velocity, Variety and Variability. That said, when it comes to data management and movement, solutions to the V problems have generally been sketched out.

2. Even so, there’s much room for innovation around data movement and management. I’d start with:

3. As I suggested last year, data transformation is an important area for innovation.  Read more

January 10, 2015


There is much confusion about migration, by which I mean applications or investment being moved from one “platform” technology — hardware, operating system, DBMS, Hadoop, appliance, cluster, cloud, etc. — to another. Let’s sort some of that out. For starters:

I mixed together true migration and new-app platforms in a post last year about DBMS architecture choices, when I wrote: Read more

December 31, 2014

Notes on machine-generated data, year-end 2014

Most IT innovation these days is focused on machine-generated data (sometimes just called “machine data”), rather than human-generated. So as I find myself in the mood for another survey post, I can’t think of any better idea for a unifying theme.

1. There are many kinds of machine-generated data. Important categories include:

That’s far from a complete list, but if you think about those categories you’ll probably capture most of the issues surrounding other kinds of machine-generated data as well.

2. Technology for better information and analysis is also technology for privacy intrusion. Public awareness of privacy issues is focused in a few areas, mainly: Read more

December 16, 2014

WibiData’s approach to predictive modeling and experimentation

A conversation I have too often with vendors goes something like:

That was the genesis of some tidbits I recently dropped about WibiData and predictive modeling, especially but not only in the area of experimentation. However, Wibi just reversed course and said it would be OK for me to tell more or less the full story, as long as I note that we’re talking about something that’s still in beta test, with all the limitations (to the product and my information alike) that beta implies.

As you may recall:

With that as background, WibiData’s approach to predictive modeling as of its next release will go something like this: Read more

December 12, 2014

Notes and links, December 12, 2014

1. A couple years ago I wrote skeptically about integrating predictive modeling and business intelligence. I’m less skeptical now.

For starters:

I’ve also heard a couple of ideas about how predictive modeling can support BI. One is via my client Omer Trajman, whose startup ScalingData is still semi-stealthy, but says they’re “working at the intersection of big data and IT operations”. The idea goes something like this:

Makes sense to me.

* The word “cluster” could have been used here in a couple of different ways, so I decided to avoid it altogether.

Finally, I’m hearing a variety of “smart ETL/data preparation” and “we recommend what columns you should join” stories. I don’t know how much machine learning there’s been in those to date, but it’s usually at least on the roadmap to make the systems (yet) smarter in the future. The end benefit is usually to facilitate BI.

2. Discussion of graph DBMS can get confusing. For example: Read more

December 10, 2014

A few numbers from MapR

MapR put out a press release aggregating some customer information; unfortunately, the release is a monument to vagueness. Let me start by saying:

Anyhow, the key statement in the MapR release is:

… the number of companies that have a paid subscription for MapR now exceeds 700.

Unfortunately, that includes OEM customers as well as direct ones; I imagine MapR’s direct customer count is much lower.

In one gesture to numerical conservatism, MapR did indicate by email that it counts by overall customer organization, not by department/cluster/contract (i.e., not the way Hortonworks does). Read more

December 7, 2014

Hadoop’s next refactoring?

I believe in all of the following trends:

Trickier is the meme that Hadoop is “the new OS”. My thoughts on that start:

There is also a minor issue that if you distribute your Hadoop work among extra nodes you might have to pay a bit more to your Hadoop distro support vendor. Fortunately, the software industry routinely solves more difficult pricing problems than that.

Read more

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