Big Data: Expert Guides and Resources

Getting the most out of your unstructured data is an essential task for any organization these days, especially when considering the disparate storage systems, applications, and user locations. So, it’s not an accident that data orchestration is the term that brings everything together.Bringing all your data together shares similarities with conducting an orchestra.

Big data makes big headlines, but it’s much more than just a buzz phrase or the latest business fad. The phenomenon is very real and it’s producing concrete benefits in so many different areas – particularly in business. Here you will get to the heart of big data as a business owner or manager: You will take a look at the key terminology you need to understand the crucial big data skills for businesses, ten steps to using big data to make better decisions, and tips for communicating insights from data to your colleagues.

Summary statistical measures represent the key properties of a sample or population as a single numerical value. This has the advantage of providing important information in a very compact form. It also simplifies comparing multiple samples or populations. Summary statistical measures can be divided into three types: measures of central tendency, measures of central dispersion, and measures of association.

To stay competitive today, companies must find practical ways to deal with big data — that is, to learn new ways to capture and analyze growing amounts of information about customers, products, and services.Data is becoming increasingly complex in structured and unstructured ways. New sources of data come from machines, such as sensors; social business sites; and website interaction, such as click-stream data.

There are several Exploratory Data Analysis (EDA) techniques you can use to test assumptions about a dataset. These include run sequence plot, lag plot, histogram, and normal probability plot. Run sequence plot Many statistical techniques are based on the assumption that the data being analyzed has the following properties: Independent variables Variables drawn from a common probability distribution Variables with common parameters (for example, mean and standard deviation) A run sequence plot tests whether the data conforms to these assumptions.

Measures of central tendency show the center of a data set. Three of the most commonly used measures of central tendency are the mean, median, and mode. Mean Mean is another word for average. Here is the formula for computing the mean of a sample: With this formula, you compute the sample mean by simply adding up all the elements in the sample and then dividing by the number of elements in the sample.

One benefit of your big data analytics can be fraud prevention. By many estimates, at least 10 percent of insurance company payments are for fraudulent claims, and the global sum of these fraudulent payments amounts to billions or possibly trillions of dollars. While insurance fraud is not a new problem, the severity of the problem is increasing and perpetrators of insurance fraud are becoming increasingly sophisticated.

The technical jargon surrounding big data can seem a little daunting at first. The key phrases and terms you’re likely to come across, with easy-to-understand definitions for each, follow: Big data: Increasingly, everything you do leaves a digital trace (or data), which you (and others) can use and analyse. The phrase big data refers to that data being collected and the ability to make use of it.

Big data can help you gain insight. Businesses gain competitive advantage when the right information is delivered to the right people at the right time. This means extracting insights and information from data and communicating them to decision makers in a way they’ll easily understand. After all, people are less likely to act if they have to work hard to understand the information in front of them.