Here’s a screen shot of the fail I noticed. Have a look at some of the tweets.

How many of them are about the weather versus something else?

First I see a person from Garland (not far away from me) who is tweeting what appears to be various national headlines, including one about the real estate situation in Florida. Apparently there are “clouds on the horizon.” Does that have anything to do with weather in my area? No, but it does have a key word “clouds” included.

I like the next example even more. We certainly have clouds here in Texas, but I can’t remember the last time we had a blizzard. Yet someone from Lewisville, Texas, just a few miles up the road from me, is tweeting his disappointment about being left out of (again I’m assuming) a beta program for the game company Blizzard Entertainment and their next incarnation of the Diablo game series. Yes, there is a weather-related keyword in that tweet, but would it not make sense to tie key words to geographical areas? The odds of having a blizzard in Texas (the weather kind, at least) are slim.

This is part of what makes text analytics so difficult. Business Objects purchased a company several years ago (Inxight) that delivers text analytics; this product is now a part of the Data Services product line. It would be interesting to see if they have a feature that would allow me to tie geo-location services to keywords so that I could discount tweets mentioning blizzards in Texas, or hurricanes in Alaska.

Ultimately it comes down to context. When I read those tweets, I can immediately see that they’re not really talking about the weather, even if they do have weather-related keywords. Apparently it’s still challenging for software to do the same thing. Then again, it’s hard enough to predict the weather correctly, so maybe I can forgive them a few errant tweets.

• Table Format
  • I have to include a header. If a table does not have a header, it doesn’t work. I could live with this, I guess.
  • Tables cannot include breaks. I think this qualifies as a “what were they thinking” moment. Seriously, no breaks? We also found out that sections are not supported.
  • Merged cells are not supported.
• Charts
  • Only basic chart types are supported. This is a real bummer, given that part of what is improved in BI 4.0 is the charting engine for Web Intelligence.
  • Formatting on chart elements such as axis lables is not supported.
• Document Interactivity
  • Drilling is not supported. See also limitations on the OpenDocument() function below.
  • Folding / unfolding is not supported.
  • Input controls are not supported. Another “wwtt” moment when I read that one.
  • The only conditional formatting that works are font colors. I have not tested this, but I assume that means no font sizes, no color changes (background / foreground colors), and no altering the cell content. I use that last feature quite a bit with Alerters to allow me to dynamically swap a new value into a block based on the data.
• OpenDocument()
  • URL components like sDocName, sPath, and sType are not supported. Some of these make sense, since Crystal reports are not yet supported on the iPad. But I’m not sure why the document name and path are not allowed.
• Prompting
  • Persitent prompts (retaining prior prompt values) is not supported.
  • Nested prompts are not supported.
  • Multi-column LOVs are not supported.

What does this mean? It means that even though I can distribute Web Intelligence reports to the iPad, there are quite a few important features that are not supported or don’t translate to the mobile platform. So while I did, in fact, see some Web Intelligence reports on an iPad, they did not include many of the features that I believe are essential for wide-spread use. I believe that Crystal is supposed to be next on the list for iPad support, but I hope they get some of these Web Intelligence gaps corrected soon as well.

Why In-Memory Analytics is Like Digital Photography: An Industry Transformation

Timo is an avid photographer as well as a BI evangelist, and in this post he combines his knowledge of both, making some excellent points along the way. It’s well worth hopping over to his blog to check it out.

iPod Evolution

The original iPod had a small hard drive (5GB) to store music files. However, even as small as it was, the hard drive (and the battery to keep it running) took most of the interior space of the case, leaving very little room for interface elements like buttons or a screen. As a result, the Apple engineers came up with the click wheel, an interface that provided all of the functions required to browse and play music. Later generations of the iPod were able to take advantage of better (and smaller) technologies and provide a color screen, larger hard drives (80GB), better battery life, and so on. But the framework was still restricted by the hard drive component. The color screen was still small, and the click wheel was still used to browse the library. Most functions were very easy, but searching was still painful due to the lack of a keyboard.

In 2007 the iPod became available in an 8GB “touch” model. The iPod Touch was very similar to the iPhone. It had a full-sized touch screen interface, meaning no more click wheel. How was this possible? The guts of the iPod Touch don’t contain a hard-drive. Instead the Touch relies on non-volatile memory for storage. Memory requires less physical space which allows for more flexibility in the overall system design. Memory requires less power which allows the battery to be smaller but still provide lengthy life. Memory does not suffer from physical abuse or accidents such as drops, making the device more reliable. And with the full-sized touch screen, the iPod Touch could do more than play music, it opened up an entirely new world of games and other applications. The trade off? Only 8GB of space. My iPod Classic has 160GB of disk space for music and other data files.

Today the iPod Touch can be ordered with up to 64GB of storage. It includes a high resolution camera, can take high-definition video, and runs thousands upon thousands of games and other applications from the Apple store, very few of which were written by Apple engineers.

What does any of this have to do with SAP, and HANA specifically?

Memory Removes Restrictions

The iPod Touch (and its cousin the iPhone) would not exist without the in-memory storage to replace the bulky and battery-draining hard drive. By removing the hard drive, Apple opened the iPod up to all sorts of possibilities. In the beginning there weren’t many applications; Apple provided only a few to start with, and there was no developer kit for outside programmers to use. As Apple grew more comfortable with the framework they started to open up the devices for outsiders to play with. I’m sure they had no idea that Angry Birds was on the horizon.

HANA today for SAP is what the first generation iPod Touch was for Apple. There aren’t a lot of applications written specifically to take advantage of the architecture just yet, and SAP has not provided any sort of SDK. (HANA supports both MDX and SQL which means most standard database calls should already work.) But SAP is already working on porting their planning applications over to HANA, and more are sure to come. One of the customer testimonials at SAPPHIRE was from a company that apparently used the initial versions of the in-memory planning application on a test system, and they were blown away by the responsiveness of the system.

HANA tomorrow … is a 64GB iPod Touch? Memory capacity continues to increase. Processor power continues to increase. As HANA matures the expectation is that more and more applications will be written to take advantage of the speed and power of the in-memory architecture. Some of those apps will certainly be to support the ERP systems, but certainly some will be in the business intelligence space as well. One of the more lofty projections suggests that the entire concept of extracting data to a warehouse for reporting will become obsolete. Data warehouses came about because we needed to be able to run massive number-crunching reports without impacting the day-to-day operations of our base systems. If those base systems are running on HANA, the theory is there will be no impact. We’ll run reports on “live” data, at the same time we’re running our transactional systems. Think of how much simpler (and less costly) this could be. No more ETL. No more warehouse hardware. No more reporting off of day-old data. The entire IT infrastructure can collapse down to operational systems.

Wow.

We’re certainly a long way from tossing all of our warehouses aside and reporting off of real-time data. Apple took about ten years to get from the original iPod to the iPod Touch 64GB we see today; the first touch appeared roughly in the middle of that evolution. HANA has roots in TREX, a text search engine that SAP built back in the late 90’s. The first incarnation of the in-memory database came in 2005 with the release of the accelerated version of BW. That engine was a specific-purpose implementation of the in-memory engine. Today HANA is a “generic” version of the in-memory systems used to accelerate BW, suitable for plugging in behind the scenes anywhere a database is required.

But someday, a couple of people in a garage just might come up with the Angry Birds of HANA. That would be something to see.

As a technical guy myself, I tend to prefer the SAP Business Objects conference or SAP TechEd over SAPPHIRE, mostly because I find more technical content at those events. However, the Wednesday keynote address from Vishal Sikka and Hasso Plattner of SAP certainly gave me plenty to chew on from a technical perspective.

Vishal Sikka

Vishal kicked off the keynote talking about HANA, and continued that theme throughout his entire (long!) presentation. In a prior post about the conference I answered the question, “what is HANA, exactly?” very simply: HANA is a database. It can be presented in a number of different ways, but ultimately that’s the function that HANA provides. I don’t install HANA to provide new functionality. In order to do anything with it, I need what I have started calling “HANA Plus One” instead. The “plus one” can be Web Intelligence, Xcelsius, or any other query tool. It can also be application code. HANA is an accelerator or an enabler. With HANA I can do the same things I did before but much faster. Or quite possibly I can now do something I wasn’t able to do before because the process took too long. (True story: A very long time ago I was asked to optimize a daily report that was taking 20+ hours to run. By the time the report was finished it was too late. With a few report tweaks and one additional database index I got the report down to 20 seconds.)

Vishal also talked about a HANA system that would cost about a half-million dollars that was able to handle 460B (that’s a “B” as in billion) database records. He called it the “perfect non-disruptive technology” because it bolts onto the back end of BW or Business Objects or… whatever. Stuff that worked before still works, it just works faster. (HANA supports both MDX and SQL query languages.) His presentation continued with a lot of customer testimonials, such as one from Colgate-Palmolive. They use HANA for sales planning and profitability analysis. Processes that used to take well over an hour ran in 13 seconds. He also showed customer testimonials related to analyzing traffic for a taxi company in Japan and margin analysis for Infosys. There were probably a dozen or more testimonials in all; after a while I got bored and stopped taking notes because they were all variations on a theme.

At one point Vishal appeared to want to emphasize that HANA was not developed in a vacumm; they used partners whenever possible. He gave as an example the history of innovation with Intel to optimize CPU logic and memory management. Intel now has chips with 10 cores, 30MB of cache, and support up to 4TB of main memory. The partnership with Intel is a win-win. SAP gets a platform to sell more software, and Intel gets demand for top-line hardware.

Towards the end of his session Vishal mentioned that Adobe was going to be using a HANA-based system to evaluate what he called “unintended license usage” of their products. They didn’t share details about that, of course.

Hasso Plattner

Hasso then took the stage, after Vishal had run well over his allocated time. Hasso was quite the comedian! He commented that since Vishal took much of his time, he would have to present everything in accelerated fashion, and coined the term “HANA Speak” followed by a bunch of gibberish that sounded like the Chipmunks after espresso. I saw more than one tweet related to that joke.

Hasso took some prearranged questions via video clips. One that I was the most interested in (and therefore took notes about) was related to how reliable could a HANA-based system be, considering it’s all based on RAM. Hasso had a very good answer… he basically said why should we care. The concept of caching is not new. I remember buying products years ago that made use of my personal computer RAM to cache commonly accessed information. Today my CPU has a cache, my hard drive has a cache, my video card has cache, heck I would not be surprised if my cache has a cache! Hasso’s point was that all systems today will cache data, but we’re relying on the database to be smart enough to manage that process. HANA doesn’t try to be smart. Instead it takes the brute-force approach and caches everything.

When my Oracle or Teradata (or any other) system is power cycled, the cache starts out empty and has to be reloaded. If a HANA system is powered off and back on again, the cache also has to be reloaded. It’s just a bigger cache.

Hasso then proceeded to show three different HANA systems. The first was HANA running on a Mac Mini (he called it a Mini-Mac, which inspired several folks around me to make McDonalds references). By the way, don’t bother looking for the Mac Mini on the hardware support list. It runs a custom port of the code which is not (to my knowledge) commercially available. But Hasso wanted to talk about the fact that entire companies were running on this system. Imagine being able to run your entire company application suite on hardware that small! The next system Hasso showed was a rack with some pizza boxes (blades) which is probably the more typical configuration. Finally, we went live to a data center that showed (if my memory is correct) a 1,000 CPU system. Impressive stuff, all done to show the scalability of HANA.

Keep in mind what I said earlier though… HANA isn’t a product that helps you make better business decisions, build prettier graphs, or deliver mobile solutions. It is an accelerator / enabler for all of these. It’s a database. Along those lines, SAP is taking steps to be able to run their ERP on top of HANA. The first module I heard about for this was their planning application. As Hasso put it, planning is all about seeing the future. HANA gives you brighter headlights so you can see farther and drive faster.

One final moment of amusement came when Hasso was looking at one of his slides and describing the various elements, and could not remember what the “ASIS” acronym stood for. Then he remembered that he was speaking in English and was able to put “as-is applications” into context. That goes back to Vishal’s comment about HANA being non-disruptive. In a theoretical situation, I don’t have to change a single thing about my business processes, reporting, or mobility solutions in order to use HANA. Anything currently in place will continue to run as it currently stands (as is). Nice idea in theory, of course, but I’m sure that in practice there might be one or two things that need tweaking.

Conclusion

As I mentioned previously, the three hot items for the conference this year were mobility, cloud, and in-memory computing. Mobile devices require fast response times or they’re not useful. I can’t sit at a client site for 30 minutes waiting for a report to run or an analytic to refresh, I need it now. HANA helps me get there, and SAP is obviously very proud of their technology. I expect to hear (and see) quite a bit about this over the coming years. Perhaps even to the point where we can paraphrase Scotty (from Star Trek IV) and say:

Hard disks, how quaint.

What I liked about slide I got was that it was based more on use rather than function. In other words, it didn’t focus on the features of the products but instead asked what was going to be done with the products. The first decision point in the flowchart was simple: is the user request for delivery or discovery?

What does that mean, and what products ended up on either side?

Information Delivery

Some products are all about the delivery process. Take Crystal, for example. For over a decade it has been one of the premier tools available for taking information and delivering it to the masses. Someone once told me that my utility bill, phone bill, bank statement (does anybody still get those anymore? or are they all online?) were most likely prepared and sent via Crystal. There are some interactive options with Crystal, but I think most folks would agree that this product is solidly on the delivery side of the equation. Here’s the flowchart path for Crystal:

Objective: Information Delivery
Type of View: Operational Report
Presentation: Formatted
Result: Crystal Reports

I found it interesting that the SAP slide included Xcelsius on the delivery side. Xcelsius is often presented as an interactive “what if” tool, which might lead to the conclusion that it’s a discovery tool. It’s really not, at least not in my opinion. I laugh a little bit inside when I see some of the demonstrations of the tool. Really, how smart to you have to be to know that if you reduce costs your profit is going to go up? Unfortunately, reducing costs is never as simple as moving a little slider control to the left. Users cannot discover anything beyond what is delivered on the dashboard. Here’s the flowchart path for Xcelsius:

Objective: Information Delivery
Type of View: Business Analytics / KPIs
Result: Xcelsius

Web Intelligence also shows up on the delivery side of the chart. I’ve certainly worked on more than one project where this tool was used to create and then distribute enterprise reports. I think most folks would agree that Web Intelligence scores far higher than Crystal Reports on the interactivity scale. Web Intelligence does suffer when compared to Crystal as far as analytical functions, and certainly falls short of the “pixel perfect” formatting that Crystal offers. But over the years Web Intelligence has certainly become an excellent product. Here’s the flowchart for Web Intelligence:

Objective: Information Delivery
Type of View: Operational Report
Presentation: Interactive
Result: Web Intelligence.

For those of you on the Desktop Intelligence “death watch” now is probably a good time to mention that it doesn’t show up anywhere on this chart. It doesn’t show up on any of the road maps for XI 4.0 either… make of that what you will.

Information Discovery

Now I move to the other side of the process: discovery. What tools are presented here?

I’ve been posting a bit about exploring Explorer via the OnDemand site. I would have to say that Explorer is the epitome of discovery; that’s exactly what it was built for. Explorer allows me to set up a data set that includes as much information as I can stand (which can be quite a lot, with the accelerated version) and poke my way through it. Here’s the flowchart path for Explorer:

Objective: Information Discovery
Type of Discovery: Unbound
Result: Explorer

But what if I don’t want to (or can’t afford to) let my business users access everything that I have? There are more tools that allow me to “discover” my data without being completely unbound. For example, BEx Analyzer / Voyager. These are true OLAP tools. (I say “true” because Web Intelligence has been presented as “desktop OLAP” in the past, and BusinessObjects will talk about their report cubes, but it’s not really true OLAP in my opinion.) BEx Analyzer is a legacy SAP product, and Voyager is from BusinessObjects. SAP has frequently talked about taking the best of both products and releasing a new OLAP tool named Pioneer with XI 4.0. How do I get here on the flowchart?

Objective: Information Discovery
Type of Discovery: Defined scope of analysis
Type of Analysis: Advanced
Source: SAP BW Result BEx Analyzer
Source: Non BW Result Voyager
Future: Pioneer

These products suffer some when compared to other tools because they can only take me as far as the cube goes. If I need more data, I either need to rebuild (extend) by cube, or get data somewhere else. SAP is making excellent strides in the “get data somewhere else” category with XI 4. Of course I have not seen anything yet, so plans (feature sets, release dates, and so on) are still subject to change. But the intent is to allow me to start out in Pioneer and seamlessly transition to Web Intelligence once I get to the boundaries of my cube. And speaking of Web Intelligence…

Web Intelligence also appears on the Discovery side of the chart. Here’s the flowchart path:

Objective: Information Discovery
Type of Discovery: Defined scope of analysis
Type of Analysis: Casual
Result: Web Intelligence

How about that? Web Intelligence is being presented as both a Delivery and a Discovery tool, and I will say that I do agree with that. It can be used as a delivery path (along with Crystal and Xcelsius) but can also be used as a discovery option, given the interactive drilling features and easy adhoc abilities of the tool.

Conclusion

SAP inherited a lot of different tools when they acquired BusinessObjects. They have made tremendous progress in the past two years in consolidating and targeting those tools to specific uses. The flowchart attached to this post does a nice job of summarizing those purposes. With XI 4 coming very soon, we should start to see easier and better integration between products (being able to jump from Explorer to Web Intelligence, for example). I’m started to get more excited to see what we’re going to get and how it all pulls together.

Related Links

• Pick the Right SAP BusinessObjects Business Intelligence Tool, present for ASUG by Ingo Hilgefort
• SAP BI Decision Tree (Flowchart) as a PowerPoint slide (794K zipped)

Merge – Join

As I mentioned, the first thing I wanted to do after my initial exploration of my data was compare prior year to current year results. I could not do that (or figure out a way to do that) because of the way I uploaded my data. I had sent a data set that looked like this:

In Web Intelligence I would have created a crosstab report with the year on the top and the period down the left side and the cases (my measure) in the middle. Explorer does not seem to work that way. So what I did was split my file into two parts. The first part contained 2008 data and the second part contained my 2009 data. Instead of one file I had two files like this:

2008 file

2009 file

Once I uploaded the two files I started figuring out how to merge the data.

The menu option is quite easy to find. You can also right-click on a data set and select “Combine” from the context menu that pops up. However, there are some restrictions that are not so obvious. Once I selected my two sets I was presented with a screen showing the merged data. It turns out that the OnDemand site merges data sets by finding matching column names. Since both of my data sets came from the same source, that wasn’t a problem. I did, however, have to rename my measure column from “Quantity” to “2008 Quantity” and “2009 Quantity” in order to be able to tell the difference.

How did I know this was how my sets were going to be merged? There was a “more” link on the page. When I clicked it I got this information.

Any changes you make to the two source datasets are not saved back to those datasets; the changes are only propagated into the combined dataset.

The join occurs on columns that have the same name. The maximum size for a created dataset is 20,000 rows and 256 columns.

When you have finished joining the data, press “Next” to continue to the next step of the wizard where you can finalize how the new dataset will appear.

The changes mentioned in the first point related to the fact that I can change column names during the merge process. My source data sets are not changed, only the merged file contains the changes. I’m fine with that. The second point told me how the join was going to be performed, and also let me know that my combined set was going to be limited to 20,000 rows. So as I mentioned last time, there are limits to the free service but they do not appear to be documented anywhere so I could plan ahead. It turns out that my initial uploads were also silently truncated to 20,000 rows as well.

I went ahead with the merge. I was curious if they would tell me how many rows were being truncated, but they did not. After the merge was complete I had a data set with all of my facets (dimensions) and two measures. I was easily able to create a Variance variable on my data set by subtracting 2008 from 2009. Ultimately I was able to create a visualization showing the trend over periods including both 2008 and 2009 data, along with my variance. Here is what the merged data set looked like:

Merge – Union

Now that I knew how to get 2008 + 2009 data together I wanted to see if I could combine two business units. In this case I would not want new columns added as I did in the prior example, I wanted to see more rows. It turned out to be quite simple. I already had two years of data for one business unit uploaded and merged. I followed the same process for the second. Now I was ready to create a union between the two.

All I had to do was select the same “combine” option and this time make sure that all of the column names were the same, even my quantities (measures). Explorer appeared to merge the sets using a union operation, which was exactly what I wanted.

I guess now is a good time to point out that some of the features I am talking about are features of the OnDemand.com service, rather than the Explorer product itself. You or your company can opt to use the service online (via the web site) or you can license the entire system (Explorer plus the OnDemand portal interface) for internal use.

Need “Sticky” Settings

I am now able to generate year-over-year comparison charts from multiple different business units (data sources). However, there are still some (not so) sticky points to deal with.

The data I uploaded included the fiscal calendar period number. We use 13 fiscal periods so the numbers range (obviously) from 1 to 13. When I picked the period facet as part of my analysis, Explorer showed me the top 10 + a category for “other” instead of showing all 13 periods. I was eventually able to figure out that I could configure the system to show all 13 periods. I was also able to get them to sort chronologically rather than from “best” to “worst” order by quantity. It wasn’t hard.

It also wasn’t sticky.

What do I mean by that? As I played around with (explored) my data I swapped things in and out. When I removed period and then later brought it back it had forgotten all of the settings I had changed. I was back to 10 periods + other instead. I can see where this would be okay for a lot of things, but it would be nice to be able to make changes to a specific item and have them always come up that way by default.

Story So Far…

Normally I would call the final section of my post “Conclusion” but I am far from that point. While I have been a bit frustrated by the limits of the trial (free) service I can certainly see the potential of the OnDemand framework. Explorer is the core of the system, of course, but they have added a front-end interface that is extremely easy to work with. We have been trying to install Explorer internally to prototype some solutions, but have encountered a number of isssues with the installation. By using OnDemand.com I was able to – in a matter of a few days – upload some data and create some Explorer data sets and then build some visualizations for our review.

I wish they published the limitations for the trial membership somewhere. I definitely would have reduced the complexity of my data in order to avoid getting cut off at 20,000 rows. Fortunately the OnDemand site offers a Feedback link that takes you to a new site where you can make suggestions on how to improve the service as well as vote on existing ideas. I have entered this as a suggestion. I also entered the idea of making changes “sticky” as mentioned above.

I still want to get Explorer up and running internally. But OnDemand.com was certainly easy enough to work with, and it allowed me to get my demonstration up and running well within my time constraints.

I first looked all over the site to find out what the restrictions are. Since I was going to be using a free trial account, I didn’t expect to be able to upload my entire two billion row fact table, nor would I want to. However, I did want to stretch the system to show of what Explorer can do. I could not find anything, anywhere, that detailed the specific limits of the free account. I even tried to find out what pricing would be in the event that I decided to host more data, and I could not find that anywhere either. If anyone finds that information online, please post a link.

Other than these site issues, how did my initial exploration go?

Upload Limits

I created an extract with two years of data from one of my fact tables. I denormalized (flattened) the data to get it into a single CSV file, which I then attempted to upload to the OnDemand service. After the upload completed I found out what I was looking for before… it seems there is a 3MB limit on upload file sizes. So at least I was able to cross that question off of my list. It took me a while to decide what to drop out of my extract to get down to that limit, but finally I got a 2.9MB file that I was able to upload and save.

Of course the first thing I tried to do after that was open Explorer. The site gave me an error, so I logged out and logged back in. Still got the error. At this point I had to move on to something else so I tabled my efforts and came back the next day. That time Explorer launched just fine.

Initial Exploration

The data set I uploaded included one measure, two time facets (Year and Period), two geographical facets (Region and Area), two product facets (Brand and Line), and three location facets (Category, Channel, and Segment). Explorer uses the term “Facet” to describe a collection of values. I uploaded approximately 30,000 rows of data.

As I mentioned above, one of the selling points for Explorer is that there are no training classes for the product. It’s supposed to be point-click-insight easy. I failed the test. The first thing I wanted to do was compare current year to prior year results. I figured out how to make my graph show a trend from last year to current year, but only if I included the year on the graph. What I wanted to be able to do was show period or region or product comparison of sales from last year to this year. With the data I uploaded I was not able to do that. I am going to go back and separate out my files by year, so I have 2008 and 2009 data as separate uploads. My thinking is that by doing two uploads I will have two measures, and that way can compare the two years. I will report back on that after I get a chance to try that experiment.

Other Frustrations

One of the data points I uploaded was Period. We have 13 periods in our fiscal calendar. Explorer tried showing me the “top ten” periods sorted by my measure, and then grouped the remaining periods as “Other” with an aggregate total. This was not what I wanted. I figured out how to change the sort to chronological order, and I also figured out how to get Explorer to show me 13 instead of 10 values + Other. But as I continued to explore my data it went back to showing only ten periods. I could not figure out how to make my change “sticky” so that it would retain my settings.

Along the way I clicked the “Create Visualization” button a couple of times. Later on when I tried to remove my data set, I was presented with an error message that said my data set could not be deleted. I figured out that my data set was protected because of the visualizations that I had generated, so I switched to the proper tab and removed all of my visualizations. Problem solved? Not quite. I had to find the “filters” that were used to drive the visualizations, and remove them too. I could not figure out a way to remove them both at once, so I had to remove them one at a time. Again, there may be a way to clean things up easier but I didn’t find it right away.

Ultimately I was able to remove my data set. It would have been nice to be presented with a list of object dependencies and asked if I wanted to delete my data set and cascade the delete to all dependent objects. Instead I had to clean up manually.

Exploration Results

After my initial attempt I’m not ready to rush out and do a demonstration for any executives just yet. I need to learn more about the proper way to model data for Explorer (assuming that there is such a thing). I need to understand more about how I can configure / control certain aspects of the interface, such as what I outlined for Period above. I may have to even <gasp> try to find some documentation in order to find out how to implement some of the analysis I want to make.

Stay tuned for more details.