Sole use of the term “Big Data” when describing analytics can be misleading. I see “Big Data” as collected raw material. That raw material isn’t always a huge amount of data from a single source, but can be many different data sets from different resources. The “Internet of Things”would be a great example.
Having this large amount of raw data doesn’t offer much value out of the box. However, when applying the correct analytics, we can extrapolate powerful insights.
Some of the common questions that are asked are:
Understanding analytics and the associated tools can help answer these and many other questions. I will describe how these tools differ, as well as the value they provide.
In its raw state, the data that we capture is indecipherable. However, the data we can conclude using analytics is indispensable. As you explore your data, you will begin to identify patterns at which point you can start to build your templates and analyze your data.
Called the “simplest class of analytics”, descriptive analytics allows you to condense big data into smaller, more useful bits of information or a summary of what happened.
It has been estimated that more than 80% of business analytics (e.g. social analytics) are descriptive. Some social data could include the number of posts, fans, followers, page views, check-ins,pins, etc. It would appear to be an endless list if we tried to list them all.
Predictive analytics is the next step up in data reduction. It utilizes a variety of statistical, modeling, data mining and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions about the future.
Think of an actuary for an insurance company. They take the raw data and provide the percentage or chance that an event (e.g. crash) can occur. Although we use the name predictive, predictive analytics can only forecast what might happen in the future; it cannot predict the future.
In the most general cases of predictive analytics, “you basically take data that you have to predict data that you don’t have”.
Prescriptive analytics goes above and beyond the previous tools by predicting not one possible future, but rather “multiple futures” based on the decision-maker’s potential actions. By showing the likely outcome of each decision, the decision-maker can take this information and execute. Additionally, it can recommend the best course of action for any pre-specified outcome.
Prescriptive analytics requires two additional components: 1) actionable data and 2) a feedback system that tracks the outcome produced by the action taken. This ultimately aids in capturing more data to analyze and adjust the action to provide more accurate results.
This is a very high level explanation of Big Data Analytics as I understand them.I welcome any feedback or perspective