Salesperson: “What shipped yesterday? Ok, what’s the breakdown? Woah, what happened in that department? That markdown is too steep, who wrote that order? Which customer? What’s that rep’s extension?”

With one-second results, that analysis would have happened in the time it took you to read it. This is a competition against human nature. One-second results makes the difference between wishing you had the answer and getting it, multiplied over and over throughout the day.

The impact on a business is not from faster queries alone. Behavior changes when decision makers trust that the data is immediately at hand. The relationship to data changes when you can find the answer while you think about it and not lose your train of thought.

Because the query engine can respond to any query in one second, we can make every path of exploration available at the beginning. One application can take the place of many reports. Users can begin to query immediately and along any drill path. The benefit of one-second results is diminished if users have to first identify the report that has the data and filtering options they need.

Can OLAP deliver this? No. We must combine speed of execution with rapid application development, full transaction details, and eliminate predefined drill paths. OLAP/MOLAP/ROLAP/SCHMOLAP can’t take us into this new era. In-memory associative and column-store databases can.

With one-second results, you don’t build a query and then start the execution. Instead, the results update as soon as you pick the first filtering option, whether it’s the day, order number or country of origin. You get immediate feedback before you make your next selection. Also, the filter options can change based on the results. Maybe you remove options that are incompatible with the selections made so far. By shrinking the feedback loop with one-second results, the filtering options can show intelligent behavior to help guide users or add context to the results. This level of dynamism lets users roll back and forth through their ideas. They can cross-reference without losing a train of thought, or discover and follow tangents that are more important.

It’s not just one decision maker getting an answer quickly. Interactions and processes benefit. Workers get feedback in near-real-time. We can do tricks like running the same query once per second. Ridiculous? This isn’t paradise, I live in the land of low budgets and “getting it done”. Vendor and customer data is available right when they’re on the phone. Less “I’ll get back to you” and more “I have that info right in front of me.” I’ve also noticed that it’s harder to bullshit when anyone in the meeting can easily explore the data on their laptop and get the real answer.

In companies where I can deliver one-second results, I spend a lot of time reconditioning people to ask for anything they desire, because now I can put any information at their fingertips, no matter how many tables, how much detail and with little knowledge of how they want to look at the data.

For nearly all companies, the entire transactional database can be copied as-is into a one-second query engine. Add a BI tool on top, rename some fields and identify the table relationships. Time is spent developing the frontend to deliver the best reports and analysis. One person can build the entire solution. Since the transactional model is already validated, there is no data modeling, no formal architecture and little documentation. This might be frightening to enterprises but the benefits are huge for strapped IT budgets.

A one-second query engine needs an interactive frontend to take advantage of it. We also need simpler ETL tools. With the engine in place first, developers will connect the dots and the tools will be built to take advantage of the new abilities.

None of this is theoretical. I’ve been doing this for the past 7 years with an in-memory associative database, ETL tool and interactive frontend called QlikView. When information flows at the speed of thought, it changes decision-maker behavior and the business process. When we can prototype and deploy one-second query engines quickly, then ideas can be built and tested quickly. Most ideas won’t be new or unexpected, but they were impossible or impractical without one-second results.

Related posts:

1. Peter Batty Discusses One-Second Results And Geospatial Analysis
2. Low-Cost Data Analysis & Visualization: It’s Getting Better All The Time
3. What makes QlikView so good?

Of course, I could just buy QlikView, but what can be done for less $money? Unfortunately data warehouses and BI front-ends are not sexy problems in the opensource community. Graphs and charts get a little more attention, but you’ll need to write your own code to glue them to your application.

In summary, what can I say about our options?

First, write your own ETL. Why do opensource ETL tools like Talend and Kettle work so hard to rebuild Informatica? It reminds me of Linux in the 1990s when the community wanted to beat Windows and kept working to look like Windows and wondering when victory would arrive. Informatica, like OLAP and mainframes, is from an era when memory was scarce; languages were low-level, slow to compile & run, abstracted little and were not at all portable. On top of that, ODBC drivers were tightly controlled and costly.

But now we can pick from many great scripting languages. Today’s languages abstract the hard parts, are easy to read, can be edited while executing and talk to any system, database, web service or application. I think the next direction for ETL will be a simple (but extensible) transformation language using an ORM wrapper… Rails on ETL. Until that arrives, you can achieve everything you need with PHP, Perl, Ruby and others.

Best option for low-cost data warehouse?

Check out the totally free MonetDB. Unless Vertica or InfoBright reconsiders releasing a low/no cost option, MonetDB will likely mature to become a first-choice column-store database. It’s an academic project that has earned a sizeable development community and user base. The product is functional today for tens of millions of rows (maybe more). So far I have personally worked with a few million rows in MonetDB and I’d like to use it again. With a little focus on usability and packaging, it could be a contender.

Greenplum, freely available for development, won’t help. The architecture is designed around Massively Parallel Processing. As a single, standalone installation, it’s basically just PostgreSQL. You won’t see extra performance without a farm of servers.

To my surprise, MySQL itself is not too bad. The MyISAM tables are speedy and Alex Tomic wrote a post about using multiple queries against the Archive storage engine and how to steal an index with that engine. With basic MyISAM on a fast server, I’m running 10GB table scans in under a minute, but moderate aggregations take a few minutes. Architecturally, MySQL is limited. One query = one thread = one core. Running two simultaneous queries is an option, but MySQL still would not do the kind of transparent, optimized caching that you need for a warehouse. Throughput is limited to disk I/O speed. InfoBright has built a column-store storage engine for MySQL but it’s targeted for the enterprise only.

What about the front end?

For the money and quality and ease of integration, it’s hard to beat Tableau. $1800 bucks isn’t cheap, but for a small business that truly needs to analyze patterns, this will do the job and it makes very pretty charts. The most recent version has integrated support for mapping based on zip code, area code, state, country and others. The maps also incorporate Census and USGS data and are pulled live from an online source. They look great! Tableau has always had a smooth, easy-to-understand layout and a crisp look that makes each chart very attractive in a presentation. It also automatically guesses what chart you want based on the quality & number of aggregates and dimensions.

The drawback is that Tableau doesn’t have its own high-speed database or ETL tool. Tableau can’t shine until a low/no-cost read-optimized database is available. Until then, it does support the most common databases and data warehouses, both commercial and open-source. Except it can’t handle generic ODBC and I don’t know why.

There’s JasperSoft = CrystalReports + OLAP + Informatica + Web Dashboards. Each component is from a different opensource project, so they don’t all use the same platform or interface, and they can’t all read the same data sources. The democratization of BI is NOT going to come from enterprise tools made cheap; it will come from simple disruptive tools that add new ideas and polish with each release. Sorry, Jasper.

What would I use to build a reporting system for a smaller business?

Well, assuming we’re doing it to make more money, not to keep up appearances, the best choice is still to pay the money for QlikView. It reads ODBC, OLE DB, text files and Excel–everything a business needs. The ETL language is easy to understand for any businessperson that has put together an Access database or enjoys Excel formulas (blech!). The GUI front-end designer is powerful & straightforward. And the in-memory database behind QlikView is so incredibly fast that I routinely analyze 10 million of rows in a split-second. It’s a one-stop shop.

Tableau is a good option but you lose the database and ETL. Maybe you don’t have a large volume of data or maybe it’s all in one view in the database–Tableau could work for you.

At a lower cost? Well, it definitely comes down to tradeoffs in coder skill, money, development time and ease of use. Whereas in QlikView anyone can write the basic code to read a couple tables, all other solutions demand heavy lifting somehwere.

If I was doing it for free?

I’d start with PHP, and possibly Ruby. Read from a database, calculate, generate Google Charts, and maybe use one of the low/no-cost Flash-based charting libraries for interactive splash. In a future post I’d like to cover ORMs and Google Chart APIs and how it can help get these projects off and running quickly.

Got any ideas? I’m always on the lookout for a faster cheaper better way to create these solutions.

50 Cool Things You Can Do with Google Charts

Related posts:

1. Interactive Information Visualization
2. Response to the Tableau 3.0 Webinar
3. What’s Vertica?

Related posts:

1. Rosling’s Trendalizer Program
2. Hans Rosling (of Gapminder.org) — Incredible Info Displays
3. What Software Was Hans Rosling Using?

I’ve been implementing speed-of-thought interactive BI tools for 6 years and I don’t want to do it any other way. When I watched my first seasoned executive lose restraint and laugh uncontrollably as he got instant answers to his hardest questions, I knew this was the only way to go. When my end-user training sessions end late because everyone is so excited about what they can do, it’s clear that people NEED interactivity.

Related posts:

1. Low-Cost Data Analysis & Visualization: It’s Getting Better All The Time
2. Response to the Tableau 3.0 Webinar
3. Social Explorer — Interactive Census Data

Annotations should be required in every BI tool. The ability to mark reference lines and data points on graphs and tables is critical to clear communication. Placing an annotation on a point in space does not require a data point to exist there, another nice feature. The smart BI vendors are focusing on collaboration and communication among users.

“Groups” stole their name from the “groups” of 2.x which are now the “sets” of 3.0 and can be used like so: similar dimensions such as coffee and tea, which may need to be represented in the database as separate product lines, can now be combined on the fly within Tableau by an end user under the simple heading “drinks”. This would make it easy to answer a question about food vs drink sales without the need to export to Excel and spend more time adding up the drink categories. In short, “groups” bring dimension values together and “sets” allow for separating special values from the rest of a dimensions values–and both can be done by the end user. Pretty nice.

I think the strongest competitor for visualization is Spotfire. However, Tableau’s use of live database interaction will become an advantage as data warehouse implementations shift to high-performance in-memory read-optimized databases. Was that over-hyphenated? Spotfire’s initial data loads are inflexible and I wouldn’t recommend it if you need to update a large dataset frequently.

Unlike QlikView, all of Tableau’s data needs to be in a single database. With good design, this is not a performance issue. The problem is that the extra expense of hardware and software to store a separate data warehouse and run ETL processing may push Tableau’s final price tag far above QlikView, which can easy pull from multiple sources and uses its own high-speed database.

Related posts:

1. Review of Tableau Professional
2. Low-Cost Data Analysis & Visualization: It’s Getting Better All The Time
3. More on Vertica

Related posts:

1. Baby Name Popularity Through Time
2. University Creates New "Analytics" Computer Science Program
3. What happened to Google?

Related posts:

1. Authenticity
2. Hans Rosling Is My Hero

Related posts:

1. GCensus: Using Google Earth for Census Analysis
2. BI Is A Social Problem — Juice Analytics Breaks It Down
3. Interactive Information Visualization

Related posts:

1. Social Explorer — Interactive Census Data
2. What happened to Google?
3. Pentaho Releases Google Maps Integration