One More Input to the Nonprofit Planning System

I said in the prior post that two knowledge artifacts are required to run the Grant Generator: the Business Plan Tree, which provides the content, and the Foundation Sequencing Table, which provides the assembly instructions. There is one other input, a community asset that I build and maintain along with the Sequencing Table, which I call the Nonprofit Element Attribute table.

In the process of building the Nonprofit Planner and Grant Generator, there were certain design and formatting decisions that were left to me as the knowledge author. One was the rules for when to use the green 'completeness' highlighting in the business plan, and the other was how to format the output in the grant application.

First, I'll explain the completeness rules. Remember, in KnowtPlan, when an item is completed it is outlined in green in the note view and highlighted in green in the outline view.

The rule for when to apply the highlighting to the 'leaves' of the tree is simple: when both the short and long answer are filled in, add the green outline. So the completeness rule for 'Organization's Mission Statement' is 'self'; it is highlighted as soon as its 'self' is complete.

The rules for how to roll the highlighting up to higher levels in the tree are somewhat more complicated. That is because some of the higher level notes require their own answers and some are just headers added for organization purposes.

An example of a note that is just a header is 'SWOT Analysis'. It has no content; if you clicked on it, you would not get a popup window asking for a long and short answer. So its completeness rule is 'children'; it is highlighted as soon as all of its children are highlighted. In the screenshot above, SWOT is not highlighted because two of its attached notes have not been filled in yet.

On the other hand, 'Organization's Purpose' is a note that acts as a header but also requires its own content. The charity is expected to fill in the organization's purpose, which should be short and compelling - different from a mission statement, which can be rather wordy. So the completeness rule for 'Organization's Purpose' is 'both': it is considered complete when all of its children are completed AND its own content is filled in.

So one piece of information I have to provide is the completeness rule for every item in the plan. I do this by adding an attribute called 'completeness' and setting its value to 'self', 'children' or 'both'. A second attribute I need to provide for each item is how it should be formatted in the grant application that is generated. This is similar to assigning a paragraph style in Microsoft Word.

Even though we ask for a 'long' and 'short' answer for each question, it is clear from working with the actual content that some answers tend to be much longer than others. For example, contact info tends to be very short. Descriptions of the Board members, on the other hand, tend to be much longer.

So we created two different paragraph styles: 'inline' puts the question header on the same line as the response pulled from the business plan; 'paragraph' puts the header on a separate line. There are also headers, the ones that have no content of their own, that never show up in the generated grant application. Their style is set to 'none'. This became a second attribute.

I'm sure there will be other attributes that will surface as we continue to develop the Nonprofit system. To make it easy to expand and maintain this sort of information, we capture it in a table that is easy for me to change, and doesn't require changes in computer code. The current version of the Nonprofit Element Attribute Table looks like this:

Adding Images to the Whiskey Recommender

While images aren't required for a Recommender app, there is a place for a picture in the 'baseball card' template, just as you would expect for any baseball card. Here is an example of the template, as it is used in the Beach Town Recommender:

I want to add images to the Whiskey Recommender, but this is more difficult than it was for beach towns. The challenge arises, once again, because I am working with a much larger set of possible options -- 450 different whiskey brands!

The Beach Town Recommender has about 30 towns in it, so it was relatively easy to search the internet for good medium-sized images of my beach towns and save them to a folder on my computer. I won't be able to do that for 450 whiskeys; it's just too much work. I'll have to find different images for different types of whiskeys, and stop there.

The basic process I used was to (1) locate images using a search engine, and then (2) save those images to a folder using a simple, consistent naming convention. Then I added a "Picture" column to my data table and (3) entered the file name of the image I wanted to display for that particular whiskey.

I started out with a nice, generic picture of whiskey that I could use for my default image. This is the one I selected:

I named this 'whiskey.jpg' and I copied that name into the entire "Picture" column in my data table. I would copy and paste over this name if I found a better/more specific picture.

Now I moved on to my top level classes in my classification tree: American, Scotch, Canadian and Irish. What sort of images could act as generic images for these high level classes?

I learned after doing a bit of image research, which I did in the image section of both Google and Bing, that there were pictures available that showed groups of whiskey bottles, selected to represent different types of whiskeys. Here's an example:

This is the image I decided to use for 'American'. Someone else had already created this for me -- I could see that different bottles had been cut and pasted into the image -- but that's okay, it saved me some time. What I liked about this picture is that it had a selection of Tennessee Whiskey, Kentucky Straight Bourbon and Rye Whiskeys, making it a snapshot of a variety of American types and brands. I found similar images for Canadian, Irish and Scotch. At this point, rather than copying and pasting image names into my data table, I decided to hold off and see what other pictures I could find.

I was able to find similar multi-bottle pictures for several sub-classes: American rye, Kentucky bourbon, Tennessee whiskey, single malt scotch, and blended scotch. I would have liked to have pictures for the other sub-classes as well, like 'American blended' or 'American corn', but I couldn't find anything appropriate. So I used the generic 'American' image for those types, just as I had to settle for only one image for all of the Canadian whiskeys. At this point, I copied the names of the most specific image I had available into the appropriate cells in my "Picture" column.

Now I assessed the situation. I realized that when someone used the Whiskey Recommender, if they were directed to a particular type of whiskey, like single malt scotch, they would see 20+ 'baseball cards' -- all with different information on them but with the same picture, this one:

That's a little too boring, even for a demo! So I knew I needed to push one more level down in the data, if I could.

There were a couple of different options I could pursue. In single malt scotch I had another column in my data table: "Region". I could look for images that went with Speyside, Highlands, Islay, etc. But the other possibility was to look for images that went with the "Company" dimension, images for Diageo, Suntory/Beam, Sazerac, and the like. I decided to look for company-specific images, because that could add visual diversity to all of the whiskey types, not just single malt scotch.

I was able to find images for Beam Irish, Beam American, Forty Creek Canadian, Sazerac Canadian, Diageo Scotch and Pernod Ricard Scotch. Here is the image for Pernod Ricard Scotch:

After adding these images to my image folder, I copied and pasted the image names into the appropriate cells of my data table. I would love to have a picture of every specific whiskey brand, but for this particular demo, I decided to stop at this point. The Whiskey Data Table -- one of the two knowledge artifacts required to create a Recommender app -- was complete.

When I build the Whiskey Recommender using the Recommender Apperator, I will need to upload my folder of images along with my Whiskey Data Table and Context Decision Tree so that the pictures can appear in my web app.

One final comment about finding appropriate images: I always use my search engine's tools to limit the images I am shown to medium-sized files. That is big enough to provide good resolution in my web app, while saving data storage and reducing load times during execution.

Now I need to move on to creating the final 'knowtifact' that is required to generate a Recommender: the Context Decision Tree.

Adding Taste Attributes to the Whiskey Data Table

Now that I had a price level attribute in the table (see my last post) I wanted to add something about taste. I wasn't sure what information would be available about taste. There are plenty of qualitative descriptions on the web, describing particular types of whiskeys, but I wanted a single source that covered most of the whiskeys in my database. I found this very helpful graphic:

This picture reminds me a lot of the poster I used as my original knowledge source. It calls out the same four high level classes: Canadian, American, Irish and Scottish. It also has many of the sub-classes but not all of them. For example, it has some of the Scottish single malt regions, but not Campbeltown and Island. And there is no single malt listed under American whiskey, though there are several in my database.

This chart uses what is called a 'radar chart' to depict its information.

There are eighteen axes coming out of the center that show the whiskey's rating on 18 different taste dimensions: sweet, smoky, grainy, vanilla, honey, spicy, briny, malty, cocoa, buttery, toffee, fruit, bacon fat, oaky, caramel, corny, biscuity, and peaty. A given whiskey can have one of three scores on each of these dimensions: 0, 1 or 2. I decided to change these numbers into written descriptions. If a whiskey scored a zero, that flavor is not mentioned at all. If it scores a '1', the flavor is mentioned, and if it scores a '2' I added the word "very". So single malt scotch from Speyside could be described as "very sweet, very honey, very fuity, very oaky, grainy, cocoa, buttery and caramel."

Once again, judgment now plays an important part. While I love this graphic, I think most people's taste buds are not refined enough to taste the subtleties conveyed here. Most of us would be lucky to perceive the "very" strong flavors, let alone the secondary mentions. So I made the decision to simplify even further and only mention the flavors that were strongly associated with the whiskey, the "very" flavors.

I had to decide how to record this information for my data table, because that will have an impact on how it is displayed in the 'baseball card' shown in the final recommendation. I could make a column for each of the 18 flavors and record the score -- 0, 1 or 2 -- but that doesn't seem very user friendly. There would be eighteen attributes listed for each whiskey, many of which will not be applicable. Instead I decided to create a group of columns and label them 'taste1', 'taste2' and so on, and fill in the strong flavors associated with that whiskey. My data table now looks like this:

The number of flavors ranges from a high of seven for Highland single malt scotch to a low of one for several types of American whiskey. I'm okay with this; I think this reflects the differing complexities of different types of whiskey. People aren't buying American corn whiskey -- "moonshine" -- for its sophistication. My only real problem is the fact that I have several whiskey types with no taste information at all, because they are not listed in my taste knowledge source.

To fill in the blanks, I did further research. I found several sources that had taste profiles for both Island and Campbeltown single malts, so I filled those in, using the terminology I had already established.

The real challenge was American single malts. I found a New York Times article that talked about how this is an up-and-coming category, so it is important to have good data, but it also said this is such a diverse group it defies being summarized with a single taste profile. So I did what I have been resisting so far in this project: I created individual taste profiles for each whiskey.

I was able to find reviews for each American single malt in my database, and I used these reviews to create a taste profile containing my established terminology. Once again, if we were crowd-sourcing the data, or if this project were being funded by a customer, this individualized approach could be used for all of the whiskeys in the database, but this is a demo application that I am building to illustrate the process of building a Recommender, so I am going to keep things as simple as possible. There is a similar dynamic tension between effort and completeness in the final step of data preparation: adding images. That will be the topic of my next post.

Adding the Price Attribute to the Whiskey Table

My next challenge in building the Whiskey Recommender is to add attributes to the data table that will become the core of the information shown on the 'baseball card' for the recommended whiskies.

I'm going to start with 'price'. I already know I won't be adding actual prices to the table because I've decided to stop my decomposition process at the 'brand' level. Since all the variants of a brand, including bottle size, will have a significant impact on the actual price I need to aggregate that pricing into something more conceptual, like 'price level'.

I start by searching the internet for a downloadable source of whiskey prices. I found one pretty close to home: I live in Northern Virginia, and the liquor stores are state run, or at least state controlled. I found a downloadable file with all the current liquor prices. It looks like this:

This type of file is called a "csv" file, which stands for 'comma separated values'. The first row in a csv file usually contains a description of the data that follows. In this case, I learned that each row in the table contains seven items of information: the description of the item, an identifying code of some sort, the brand, the bottle size, the age of the liquor, its proof, and price.

It's pretty simple to import this kind of file into Excel, which will create a table using the first row for column headings and placing the data in the correct cells beneath.

The next thing I did is eliminate all the rows that did not contain whiskey information. The VA liquor stores sell many different alcoholic products and I needed to pull the irrelevant ones out of the analysis. I also eliminated columns that weren't relevant to my analysis: code, age and proof. If I were decomposing my whiskey brands to a lower level, like different ages, I would need this information, but since I have made the decision to aggregate all the variants into a single price level I can eliminate these columns. Now I was down to description, brand, bottle size and price.

One variant I can't ignore is the impact of bottle size.To make sure I am aggregating 'apples to apples' prices, I need to average prices for one bottle size only; this is called 'controlling' for bottle size. I notice by looking at the data that 750 ml is the most common bottle size, so I used the Excel data functions to filter the data and keep only the rows for 750 ml bottles. This is now the data set I will use for my analysis.

I then used Excel's average function to calculate averages for each description. For example, the data shown above would be included in the 'scotch' average. It's clear from inspection that there are some outliers  -- some bottles are very expensive! But our hope, as data analysts, is that the existence of outliers is spread out across different categories, and if there are clusters of them (like in scotch) that is a fair representation of the relative price for that category. Remember, we are trying to calculate a price rating of some sort, not an actual price. Scotch, and in particular, single malt scotch, is relatively expensive.

Once I had calculated an average for each description, I created a 5 point scale and a price range for each, with "5" being the most expensive. I used the description to map as closely as possible into the classification structure I had created in my data table and entered the price rating. To keep with our scotch example, I set the price level for every scotch in the table to "4". Now is where judgment comes in.

If I were building this application for a client, I would take the time to breakout sub-classes like 'single malt' in the Virginia price list so I could actually calculate the average price for that group. But since I am building this app as a demonstration piece, I was comfortable with sampling the single malts (I mean statistically sample, not literally - though I have done some of that in the course of my research) and giving single malt scotch a '5' on price level.

As a final pass of the data, I looked for brands that were priced significantly lower than other brands in their cohort. Different companies will have portfolios of brands in which one is used as their entry price point and is priced lower than the rest. If I can call those out without too much additional effort, I will. So I gave Dewar's a '3' and a scotch called Passport a '2'. I'm not sure that I caught all of these special cases, because the Virginia liquor stores don't sell every whiskey that's in my database, but I did the best I could with the data that is readily available.

Here's a sample of the whiskey data table with the price attribute added:

Notice Speyburn is a single malt scotch that I gave a '3' price rating, based on the actual price being significantly lower than other single malt scotches in the Virginia price list.