In previous videos we explored the information action value chain, which starts with events in the real world and ends with an action in the marketplace. In this video we start diving into that value chain in more detail beginning with events in the real world. It turns out that just about anything in the real world can be turned into data. Technology is continually allowing us to push the envelope, both in terms of the types and information that can be captured and the sheer volume of data that can be captured and stored. We couldn't possibly cover everything that might be available to you with the analyst, but what we will do is outline some of the major types of real-world phenomena that might be of interest to you and your business. We'll break our discussion into two parts. First, we'll talk about people. Who they are and what they do. Then we'll talk about things, objects, and the environment. Let's start with people. People have characteristics that describe them, like age, gender, nationality, ethnicity, race, marital and familial status. Educational level, socioeconomic status, housing status. The list goes on and on. People also have preferences, beliefs, attitudes and motivations that help define who they are. They may not be obvious or easy to get, but they do exist and there are methods of capturing information about them. We often group these characteristics into a few broad categories that you might encounter in a business context. Namely demographics, psychographics and technographics. Demographics broadly describe population level characteristics like age, gender, nationality, etc. And are the most widely used characteristics in a lot of different types of analysis. Psychographics speak more to people's opinions, attitudes, and interests. They include preferences, likes and dislikes, and tend to reveal insights about why people do the things they do. Technographics are really a subset of psychographics which focus on how people approach technology and what their motivations and attitudes are about using new and existing technologies. Certainly there are other categories of attributes. In some areas, like healthcare, the notion of personal attributes can go a lot deeper to include a whole host of physical attributes that might be important to a business organization. Some of these characteristics also imply events. Age implies birthdays, marital status might imply a wedding or anniversary, education implies graduations. All of these related events could be of interest to the business and to the analyst. In addition to characteristics, people also have identifiers. They have names, addresses, telephone numbers, email addresses, Facebook and Twitter handles. And all sorts of unique attributes that might be used to identify them in the real world. Let's shift slightly from people themselves to where those people are and where they go. We can think about this in at least two ways. First, we can think about the idea of physical location. Where people live, how they move, and where they are at any given time. People commute, they travel. And they have natural patterns of movement in their day-to-day lives. We can also think about location in the virtual sense. People can navigate through the online environment via web browsing. They can frequent certain websites or be present at a given time at an online location. Of course, they are also accessing the web from some physical location as well. And thanks to mobile connectivity, they may even be moving while doing it. In many industries, some of the most important and frequently used information is around transaction or events that involve an exchange between people or businesses. Far and away, the most common transaction of interest in business analytics is a purchase, the event where someone buys a product or service that our company is selling. But there are a wide array of other types of transactions, like investments, transfers, execution of contracts, accounting entries. Or more detailed elements of purchase transactions, like placed orders or payment processing events. A closely related idea is that of consumption, or usage. Often how people use our products and services, or how much of them they consume, is equally, if not more, important than the buying of those products and services themselves. Many of these examples are in telecommunications and utilities, where we are interested in things like minutes of use or megabyte or data consumption, or electricity, gas or water usage. The volumes and patterns of usage behavior often provide key insights to a business. We also recognize that people interact with our business and with each other in the real world. We may interact with people through mass marketing or traditional outbound communications like mail, email, text, or phone calls. Businesses often have customer care organizations that interact with customers in call centers or via online chats. Businesses also participate in social media interactions with customers via social networking sites and services. Of course, the vast majority of interactions that people have are not directly related to our business. People are in constant communication with each other, using many of those same mechanisms. They form communities and networks, establishing patterns of behaviors of communication. Like personal characteristics, not all of these interactions can be observed but an increasingly large percentage of key interactions actually can be observed and can be used in our analysis. Okay, while not exhaustive, that's a pretty broad look at people. Let's pivot to things in the real world that are not people, like objects and environmental events, where some of the same ideas apply. Like people, objects including the products we sell, have characteristics. Size, shape weight, features and functions, color, and a potentially long list of technical specifications or other qualifiers. Objects also have a physical location and can move. Think about the delivery path of a package or how your luggage moves when you take a flight. Consider the way raw materials might enter a factory and are progressively passed through an assembly line, turned into a product and warehoused. We also tend to sell products in and through sales channels like stores or websites. There are a wide variety of supply chain, logistics, and transportation applications where the location and movement of objects are critical to the function of a business, and where there are opportunities for data analytics. Furthermore, objects don't just exist and get moved around. They can actually do things, especially in machines. Engines run, air conditioning systems maintain temperatures and turn on and off. Computers run programs and process data. Advances in sensor and communications technology are allowing a vast mount of machine data to be generated and made available for monitoring and analytic application. Ideas like the connected home, and even the intersection of machine data and human physical data, like the connected body, are becoming more and more prevalent. Beyond people and objects, there are still a host of other natural and non-natural events and phenomena that can have an influence on our business. On one side there are things like seasons, temperature and weather. Tides and currents, major events like earthquakes and a variety of other biological or cosmic phenomenon that occur in the natural environment. There are also non-natural events or patterns that are initiated or influenced by people. Like weekdays and weekends, holidays, major sporting events, gatherings, traffic accidents, elections, war, and conflicts. And there are events with natural and non-natural drivers. Like outbreaks of disease or famine. Both natural and non-natural events can have a significant impact on a business. Think about the impact of weather on air travel or the impact of a major event like the Superbowl or World Cup on television viewership or internet usage. The impact of major world events can obviously have an enormous impact on people as well as businesses. The most important takeaway from all of this of course, is the recognition that just about everything you will be looking at as a data analyst starts as something in the real world. The second thing you should appreciate at this point is that there are an awful lot of things out there for you to potentially access and use.
