Types of Big-data Analytics

The volume of data and input received via various channels every second is just mind blowing. The technology has thrown a wide spread to collect all the inputs in different ways and in different forms. Be it images or comments or likes or videos or any creative inputs - they are captured in abundance with the magic of internet, machine learning and artificial intelligence that is branching out widely both in the social arena and the business bays. But how useful are these Big Data? What actual use is implied with the mounting pile of data and dots? Data has to be converted to information that can be used and dots need to be connected to make sense.

Analysis or Analytics of these data has evolved through time right from data mining up-to big data analytics at present. Let us introduce the basics of the different analytics generally used.The most common analytics include descriptive, predictive and prescriptive analytics. Descriptive analytics gives a clear picture and usually summarises the volume of data to give a meaningful information. This is one of the most common approaches used by Research agencies and data compilers. Predictive analytics is an advanced method that basically builds a pattern or a model that is suggestive of the future based on the past data or the compiled data. And one of the most advanced analytics is prescriptive analysis which is mostly action oriented. That is, based on the descriptive and predictive info, the system or the method is able to prescribe something that works better. It suggests action that is best, either based on cost reduction or time saving or any other resource of interest.

• Big data is extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.

Descriptive analytics primarily feeds you with the info that answers questions like what, where, how much or who etc. It condenses the data mass into something that is more meaningful and helpful in understanding a phenomenon or taking a decision based on the info. For instance, when a social media input as simple as number of views or number of shares is condensed on an average per unit of time to understand the sentiment or the intensity of the data that prevails in the atmosphere, it is descriptive. This enables the user to place significance on the issue or subject considered and accordingly take decisions or take steps.

Big Data Analytics

Predictive analytics is a methodology that give deeper info, where different data are used to predict an outcome of interest. Though it is not an exact forecastingof the future, it is a model building or a pattern developing technique, where in with the readily available mass of data, you are able to project the best or the most probable outcome. For example, a business person can predict his sales growth based on different factors that influence the sales: like trend, quality, competition, price and demographic variations. When a model or a pattern is developed that predicts the sales based on these factors, the model will suggest him to alter and manage these factors in order to enable maximum sales. This is a simple example citing the independent factors and the dependent or the predictable variable of interest.

Prescriptive analytics is an advanced technique where specific action or suggestion is prescribed that can be considered for the best output opted. For instance, when a GPS suggests a specific route to save time, amidst a number of different routes possible, it prescribes based on the time availability, if time is the main element to be considered for prescription. However, the algorithm can be changed to select a route based on best roads available or options of eateries available, depending on the need of the user, to accordingly receive a different prescription. More light will be thrown on Predictive Analytics in our next blog. Meanwhile feel free to chat with us to identify the technology that best answers the analytics you adopt.

The volume of data and input received via various channels every second is just mind blowing. The technology has thrown a wide spread to collect all the inputs in different ways and in different forms. Be it images or comments or likes or videos or any creative inputs - they are captured in abundance with the magic of internet, machine learning and artificial intelligence that is branching out widely both in the social arena and the business bays. But how useful are these Big Data? What actual use is implied with the mounting pile of data and dots? Data has to be converted to information that can be used and dots need to be connected to make sense.