Analysis of companies, products, and user strategies in the area of business intelligence. Related subjects include:
If I had my way, the business intelligence part of investigative analytics — i.e. , the class of business intelligence tools exemplified by QlikView and Tableau — would continue to be called “data exploration”. Exploration what’s actually going on, and it also carries connotations of the “fun” that users report having with the products. By way of contrast, I don’t know what “data discovery” means; the problem these tools solve is that the data has been insufficiently explored, not that it hasn’t been discovered at all. Still “data discovery” seems to be the term that’s winning.
Confusingly, the Teradata Aster library of functions is now called “Discovery” as well, although thankfully without the “data” modifier. Further marketing uses of the term “discovery” will surely follow.
Enough terminology. What sets exploration/discovery business intelligence tools apart? I think these products have two essential kinds of feature:
- Query modification.
- Query result revisualization.*
|Categories: Business intelligence, Endeca, Memory-centric data management, QlikTech and QlikView, Tableau Software||8 Comments|
It’s hard to make data easy to analyze. While everybody seems to realize this — a few marketeers perhaps aside — some remarks might be useful even so.
Many different technologies purport to make data easy, or easier, to an analyze; so many, in fact, that cataloguing them all is forbiddingly hard. Major claims, and some technologies that make them, include:
- “We get data into a form in which it can be analyzed.” This is the story behind, among others:
- Most of the data integration and ETL (Extract/Transform/Load) industries, software vendors and consulting firms alike.
- Many things that purport to be “analytic applications” or data warehouse “quick starts”.
- “Data reduction” use cases in event processing.*
- Text analytics tools.
- “Forget all that transformation foofarah — just load (or write) data into our thing and start analyzing it immediately.” This at various times has been much of the story behind:
- Relational DBMS, according to their inventor E. F. Codd.
- MOLAP (Multidimensional OnLine Analytic Processing), also according to RDBMS inventor E. F. Codd.
- Any kind of analytic DBMS, or general purpose DBMS used for data warehousing.
- Newer kinds of analytic DBMS that are faster than older kinds.
- The “data mart spin-out” feature of certain analytic DBMS.
- In-memory analytic data stores.
- NoSQL DBMS that have a few analytic features.
- TokuDB, similarly.
- Electronic spreadsheets, from VisiCalc to Datameer.
- “Our tools help you with specific kinds of analyses or analytic displays.” This is the story underlying, among others:
- The business intelligence industry.
- The predictive analytics industry.
- Algorithmic trading use cases in complex event processing.*
- Some analytic applications.
*Complex event/stream processing terminology is always problematic.
My thoughts on all this start: Read more
I’m usually annoyed by lists of year-end predictions. Still, a reporter asked me for some, and I found one kind I was comfortable making.
Trends that I think will continue in 2013 include:
Growing attention to machine-generated data. Human-generated data grows at the rate business activity does, plus 0-25%. Machine-generated data grows at the rate of Moore’s Law, also plus 0-25%, which is a much higher total. In particular, the use of remote machine-generated data is becoming increasingly real.
Hadoop adoption. Everybody has the big bit bucket use case, largely because of machine-generated data. Even today’s technology is plenty good enough for that purpose, and hence justifies initial Hadoop adoption. Development of further Hadoop technology, which I post about frequently, is rapid. And so the Hadoop trend is very real.
Application SaaS. The on-premises application software industry has hopeless problems with product complexity and rigidity. Any suite new enough to cut the Gordian Knot is or will be SaaS (Software as a Service).
Newer BI interfaces. Advanced visualization — e.g. Tableau or QlikView — and mobile BI are both hot. So, more speculatively, are “social” BI (Business Intelligence) interfaces.
Price discounts. If you buy software at 50% of list price, you’re probably doing it wrong. Even 25% can be too high.
MySQL alternatives. NoSQL and NewSQL products often are developed as MySQL alternatives. Oracle has actually done a good job on MySQL technology, but now its business practices are scaring companies away from MySQL commitments, and newer short-request SQL DBMS are ready for use.
|Categories: Business intelligence, Hadoop, MySQL, NewSQL, NoSQL, Open source, Oracle, Pricing, Software as a Service (SaaS), Surveillance and privacy||3 Comments|
Merv Adrian and Doug Henschen both reported more details about Amazon Redshift than I intend to; see also the comments on Doug’s article. I did talk with Rick Glick of ParAccel a bit about the project, and he noted:
- Amazon Redshift is missing parts of ParAccel, notably the extensibility framework.
- ParAccel did some engineering to make its DBMS run better in the cloud.
- Amazon did some engineering in the areas it knows better than ParAccel — cloud provisioning, cloud billing, and so on.
“We didn’t want to do the deal on those terms” comments from other companies suggest ParAccel’s main financial take from the deal is an already-reported venture investment.
The cloud-related engineering was mainly around communications, e.g. strengthening error detection/correction to make up for the lack of dedicated switches. In general, Rick seemed more positive on running in the (Amazon) cloud than analytic RDBMS vendors have been in the past.
So who should and will use Amazon Redshift? For starters, I’d say: Read more
|Categories: Amazon and its cloud, Business intelligence, Cloud computing, Data mart outsourcing, Data warehousing, Infobright, ParAccel, Predictive modeling and advanced analytics, Pricing, Vertica Systems||4 Comments|
Business intelligence dashboards are frequently bashed. I slammed them back in 2006 and 2007. Mark Smith dropped the hammer last August. EIS, the most dashboard-like pre-1990s analytic technology, was also the most reviled. There are reasons for this disdain, but even so dashboards shouldn’t be dismissed entirely.
In essence, I’d say:
- Dashboards are overrated and oversold.
- They are useful even so.
- Their usefulness is ebbing as technology advances.
In particular: Read more
Imagine a website whose purpose is to encourage consumers to take actions — for example to click on an ad, click on the next page, or actually make a purchase. Best practices for such a site include:
- An ever-evolving user experience, informed by — among other factors — creativity, brand identity, the vendor’s evolving product line itself, and …
- … predictive modeling.
- Personalization based on predictive modeling.
Those predictive models themselves will keep changing, because:
- Organizations learn.
- Consumer tastes change.
- More or different kinds of data keep becoming available.
In that situation, what would it mean to offer the website owner a predictive modeling “application”? Read more
I can think of seven major reasons not to use an analytic RDBMS. One is good; but the other six seem pretty questionable, niche circumstances excepted, especially at this time.
The good reason to not have an analytic RDBMS is that most organizations can run perfectly well on some combination of:
- SaaS (Software as a Service).
- A low-volume static website.
- A network focused on office software.
- A single cheap server, likely running a single instance of a general-purpose RDBMS.
Those enterprises, however, are generally not who I write for or about.
The six bad reasons to not have an analytic RDBMS all take the form “Can’t some other technology do the job better?”, namely:
- A data warehouse that’s just another instance of your OLTP (OnLine Transaction Processing) RDBMS. If your problem is that big, it’s likely that a specialized analytic RDBMS will be more cost-effective and generally easier to deal with.
- MOLAP (Multi-Dimensional OnLine Analytic Processing). That ship has sailed … and foundered … and been towed to drydock.
- In-memory BI. QlikView, SAP HANA, Oracle Exalytics, and Platfora are just four examples of many. But few enterprises will want to confine their analytics to such data as fits affordably in RAM.
- Non-tabular* approaches to investigative analytics. There are many examples in the Hadoop world — including the recent wave of SQL add-ons to Hadoop — and some in the graph area as well. But those choices will rarely suffice for the whole job, as most enterprises will want better analytic SQL performance for (big) parts of their workloads.
- Tighter integration of analytics and OLTP (OnLine Transaction Processing). Workday worklets illustrate that business intelligence/OLTP integration is a really good idea. And it’s an idea that Oracle and SAP can be expected to push heavily, when they finally get their product acts together. But again, that’s hardly all the analytics you’re going to want to do.
- Tighter integration of analytics and other short-request processing. An example would be maintaining a casual game’s leaderboard via a NoSQL write-optimized database. Yet again, that’s hardly all the analytics a typical enterprise will want to do.
|Categories: Business intelligence, Data warehousing, Games and virtual worlds, Hadoop, In-memory DBMS, MOLAP||10 Comments|
I recently proposed a 2×2 matrix of BI use cases:
- Is there an operational business process involved?
- Is there a focus on root cause analysis?
Let me now introduce another 2×2 matrix of analytic scenarios:
- Is there a compelling need for super-fresh data?
- Who’s consuming the results — humans or machines?
My point is that there are at least three different cool things people might think about when they want their analytics to be very fast:
- Fast investigative analytics — e.g., business intelligence with great query response.
- Computations on very fresh data, presented to humans — e.g. “heartbeat” graphics monitoring a network.
- Computations on very fresh data, presented back to a machine — e.g., a recommendation engine that includes makes good use of data about a user’s last few seconds of actions.
There’s also one slightly boring one that however drives a lot of important applications: Read more
|Categories: Business intelligence, Complex event processing (CEP), Games and virtual worlds, Log analysis, Predictive modeling and advanced analytics, Splunk, WibiData||4 Comments|
Stuart Frost, of DATAllegro fame, has started a small family of companies, and they’ve become my clients sort of as a group. The first one that I’m choosing to write about is Cirro, for which the basics are:
- Cirro does data federation for analytics.
- Cirro has 10 full-time people plus 4 part-timers.
- Cirro launched its product in June.
- Cirro doesn’t have customers yet, but hopes to fix that soon.
Data federation stories are often hard to understand because, until you drill down, they implausibly sound as if they do anything for everybody. That said, it’s reasonable to think of Cirro as a layer between Hadoop and your BI tool that:
- Helps with data transformations.
- Helps join Hadoop data to relational tables, even if the joins are large ones.
In both cases, Cirro is calling on your data management software for help, RDBMS or Hadoop as the case may be.
More precisely, Cirro’s approach is: Read more
|Categories: Business intelligence, Cirro, Data integration and middleware, Hadoop, MapReduce, Tableau Software||4 Comments|
When I wrote last week that I have at least 5 clients claiming they’re uniquely positioned to support BI over Hadoop (most of whom partner with a 6th client, Tableau) the non-partnering exception I had in mind was Platfora, Ben Werther’s oh-so-stealthy startup that is finally de-stealthing today. Platfora combines:
- An interesting approach to analytic data management.
- Business intelligence tools integrated with that.
The whole thing sounds like a perhaps more general and certainly non-SaaS version of what Metamarkets has been offering for a while.
The Platfora technical story starts: Read more
|Categories: Business intelligence, Columnar database management, Data models and architecture, Data warehousing, Database compression, Hadoop, Memory-centric data management, Platfora||6 Comments|