What Do Marketers Use to Analyze Big Data?
As a practitioner of Big Data Analytics, one of the biggest questions I am consistently asked is “what do marketers use to analyze such Big Data?” The answer to this question is anything and everything that can be done with the data. From individual customer data to financial sales activity, you name it. There are even companies who specialize in providing “social intelligence” based services based on Big Data analytics.
The first step towards answering the question “what do marketers use to analyze big data?” is to understand why we need to analyze such data at all. After all, if there was no need for data analysis, then the analysis would serve no useful purpose. Why? Simply because data is used to make decisions, to determine what actions to take and how to conduct those actions in order to achieve certain goals.
So what do marketers use to analyze such Big Data? The answer may surprise you… Analysts use anything and everything that can be analyzed. Some of the most commonly used tools include graphs, tables, histograms, and neural networks. Let’s take a closer look at each of these:
Graphs – Are you familiar with some of the more sophisticated tools available for analysis? You are certainly familiar with traditional histograms and graphs. These tools allow you to visually depict the data in question. One popular application includes “heat maps” which allow you to view the data in different color depths. Heat maps are also commonly used in the web-based analytical environment
Tables – What about the table? We’re not discussing the type that we eat from our favorite restaurant. Instead, what about the kind of table used in hospitals, laboratories, and other places of business where large amounts of data are needed to analyze? If you use R or Python for data mining, you probably already know how to create and analyze tables. This is actually the most common tool available for analysis.
Histograms – This tool comes in a variety of shapes and sizes. You can find them as histograms, scatter plots, or as charts. They can be produced in many ways, but the most common (and the most effective) way is by using a log function. A log function takes the data you want to plot and converts it into a higher frequency (Hertz) plot. The lines between the points on the plot can be visualized using a logarithm. This is a powerful tool because many’s (High Frequency Trading) utilize this technique.
Of course, these are just a few examples. There are many more that you can learn about once you begin studying big data analysis software. Some of them will make your job easier, some of them will allow you to visualize data in new ways, and others will allow you to utilize a previously unknown or underutilized method for analyzing large volumes of data. As with anything, however, you should research the different features available before choosing a package. Don’t rely on what the sales page says; if you’re an analyst, it’s all about what you need.
Learn how you can benefit from big data analysis software by reading the product descriptions and learning about how it works. Pay close attention to what the package provides and whether it is right for your needs. Try to demo the product. Use all the features available, but don’t rely on them too much. Once you have learned what do marketers use to analyze big data, you’ll be able to make better decisions about what to add or what features to remove.