What is Big Data and How is it Used in Data Analytics and Data Visualization?

Ricardo Garza
Sep 6, 2018 · 4 min read

It’s not a secret that organizations and corporations today have more data than ever at their disposal. Your google search, a passport scan, a barcode scan at your grocery store, your online shopping history, an EKG reading, a tweet, a voicemail, a social media post, a music playlist, or a tv show you binged watch. All of these things contain data that can be collected, analyzed, and monetized.

Big data refers to extremely large data sets that are analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Customer behavior and preferences is used to better understand and predict consumer actions. Target is able to accurately predict when a customer will expect a baby, Wal-Mart can predict what products will sell, and car insurance companies can predict how well a customer will drive. Delivery route optimization is being used by shipping companies to track goods, delivery vehicles, and live traffic data.

Dating websites apply big data tools and algorithms to find matches. Law enforcement uses big data to foil terrorism and prevent cyber attacks. Credit card companies use big data to detect fraudulent transactions. In the financial sector big data is used to make trading decisions based on data algorithms that use social media networks and news websites to make, buy and sell in split seconds.

Netflix understands the importance of big data and has used analytics to give itself an edge over Hollywood. Valued at $164 billion, Netflix has overtaken Disney as the world’s most valuable media company. Not only do we give Netflix our money, but something more valuable: detailed information about our tastes, habits, and interests.

Every action we take leaves a digital trail. To make sense of all this data organizations and corporations use analytics involving artificial intelligence and machine learning. We teach computers to identify what the data represents using image recognition or natural language processing to spot patterns much more quickly and reliably than humans.

Data Visualization

Numbers are difficult to look at. Data visualization represents data by making explicit the trends and patterns inherent in the data. This data is then interpreted into traditional forms of visualization such as charts, tables, line graphs, column charts, and even 3d visualizations. These visualizations are only as effective as the data that is used to prepare the visualization. Incomplete data will give us half-baked, obsolete, or error prone visualization. Visualization tools mostly deal with raw and unstructured data. In other words, visualization tools focus on reporting data rather than analyzing it.

Some of the most popular tools that developers use for visualization include the following JavaScript libraries: D3.js, Ember Charts, NVD3, Google Charts, Fusion Charts, Highcharts, Chart.js, Leaflet, Chartist.js, Sigma JS, Polymaps, and Processing.js.

Data Analytics

Many confuse data analytics with data visualization. Although both allow users to make sense of data, data analytics deals with data at a deeper level going to so far as to not only have a front end which transforms the data into a visual context, but also a backend with tools and algorithms. Data analytics goes deeper by identifying and discovering trends and patterns inherent in the data. End to end analytic tools use data mining algorithms to cleanse the data, evaluates it using different evaluation models and software tools, runs it through algorithms, and then displays the results.

There are wide variety of tools, software, and service providers for use with big data analytics. Some of the more well known are KNIME, Open Refine, R-programming, orange, Talend, RapidMiner, MainerGephi, Tableau Public, NodeXL, Hadoop, and Segment.

While there is overlap between analytics and visualization, it’s important to note that visualization isn’t always the end of the process or culmination of a project. In most situations data analytics and visualization are used in a cycle. Sometimes visualization is used early in the process to identify possible correlations or identify metrics such median averages, data spread, deviation, and scope.

The best solutions use both, a backend data analytics platform and a frontend tool that delivers data visualizations to convey information powerfully and intuitively. Analytics cannot do what data visualization can do, which is to help to communicate and explain a picture with precision and brevity that allows the brain to consume it quickly. It makes the data human-friendly. Both Data analysis and data visualization are critical business intelligence tools to mine the power within an organization’s large collection of data.

Ricardo Garza

Written by

Former teacher. Current student at Flatiron School. Future full stack web developer.

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