Big Data, you might hear the term Big Data thrown around a lot not fully understand what is meant by it. In this video, you will be enlightened and learn how big data can help you and your business both now and in the future. Essentially, big data is nothing more than large datasets. These large data sets are increasingly common online. Seniors everything online is easy to measure and document. If you think about a company like Google, it has immense datasets that it works from describing the search history of billions of users.

But even a standard website that gets 1000 visitors a day will work with huge amounts of information. A website will naturally record each of those visits and will also store data about each one, such as the country of origin and the length of time spent on the site. In a few weeks, this data will likely crash a lot of spreadsheet software. The reason that big data is featured in so many discussions is that it is very difficult to handle. Making Sense of such huge amounts of information requires a lot of smart man while Simply storing and handling that kind of data requires a lot of storage and computational power. But the potential value of big data is also absolutely huge.

Big Data provides patterns and insights that you simply can't get by observing a few users. This is essentially how machine learning works. By looking for patterns in Massive Datasets. The difference is that this is being leveraged in a slightly different way. predictive modeling. predictive modeling is a process that involves data mining and probability to forecast potential future outcomes.

A model is created using a number of predictors, predictors or variables that are thought to influence future results. Once data is collected for these predictors, a statistical model can be created that might use a simple linear equation, or it might use complex neural networks. Either way, statistical analysis can then be used in order to make predictions about how things are likely to go in the future. With regards to marketing, this can help provide better customer insights. Better lead scoring, campaign nurturing, upselling and cross selling personalized product recommendations and more. Amazon is an example of a site that uses big data in order to provide personalized product recommendations.

Amazon doesn't just use a database of items grouped together, which would be almost impossible to maintain, but rather generates data automatically from every single transaction and sale, and then looks for patterns. It will see what products tend to be bought together, there's that co occurrence again, and can therefore use this information to show items that it thinks a user might want to buy next. Likewise, when it comes to lead scoring, big data can be immensely useful. lead scoring means understanding which leads are likely ready to purchase them which are not. This is immensely useful information for companies that might want to send sales letters to the cross section of their mailing list that they think will actually buy from them. Rather than being put off by the amount of sales material they're receiving.

Amnesty International user segmentation and predictive modeling techniques in order to better identify the right groups to market toward by collecting data and then looking at what that data reveals about the kinds of people who donate, Amnesty International knows who it should be targeting with its ads, how much they're likely to spend, and how they're likely to do it. Any charity can benefit from this kind of data analysis, as can any business collecting big data. If you want to start collecting data for your business, there are a wide number of plugins and tools you can use to do so. You should find that a lot of tools such as Google Analytics, will allow you to export massive amounts of data in order to work on, you can then choose to use this information yourself or outsource it to a data science organization that can use that information to provide valuable, useful insights.

Another good idea to prepare yourself for the future is to allow users to create profiles. By doing this, you can collect much more data on individual users and in future provide better recommendations on an individual basis to this system. thing that stores have been doing for decades with loyalty cards. But of course the digital nature of selling online creates even more potential opportunities.