The menus in this particular Japanese restaurant were entirely in Japanese. English pronunciations were provided (e.g. “ten zaru soba”) but there was no other information about each entrée (I least, I think they were entrées). Since I don’t speak Japanese and I had no knowledge of Japanese cuisine, it seemed all too easy to accidentally order a dish made with something famously-disgusting to western palettes like sea-urchin or even potentially life-threatening like poisonous pufferfish. The waitress seemed helpful enough to tolerate maybe one or two questions from her ignorant customer, but the question “what do you think I want to eat?” didn’t seem like it would get me good results.

But as the waitress approached, I noticed that the menu was organized into categories. It didn’t matter that the headings for each group of dishes were just as impenetrable to me as the names of the entrées, because now I knew at least one thing that I didn’t know.

“What is this?” I asked pointing to a heading.

“Noodles.”

And under the noodle group, I suddenly observed that the English-alphabet pronunciation of lot of the dishes ended in the word “soba”. It was like there was a sub-category of noodles that had some kind of soba-like nature. Another thing I now knew that I didn’t know.

“What does soba mean?”

“Those are buckwheat noodles.”

I ordered something called “ten zaru soba” and was very pleased with the result delivered from the kitchen.

It may already be obvious how this anecdote relates to DAC’s analytic products and technology, but I’ll hammer the point home anyway. In cases where the data is overwhelming unfamiliar (as in the Japanese restaurant), or overwhelmingly large, as it is in intelligence data sets for country-sized regions, even a small revelation about the structure or categorization of the data can go a long way. And DAC’s technology aggressively reveals both large and small aspects of the organization and structure of even the most eclectic data. And it does so on large and small scales and with adjustable levels of fidelity. DAC’s tools and technology make it possible to answer the vague but important questions like “What is all of this data about?” and “What kinds of things could I search for?”