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  • Investment research and trading

    Discussion of how data management and analytic technologies are used in trading and investment research. (As opposed to a discussion of the services we ourselves provide to investors.) Related subjects include:

    October 21, 2016

    Rapid analytics

    “Real-time” technology excites people, and has for decades. Yet the actual, useful technology to meet “real-time” requirements remains immature, especially in cases which call for rapid human decision-making. Here are some notes on that conundrum.

    1. I recently posted that “real-time” is getting real. But there are multiple technology challenges involved, including:

    2. In early 2011, I coined the phrase investigative analytics, about which I said three main things: Read more

    October 3, 2016

    Notes on the transition to the cloud

    1. The cloud is super-hot. Duh. And so, like any hot buzzword, “cloud” means different things to different marketers. Four of the biggest things that have been called “cloud” are:

    Further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. A number of vendors have backed away from such stories, but a few are still pushing it, including Oracle and Microsoft.

    This is a good example of Monash’s Laws of Commercial Semantics.

    2. Due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). The rest should use some combination of colo, SaaS, and public cloud.

    This fact now seems to be widely understood.

    Read more

    September 6, 2016

    “Real-time” is getting real

    I’ve been an analyst for 35 years, and debates about “real-time” technology have run through my whole career. Some of those debates are by now pretty much settled. In particular:

    A big issue that does remain open is: How fresh does data need to be? My preferred summary answer is: As fresh as is needed to support the best decision-making. I think that formulation starts with several advantages:

    Straightforward applications of this principle include: Read more

    August 21, 2016

    More about Databricks and Spark

    Databricks CEO Ali Ghodsi checked in because he disagreed with part of my recent post about Databricks. Ali’s take on Databricks’ position in the Spark world includes:

    Ali also walked me through customer use cases and adoption in wonderful detail. In general:

    The story on those sectors, per Ali, is:? Read more

    May 30, 2016

    Adversarial analytics and other topics

    Five years ago, in a taxonomy of analytic business benefits, I wrote:

    A large fraction of all analytic efforts ultimately serve one or more of three purposes:

    • Marketing
    • Problem and anomaly detection and diagnosis
    • Planning and optimization

    That continues to be true today. Now let’s add a bit of spin.

    1. A large fraction of analytics is adversarial. In particular: Read more

    May 20, 2015

    MemSQL 4.0

    I talked with my clients at MemSQL about the release of MemSQL 4.0. Let’s start with the reminders:

    The main new aspects of MemSQL 4.0 are:

    There’s also a new free MemSQL “Community Edition”. MemSQL hopes you’ll experiment with this but not use it in production. And MemSQL pricing is now wholly based on RAM usage, so the column store is quasi-free from a licensing standpoint is as well.

    Read more

    May 2, 2015

    Notes, links and comments, May 2, 2015

    I’m going to be out-of-sorts this week, due to a colonoscopy. (Between the prep, the procedure, and the recovery, that’s a multi-day disablement.) In the interim, here’s a collection of links, quick comments and the like.

    1. Are you an engineer considering a start-up? This post is for you. It’s based on my long experience in and around such scenarios, and includes a section on “Deadly yet common mistakes”.

    2. There seems to be a lot of confusion regarding the business model at my clients Databricks. Indeed, my own understanding of Databricks’ on-premises business has changed recently. There are no changes in my beliefs that:

    However, I now get the impression that revenue from such relationships is a bigger deal to Databricks than I previously thought.

    Databricks, by the way, has grown to >50 people.

    3. DJ Patil and Ruslan Belkin apparently had a great session on lessons learned, covering a lot of ground. Many of the points are worth reading, but one in particular echoed something I’m hearing lots of places — “Data is super messy, and data cleanup will always be literally 80% of the work.” Actually, I’d replace the “always” by something like “very often”, and even that mainly for newish warehouses, data marts or datasets. But directionally the comment makes a whole lot of sense.

    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

    September 21, 2014

    Data as an asset

    We all tend to assume that data is a great and glorious asset. How solid is this assumption?

    *”Our assets are our people, capital and reputation. If any of these is ever diminished, the last is the most difficult to restore.” I love that motto, even if Goldman Sachs itself eventually stopped living up to it. If nothing else, my own business depends primarily on my reputation and information.

    This all raises the idea — if you think data is so valuable, maybe you should get more of it. Areas in which enterprises have made significant and/or successful investments in data acquisition include:? Read more

    May 6, 2014

    Notes and comments, May 6, 2014

    After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.

    Here is a catch-all post to complete the set.? Read more

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