I know this will sound a little strange, but GEOINT Data is not really data in the strict definition of the term. GEOINT Data are extracted chunks of information from other intelligence and geographic products such as imagery and maps. Let's examine this statement by first viewing a few definitions, and then we'll explore this as an analogy of baking a cake. The definitions of data, information, and insights are:
- Data: Facts and statistics collected together for reference or analysis
(Source: Oxford Dictionaries: Data).
- Information: What is conveyed or represented by a particular arrangement or sequence of things
(Source: Oxford Dictionaries: Information).
- Insight: The capacity to gain an accurate and deep intuitive understanding of a person or thing
(Source: Oxford Dictionaries: Insight).
Now for our cake baking analogy:
A chef has a request to bake a carrot cake. The carrots were previously picked from the ground, cleaned, packaged, and transported to the grocery store. The carrots are purchased by a chef at the store to be shredded, mixed with other ingredients, and baked into the cake. The carrots could also be sliced for a salad. It is the chef's knowledge, skill, and wisdom that determines how well the ingredients are made into a cake.
The analogy is that GEOINT data are previously collected (picked), inconsistencies removed (cleaned), structured (packaged), and distributed (transported). The analyst acquires the data from a warehouse (grocery store), subsets the data (shreds the carrots), and combines (mixes) it with other information to create insights (the cake). The same data might be used for other analytic purposes (making a salad). It is the analyst's (bakers) knowledge, skill, and wisdom that determines how well the information is converted into insights.
Satellite imagery is one of the key ingredients of GEOINT. A satellite image is processed data. A satellite image is made up of raw data (thousands of pixels) that are arranged in a particular way. This raw data is processed, organized, structured, and presented so as to make it useful as a satellite image. Importantly, this satellite imagery can then be used to create other data. For example, the height of a particular building extracted from the imagery is new data.
Mapmaking uses data in the creation of information and has been an integral part of human history for possibly up to 8,000 years. Maps and imagery are representations of the Earth. Before remote sensing technologies, if we wanted to know something about a location on the Earth, we would have had to visit the location. In those days, our knowledge was limited by our direct experiences. Information, such as maps and satellite imagery, allows us to expand our insights beyond the range of our experiences. Information can be shared and used by others at different times and places.
Primary data is raw information collected for a specific purpose. For example, the direct measurement of a building's height would be primary data. Secondary data are extracted from information developed by others. The advantage of primary data is the opportunity to tailor it to our need. We "know" the data. The disadvantage of collecting primary data is that it is costly and time consuming. The main advantages of secondary data are that it can be quicker and less expensive. It is easier to examine information, such as imagery, collected over a long period of time to identify changes. However, the information may be outdated, or inaccurate, or too vague. This is to say, we might not "know" the data and be able to fully articulate our insights. Figure 2.10 below illustrates this data-information-insights process.
People create data as a means to help understand how natural and human systems work. Such systems can be hard to analyze because they're made up of many interacting phenomena that are often difficult to observe directly, plus they tend to change over time. We attempt to make systems and phenomena easier to study by measuring their characteristics at certain times. We measure selectively because it's not practical to measure everything, everywhere, all the time. How accurately data reflect the phenomena they represent depends upon how, when, where, and what aspects of the phenomena were measured. Thus, all measurements contain a certain amount of error that the wisdom of the analyst must take into account.