Business intelligence

Analysis of companies, products, and user strategies in the area of business intelligence. Related subjects include:

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

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 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

November 15, 2014

Technical differentiation

I commonly write about real or apparent technical differentiation, in a broad variety of domains. But actually, computers only do a couple of kinds of things:

And hence almost all IT product differentiation fits into two buckets:

As examples of this reductionism, please consider:

Similar stories are true about application software, or about anything that has an API (Application Programming Interface) or SDK (Software Development Kit).

Yes, all my examples are in software. That’s what I focus on. If I wanted to be more balanced in including hardware or data centers, I might phrase the discussion a little differently — but the core points would still remain true.

What I’ve said so far should make more sense if we combine it with the observation that differentiation is usually restricted to particular domains. Read more

October 26, 2014

Datameer at the time of Datameer 5.0

Datameer checked in, having recently announced general availability of Datameer 5.0. So far as I understood, Datameer is still clearly in the investigative analytics business, in that:

Key aspects include:

Read more

September 28, 2014

Some stuff on my mind, September 28, 2014

1. I wish I had some good, practical ideas about how to make a political difference around privacy and surveillance. Nothing else we discuss here is remotely as important. I presumably can contribute an opinion piece to, more or less, the technology publication(s) of my choice; that can have a small bit of impact. But I’d love to do better than that. Ideas, anybody?

2. A few thoughts on cloud, colocation, etc.:

3. As for the analytic DBMS industry: Read more

September 7, 2014

An idealized log management and analysis system — from whom?

I’ve talked with many companies recently that believe they are:

At best, I think such competitive claims are overwrought. Still, it’s a genuinely important subject and opportunity, so let’s consider what a great log management and analysis system might look like.

Much of this discussion could apply to machine-generated data in general. But right now I think more players are doing product management with an explicit conception either of log management or event-series analytics, so for this post I’ll share that focus too.

A short answer might be “Splunk, but with more analytic functionality and more scalable performance, at lower cost, plus numerous coupons for free pizza.” A more constructive and bottoms-up approach might start with:  Read more

August 14, 2014

“Freeing business analysts from IT”

Many of the companies I talk with boast of freeing business analysts from reliance on IT. This, to put it mildly, is not a unique value proposition. As I wrote in 2012, when I went on a history of analytics posting kick,

  • Most interesting analytic software has been adopted first and foremost at the departmental level.
  • People seem to be forgetting that fact.

In particular, I would argue that the following analytic technologies started and prospered largely through departmental adoption:

  • Fourth-generation languages (the analytically-focused ones, which in fact started out being consumed on a remote/time-sharing basis)
  • Electronic spreadsheets
  • 1990s-era business intelligence
  • Dashboards
  • Fancy-visualization business intelligence
  • Planning/budgeting
  • Predictive analytics
  • Text analytics
  • Rules engines

What brings me back to the topic is conversations I had this week with Paxata and Metanautix. The Paxata story starts:

Metanautix seems to aspire to a more complete full-analytic-stack-without-IT kind of story, but clearly sees the data preparation part as a big part of its value.

If there’s anything new about such stories, it has to be on the transformation side; BI tools have been helping with data extraction since — well, since the dawn of BI. Read more

July 20, 2014

Data integration as a business opportunity

A significant fraction of IT professional services industry revenue comes from data integration. But as a software business, data integration has been more problematic. Informatica, the largest independent data integration software vendor, does $1 billion in revenue. INFA’s enterprise value (market capitalization after adjusting for cash and debt) is $3 billion, which puts it way short of other category leaders such as VMware, and even sits behind Tableau.* When I talk with data integration startups, I ask questions such as “What fraction of Informatica’s revenue are you shooting for?” and, as a follow-up, “Why would that be grounds for excitement?”

*If you believe that Splunk is a data integration company, that changes these observations only a little.

On the other hand, several successful software categories have, at particular points in their history, been focused on data integration. One of the major benefits of 1990s business intelligence was “Combines data from multiple sources on the same screen” and, in some cases, even “Joins data from multiple sources in a single view”. The last few years before application servers were commoditized, data integration was one of their chief benefits. Data warehousing and Hadoop both of course have a “collect all your data in one place” part to their stories — which I call data mustering — and Hadoop is a data transformation tool as well.

Read more

July 14, 2014

21st Century DBMS success and failure

As part of my series on the keys to and likelihood of success, I outlined some examples from the DBMS industry. The list turned out too long for a single post, so I split it up by millennia. The part on 20th Century DBMS success and failure went up Friday; in this one I’ll cover more recent events, organized in line with the original overview post. Categories addressed will include analytic RDBMS (including data warehouse appliances), NoSQL/non-SQL short-request DBMS, MySQL, PostgreSQL, NewSQL and Hadoop.

DBMS rarely have trouble with the criterion “Is there an identifiable buying process?” If an enterprise is doing application development projects, a DBMS is generally chosen for each one. And so the organization will generally have a process in place for buying DBMS, or accepting them for free. Central IT, departments, and — at least in the case of free open source stuff — developers all commonly have the capacity for DBMS acquisition.

In particular, at many enterprises either departments have the ability to buy their own analytic technology, or else IT will willingly buy and administer things for a single department. This dynamic fueled much of the early rise of analytic RDBMS.

Buyer inertia is a greater concern.

A particularly complex version of this dynamic has played out in the market for analytic RDBMS/appliances.

Otherwise I’d say:  Read more

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