 In this module, we're going to lay down a basic understanding of what we mean by this term analytics or data analytics. Let's first take a look at some of the definitions that are out there, so as to give us an intuition for what we're talking about. Wikipedia has a straightforward definition. Analytics is the discovery, interpretation and communication of meaningful patterns in data. The business dictionary expands upon this. Analytics often involves studying past historical data to research potential trends to analyze the effects of certain decisions or events or to evaluate the performance of a given tool or scenario. The goal of analytics is to improve the business by gaining knowledge which can be used to make improvements or changes. Or finally, a definition from David Goud, who defines us how an entity, i.e. a business, arrives at the most optimal or realistic decision from a variety of available options based on existing data. Analytics then can be understood quite simply as using data to answer questions. It is the process of analyzing and studying data in order to derive insight from which we can make decisions and take actions that lead to effective outcomes. On a more general level, we can understand analytics as simply the information processing activity that takes place within all organisms, individuals and organizations. Whereby we take in information, process it and create a response that enables the development of the organization within its environment. The central aspect here is that of information. Information is understood on a technical level as a measurement of uncertainty. If we take a binary digit that can have two states, one or zero, before I'm given any information, I am uncertain about the state of this system. It could be one or it could be zero. However, when you give me that piece of information, I can check to see what state it's in and in so doing reduce the uncertainty about its value. Indeed, information is not just about uncertainty, but by extension it is the capacity for an organization to grow and develop over time. This is due to the fact that by reducing the uncertainty, we increase the certainty that our actions will be successful. When we reduce the uncertainty, we can increase the efficiency with which we allocate resources and can thus develop faster. A simple example of this could be seen in finance when we're trying to hedge our bets. If we know that a certain outcome or set of outcomes will not occur, then we do not need to hedge against them and spread our resources. Instead, we can concentrate the allocation of our capital to a specific set of outcomes and thus increase our returns. This is important because it's a general condition. The more information we have, the less we have to expend resources in uncertain conditions and the more the organization can invest in those options that will lead to growth. When I walk into a train station that I've never been into before, I will have to expend a considerable amount of time finding where to buy a ticket, what time the train leaves, which platform, where the platform is etc. But the next time I enter the train station, I will have all this information from past experience. I will walk straight to the ticket machine and then straight to the train, thus conserving time and energy. Less time and resources expended mean they're available for me to invest in other options. The same is true for technology. If I have a motion sensor in my house, it can know when there's no one there and switch the lighting and heating off so as to have more resources to allocate in the future. So this is why we're interested in analytics, because it's the central part of the information processing system within an organization and can enable it to develop and grow more effectively. Analytics is all about finding patterns in data, which is exactly what humans do all day every day. However, our aim here is to automate this process of pattern discovery so that it can be scaled to large organizations. When we use the term analytics, we're typically talking about the systematic computation of data within an organization. We take data and use computers to search through it to answer a question that is in some way of importance to the success of the organization. If we can figure out how to formulate the problem into computer codes, then we can harness the true power of computation, which is to iterate very rapidly on simple rules. By iterating very rapidly on simple rules that are combined into high-level algorithms, a computer can analyze much more data much faster than a human can. As a result, we can begin to approach the amount of data analytics that is required for enabling a large organization to operate successfully in a complex environment, which is the end objective. So data analytics is the information processing unit of an organization that uses data, computation, and mathematical modeling to generate actionable insight. It all starts with data, unlike a more theoretical approach that might start with logical reasoning and theoretical frameworks for deducing information. In contrast, analytics is always grounded in data. We sample a state space, taking in data about the system or environments, and data modeling is used to organize and structure it into a form that can be processed by the system. This may be called descriptive analytics, which is the simplest form involving the gathering and description of data. Most analytics is of this form, simply sampling data and presenting it to the organization for them to make decisions with. Descriptive analytics, in the form of pie charts and bar charts in presentations, have been a staple of business intelligence for decades now. A step up from this is predictive analytics, which tries to apply rules to the data to process it into forecasts about what will happen in the future. Beyond this, prescriptive analytics involves using that insight to make recommendations and suggest courses of actions for the organization to take. All data analytics exists within the context of the broader business intelligence of the organization. This typically involves people asking the questions to start with and those people making the decisions at the end of the day. As such, if we want effective overall outcomes, we have to think about the system as a whole, the people and the technology. You don't just need the right data, models and technology, you also need the right people asking the right questions. If you ask the wrong question in the first place, it doesn't matter how good your answer is, it will lead you in the wrong direction. You need human intelligence and you need that working with the analytical capabilities of the organization. It's only then that you can really hope to achieve sustained success. At the end of the day, this is all about the success of the organization and that is dependent upon the whole system of human intelligence and analytical computational processes working together.