Data Analytics

Data Analytics

Analytics may be understood as using data to answer questions. It is the process of assessing and studying data in order to derive insight, from which one can make decisions and take actions that lead to effective outcomes. Wikipedia has a straightforward definition1 “Analytics is the discovery, interpretation, and communication of meaningful patterns in data.” The Business Dictionary expands upon this2 “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 a third definition from David Gould who defines it as3 “how an entity(i.e., business) arrives at the most optimal or realistic decision from a variety of available options, based on existing data.” On a more generalized 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 measure of uncertainty. If we take a binary digit that can have two states 1 or 0, before I am given any information I am uncertain about the state of this system, it could be 1 or 0, however when you give me that piece of information I can check to see which state it is 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. 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 would be in finance, when we are 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 is 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 have never been to 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 station I will have all this information from past experience and I will walk straight to the ticket machine and then straight to the train, thus conserving time and energy, less time and resources expended means they are 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 is 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 are interested in analytics because it is the central part of the information processing system within any organization that 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 is scalable. When we use the term analytics we are typically talking about the systematic computational analysis 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 formalize the problem into computer code 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 analysis that is required for enabling a whole organization to operate successfully, 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 insights.


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 environment 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 describing of data. Most analytics is of this form simply sampling data and presenting it to the organization to make decisions with. Descriptive analytics in the form of pie charts and bar charts in presentations have been the staple of business intelligence for decades. 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 action for the organization.


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 making the important 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 still lead you in the wrong direction. You need human intelligence and you need that working with the analytical capacities of the organization, it is only then that you can hope to really 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 computation processes working together.


1. Wikiwand. (2018). Analytics | Wikiwand. [online] Available at: [Accessed 8 Feb. 2018].

2. (2018). What are analytics? definition and meaning. [online] Available at: [Accessed 8 Feb. 2018].

3. Urban Dictionary. (2018). Urban Dictionary: Analytics. [online] Available at: [Accessed 8 Feb. 2018].

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