Have you ever heard of personal data theft for the data analysis? Must have heard of Facebook–Cambridge Analytica data scandal? Cambridge Analytica was a British political consulting firm which managed to collect millions of Facebook user’s personal data and it is said to be a major influencer in the USA elections 2016. It is one of the biggest scandals of recent times. Data Mining and Predictive Analytics are all about dealing with such huge data and its analysis. Apache Hadoop is one of the well-known tools to deal with massive amounts of data.
Even if you’re not really sure what kind of data big tech companies are mining from you, you’ve probably noticed some things that make you wonder who is “watching” you and what kind of information they’re storing. For example, if you’re on Facebook, ads for things that you’ve recently viewed on another site may pop up. Or if you’re reading a news website, you might notice a link to a shirt that you had your eye on. It’s almost like your computer is retrieving all that information and recording it.
Not to scare you, but that’s because it is. But it’s not necessarily all bad. Companies are retrieving a lot of information about you – gigantic swaths of data. Luckily (or maybe not) for all of us, they haven’t quite figured out what to do with it. Once they do – and once you do for your own company – you’re going to be fast into the world of data mining and predictive analytics, and that’s where things get interesting.
Of course, experts already know that those two topics – data mining and predictive analytics – aren’t the same things. The first is pretty easy to understand, and you’ll start to see how companies mine your data throughout the day (and also how you can mine your customer’s data). For example, they’ll start by trying to collect your email to sign you up for rewards. They would want you to use that rewards number whenever you buy something because if you do, they can start to record what you’re buying and when you’re buying it.
On the surface, that’s just data, nothing more. It’s important, but it’s what companies do with the data that matters. If they’re smart, they’re going to take it to the next step of data mining, which is figuring out patterns and organizing methods for that data. It helps you understand what time of day to market to them, of course, and what days of the week they like to visit a site, to name just two examples.
The next step, of course, follows data mining and that’s predictive analysis. This process puts the data to work – who are you and what are you going to do now and in the future? Are you going to grow your spending, and what information can a company provide to you to encourage you to do that? If companies can figure that out, then they can forecast the future of sales – beyond just you, of course. More examples of how data can be used to predict the end result are explained here.
You may not realize it, but you’ve probably already been subject to predictive analysis. Think about applying for a credit card or a mortgage. Your credit score is a collection of data, and the approval or denial of the loan or credit card is a predictive analysis the financial institution makes on your behalf.
The following infographics explain what are data mining and
predictive analytics and how these both can be combined to get better results:
Data mining is the process of discovering useful data or patterns in large data sets. The below image explains the process of data mining. It starts with a data warehouse where the large data is stored usually and then cleaning, analyzing, applying algorithms (ML), interpreting results are performed.
Predictive analysis is the continuation of data mining where a predictive score is assigned to the identified patterns. This helps in prioritizing the data based on the importance.
When you use both data mining and predictive analysis together it can create wonders. The important data can be filtered in seconds. The output of data mining acts as input to the predictive analysis i.e. the predictive analysis acts on the patterns identified by the data mining and the predictive score is assigned to the patter
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