Analysis of companies, products, and user strategies in the area of business intelligence. Related subjects include:
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.
- Volume has been solved. There are Hadoop installations with 100s of petabytes of data, analytic RDBMS with 10s of petabytes, general-purpose Exadata sites with petabytes, and 10s/100s of petabytes of analytic Accumulo at the NSA. Further examples abound.
- Velocity is being solved. My recent post on Hadoop-based streaming suggests how. In other use cases, velocity is addressed via memory-centric RDBMS.
- Variety and Variability have been solved. MongoDB, Cassandra and perhaps others are strong NoSQL choices. Schema-on-need is in earlier days, but may help too.
2. Even so, there’s much room for innovation around data movement and management. I’d start with:
- Product maturity is a huge issue for all the above, and will remain one for years.
- Hadoop and Spark show that application execution engines:
- Have a lot of innovation ahead of them.
- Are tightly entwined with data management, and with data movement as well.
- Hadoop is due for another refactoring, focused on both in-memory and persistent storage.
- There are many issues in storage that can affect data technologies as well, including but not limited to:
- Solid-state (flash or post-flash) vs. spinning disk.
- Networked vs. direct-attached.
- Virtualized vs. identifiable-physical.
- Graph analytics and data management are still confused.
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:
- Web, network and other IT logs.
- Game and mobile app event data.
- CDRs (telecom Call Detail Records).
- “Phone-home” data from large numbers of identical electronic products (for example set-top boxes).
- Sensor network output (for example from a pipeline or other utility network).
- Vehicle telemetry.
- Health care data, in hospitals.
- Digital health data from consumer devices.
- Images from public-safety camera networks.
- Stock tickers (if you regard them as being machine-generated, which I do).
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
1. A couple years ago I wrote skeptically about integrating predictive modeling and business intelligence. I’m less skeptical now.
- The predictive experimentation I wrote about over Thanksgiving calls naturally for some BI/dashboarding to monitor how it’s going.
- If you think about Nutonian’s pitch, it can be approximated as “Root-cause analysis so easy a business analyst can do it.” That could be interesting to jump to after BI has turned up anomalies. And it should be pretty easy to whip up a UI for choosing a data set and objective function to model on, since those are both things that the BI tool would know how to get to anyway.
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:
- Suppose we have lots of logs about lots of things.* Machine learning can help:
- Notice what’s an anomaly.
- Group* together things that seem to be experiencing similar anomalies.
- That can inform a BI-plus interface for a human to figure out what is happening.
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
|Categories: Business intelligence, Greenplum, Hadoop, Hortonworks, Log analysis, Neo Technology and Neo4j, Nutonian, Predictive modeling and advanced analytics, RDF and graphs, WibiData||1 Comment|
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:
- Accept instructions.
- Execute them.
And hence almost all IT product differentiation fits into two buckets:
- Easier instruction-giving, whether that’s in the form of a user interface, a language, or an API.
- Better execution, where “better” usually boils down to “faster”, “more reliable” or “more reliably fast”.
As examples of this reductionism, please consider:
- Application development is of course a matter of giving instructions to a computer.
- Database management systems accept and execute data manipulation instructions.
- Data integration tools accept and execute data integration instructions.
- System management software accepts and executes system management instructions.
- Business intelligence tools accept and execute instructions for data retrieval, navigation, aggregation and display.
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
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:
- Datameer does business intelligence, but not at human real-time speeds. Datameer query durations are sometimes sub-minute, but surely not sub-second.
- Datameer also does lightweight predictive analytics/machine learning — k-means clustering, decision trees, and so on.
Key aspects include:
- Datameer runs straight against Hadoop.
- Like many other analytic offerings, Datameer is meant to be “self-service”, for line-of-business business analysts, and includes some “data preparation”. Datameer also has had some data profiling since Datameer 4.0.
- The main way of interacting with Datameer seems to be visual analytic programming. However, I Datameer has evolved somewhat away from its original spreadsheet metaphor.
- Datameer’s primitives resemble those you’d find in SQL (e.g. JOINs, GROUPBYs). More precisely, that would be SQL with a sessionization extension; e.g., there’s a function called GROUPBYGAP.
- Datameer lets you write derived data back into Hadoop.
|Categories: Business intelligence, Databricks, Spark and BDAS, Datameer, Hadoop, Log analysis, Market share and customer counts, Predictive modeling and advanced analytics, Web analytics||5 Comments|
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.:
- The economies of scale of colocation-or-cloud over operating your own data center are compelling. Most of the reasons you outsource hardware manufacture to Asia also apply to outsourcing data center operation within the United States. (The one exception I can think of is supply chain.)
- The arguments for cloud specifically over colocation are less persuasive. Colo providers can even match cloud deployments in rapid provisioning and elastic pricing, if they so choose.
- Surely not coincidentally, I am told that Rackspace is deemphasizing cloud, reemphasizing colocation, and making a big deal out of Open Compute. In connection with that, Rackspace has pulled back from its leadership role in OpenStack.
- I’m hearing much more mention of Amazon Redshift than I used to. It seems to have a lot of traction as a simple and low-cost option.
- I’m hearing less about Elastic MapReduce than I used to, although I imagine usage is still large and growing.
- In general, I get the impression that progress is being made in overcoming the inherent difficulties in cloud (and even colo) parallel analytic processing. But it all still seems pretty vague, except for the specific claims being made for traction of Redshift, EMR, and so on.
- Teradata recently told me that in colocation pricing, it is common for floor space to be everything, with power not separately metered. But I don’t think that trend is a big deal, as it is not necessarily permanent.
- Cloud hype is of course still with us.
- Other than the above, I stand by my previous thoughts on appliances, clusters and clouds.
3. As for the analytic DBMS industry: Read more
I’ve talked with many companies recently that believe they are:
- Focused on building a great data management and analytic stack for log management …
- … unlike all the other companies that might be saying the same thing …
- … and certainly unlike expensive, poorly-scalable Splunk …
- … and also unlike less-focused vendors of analytic RDBMS (which are also expensive) and/or Hadoop distributions.
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
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
- Fancy-visualization business intelligence
- 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:
- Paxata is offering easy — and hopefully in the future comprehensive — “data preparation” tools …
- … that are meant to be used by business analysts rather than ETL (Extract/Transform/Load) specialists or other IT professionals …
- … where what Paxata means by “data preparation” is not specifically what a statistician would mean by the term, but rather generally refers to getting data ready for business intelligence or other analytics.
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
|Categories: Business intelligence, Datameer, EAI, EII, ETL, ELT, ETLT, Predictive modeling and advanced analytics, Progress, Apama, and DataDirect||9 Comments|
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.
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 significant minority of enterprises are highly committed to their enterprise DBMS standards.
- Another significant minority aren’t quite as committed, but set pretty high bars for new DBMS products to cross nonetheless.
- FUD (Fear, Uncertainty and Doubt) about new DBMS is often justifiable, about stability and consistent performance alike.
A particularly complex version of this dynamic has played out in the market for analytic RDBMS/appliances.
- First the newer products (from Netezza onwards) were sold to organizations who knew they wanted great performance or price/performance.
- Then it became more about selling “business value” to organizations who needed more convincing about the benefits of great price/performance.
- Then the behemoth vendors became more competitive, as Teradata introduced lower-price models, Oracle introduced Exadata, Sybase got more aggressive with Sybase IQ, IBM bought Netezza, EMC bought Greenplum, HP bought Vertica and so on. It is now hard for a non-behemoth analytic RDBMS vendor to make headway at large enterprise accounts.
- Meanwhile, Hadoop has emerged as serious competitor for at least some analytic data management, especially but not only at internet companies.
Otherwise I’d say: Read more