Analysis of companies, products, and user strategies in the area of business intelligence. Related subjects include:
Let’s start with some terminology biases:
- I dislike the term “big data” but like the Vs that define it — Volume, Velocity, Variety and Variability.
- Though I think it’s silly, I understand why BI innovators flee from the term “business intelligence” (they’re afraid of not sounding new).
So when my clients at Zoomdata told me that they’re in the business of providing “the fastest visual analytics for big data”, I understood their choice, but rolled my eyes anyway. And then I immediately started to check how their strategy actually plays against the “big data” Vs.
It turns out that:
- Zoomdata does its processing server-side, which allows for load-balancing and scale-out. Scale-out and claims of great query speed are relevant when data is of high volume.
- Zoomdata depends heavily on Spark.
- Zoomdata’s UI assumes data can be a mix of historical and streaming, and that if looking at streaming data you might want to also check history. This addresses velocity.
- Zoomdata assumes data can be in a variety of data stores, including:
- Relational (operational RDBMS, analytic RDBMS, or SQL-on-Hadoop).
- Files (generic HDFS — Hadoop Distributed File System or S3).*
- NoSQL (MongoDB and HBase were mentioned).
- Search (Elasticsearch was mentioned among others).
- Zoomdata also tries to detect data variability.
- Zoomdata is OEM/embedding-friendly.
*The HDFS/S3 aspect seems to be a major part of Zoomdata’s current story.
Core aspects of Zoomdata’s technical strategy include: Read more
It’s difficult to project the rate of IT change in health care, because:
- Health care is suffused with technology — IT, medical device and biotech alike — and hence has the potential for rapid change. However, it is also the case that …
- … health care is heavily bureaucratic, political and regulated.
Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:
- The human-generated part of what’s in ordinary paper health records today, but across a patient’s entire lifetime. This of course includes notes created by doctors and other care-givers.
- Large amounts of machine-generated data, including:
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
- Most tests exploit electronic technology. Progress in electronics is intense.
- Biomedical research is itself intense.
- In particular, most research technologies (for example gene sequencing) can be made cheap enough over time to be affordable clinically.
- The output of consumer health-monitoring devices — e.g. Fitbit and its successors. The buzzword here is “quantified self”, but what it boils down to is that every moment of our lives will be measured and recorded.
- The results of clinical tests. Continued innovation can be expected in testing, for reasons that include:
These vastly greater amounts of data cited above will allow for greatly changed analytics.
1. There are multiple ways in which analytics is inherently modular. For example:
- Business intelligence tools can reasonably be viewed as application development tools. But the “applications” may be developed one report at a time.
- The point of a predictive modeling exercise may be to develop a single scoring function that is then integrated into a pre-existing operational application.
- Conversely, a recommendation-driven website may be developed a few pages — and hence also a few recommendations — at a time.
Also, analytics is inherently iterative.
- Everything I just called “modular” can reasonably be called “iterative” as well.
- So can any work process of the nature “OK, we got an insight. Let’s pursue it and get more accuracy.”
If I’m right that analytics is or at least should be modular and iterative, it’s easy to see why people hate multi-year data warehouse creation projects. Perhaps it’s also easy to see why I like the idea of schema-on-need.
2. In 2011, I wrote, in the context of agile predictive analytics, that
… the “business analyst” role should be expanded beyond BI and planning to include lightweight predictive analytics as well.
I gather that a similar point is at the heart of Gartner’s new term citizen data scientist. I am told that the term resonates with at least some enterprises. Read more
|Categories: Business intelligence, Data warehousing, Datameer, Hadoop, Log analysis, Oracle, Platfora, Predictive modeling and advanced analytics, SAS Institute, Software as a Service (SaaS), Tableau Software, Web analytics||2 Comments|
I hear much discussion of shortfalls in analytic technology, especially from companies that want to fill in the gaps. But how much do these gaps actually matter? In many cases, that depends on what the analytic technology is being used for. So let’s think about some different kinds of analytic task, and where they each might most stress today’s available technology.
In separating out the task areas, I’ll focus first on the spectrum “To what extent is this supposed to produce novel insights?” and second on the dimension “To what extent is this supposed to be integrated into a production/operational system?” Issues of latency, algorithmic novelty, etc. can follow after those. In particular, let’s consider the tasks: Read more
|Categories: Business intelligence, Data warehousing, Databricks, Spark and BDAS, Hadoop, Netezza, NoSQL, Predictive modeling and advanced analytics, Tableau Software||1 Comment|
I’m skeptical of data federation. I’m skeptical of all-things-to-all-people claims about logical data layers, and in particular of Gartner’s years-premature “Logical Data Warehouse” buzzphrase. Still, a reasonable number of my clients are stealthily trying to do some kind of data layer middleware, as are other vendors more openly, and I don’t think they’re all crazy.
Here are some thoughts as to why, and also as to challenges that need to be overcome.
There are many things a logical data layer might be trying to facilitate — writing, querying, batch data integration, real-time data integration and more. That said:
- When you’re writing data, you want it to be banged into a sufficiently-durable-to-acknowledge condition fast. If acknowledgements are slow, performance nightmares can ensue. So writing is the last place you want an extra layer, perhaps unless you’re content with the durability provided by an in-memory data grid.
- Queries are important. Also, they formally are present in other tasks, such as data transformation and movement. That’s why data manipulation packages (originally Pig, now Hive and fuller SQL) are so central to Hadoop.
Over the past couple years, there have been various quick comments and vague press releases about “BI for NoSQL”. I’ve had trouble, however, imagining what it could amount to that was particularly interesting, with my confusion boiling down to “Just what are you aggregating over what?” Recently I raised the subject with a few leading NoSQL companies. The result is that my confusion was expanded. Here’s the small amount that I have actually figured out.
As I noted in a recent post about data models, many databases — in particular SQL and NoSQL ones — can be viewed as collections of <name, value> pairs.
- In a relational database, a record is a collection of <name, value> pairs with a particular and predictable — i.e. derived from the table definition — sequence of names. Further, a record usually has an identifying key (commonly one of the first values).
- Something similar can be said about structured-document stores — i.e. JSON or XML — except that the sequence of names may not be consistent from one document to the next. Further, there’s commonly a hierarchical relationship among the names.
- For these purposes, a “wide-column” NoSQL store like Cassandra or HBase can be viewed much as a structured-document store, albeit with different performance optimizations and characteristics and a different flavor of DML (Data Manipulation Language).
Consequently, a NoSQL database can often be viewed as a table or a collection of tables, except that:
- The NoSQL database is likely to have more null values.
- The NoSQL database, in a naive translation toward relational, may have repeated values. So a less naive translation might require extra tables.
That’s all straightforward to deal with if you’re willing to write scripts to extract the NoSQL data and transform or aggregate it as needed. But things get tricky when you try to insist on some kind of point-and-click. And by the way, that last comment pertains to BI and ETL (Extract/Transform/Load) alike. Indeed, multiple people I talked with on this subject conflated BI and ETL, and they were probably right to do so.
|Categories: Business intelligence, Cassandra, EAI, EII, ETL, ELT, ETLT, HBase, MongoDB, NoSQL, Structured documents||5 Comments|
I found yesterday’s news quite unpleasant.
- A guy I knew and had a brief rivalry with in high school died of colon cancer, a disease that I’m at high risk for myself.
- GigaOm, in my opinion the best tech publication — at least for my interests — shut down.
- The sex discrimination trial around Kleiner Perkins is undermining some people I thought well of.
So I want to unclutter my mind a bit. Here goes.
1. There are a couple of stories involving Sam Simon and me that are too juvenile to tell on myself, even now. But I’ll say that I ran for senior class president, in a high school where the main way to campaign was via a single large poster, against a guy with enough cartoon-drawing talent to be one of the creators of the Simpsons. Oops.
2. If one suffers from ulcerative colitis as my mother did, one is at high risk of getting colon cancer, as she also did. Mine isn’t as bad as hers was, due to better tolerance for medication controlling the disease. Still, I’ve already had a double-digit number of colonoscopies in my life. They’re not fun. I need another one soon; in fact, I canceled one due to the blizzards.
Pro-tip — never, ever have a colonoscopy without some kind of anesthesia or sedation. Besides the unpleasantness, the lack of meds increases the risk that the colonoscopy will tear you open and make things worse. I learned that the hard way in New York in the early 1980s.
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||3 Comments|