Big data is an enormous volume of information. As a practice, big data analytics refers to collecting, analyzing, and using extreme quantities of data to get scientific insights or business advantages.
Big data turns the information that all companies naturally accumulate into an asset. Learning how to use big data can help your business grow by enabling you to make more informed and precise decisions, analyze previous mistakes effectively, and adapt to emerging demands faster than your competitors.
The main benefits you can receive from leveraging this method include the following:
- a scientific approach to decision making;
- greater customer satisfaction through precise personalization;
- a thorough analysis of how effective your strategies are;
- the discovery of new potential revenue sources;
- a significant improvement in the efficiency of operations, which reduces operating costs; and
- higher quality of the final product.
The primary big data terms you should get familiar with include the following:
- Analytics: a practice of deriving typically actionable insights and patterns from raw data;
- Predictive analytics: a practice used to analyze previous events and generate forecasts based on them;
- Descriptive analytics: unlike predictive analytics, this practice simply describes previous occurrences. For instance, such an application could break down your monthly spendings into categories without providing any recommendations;
- Cloud computing: using several servers instead of computing everything locally;
- Dark data: a pool of information that a company gathers but never uses or plans to use;
- Algorithm: a sequence of instructions given to a computer to perform some objective;
- Clustering: determining similar qualities in several elements and bringing those elements together;
- Data science: a scientific field that studies how practical value can be derived out of data;
- Machine learning: an approach that strives to enable a computer to perform certain calculations without being explicitly programmed to do so;
- Structured and unstructured data: structured data (temperature, speed, growth rate, etc.) can be arranged into tables, while unstructured data needs processing to be fit for that (videos, emails, Tweets, pictures, etc.);
- Dataset: a collection of data entries;
- Data mining: a practice of using statistics, machine learning, and artificial intelligence to find patterns—not necessarily actionable—in any dataset;
- NoSQL: a database format designed to hold and operate large amounts of data;
- Python and R: the two most popular programming languages used for data science;
- Visualization: a practice of creating visual representations (graphs, charts, etc.) of data for analytics and convenience.
Big data applications are extensive in all fields, from healthcare and software development to retail and entertainment.
The most widespread ways of how companies use big data include the following:
- personalized advertising;
- better targeting;
- in healthcare, using data from wearable devices to monitor a user’s condition;
- faster and more helpful technical support responses;
- automation of communication via chatbots and email templates;
- enhanced quality and compliance control;
- assigning employees’ to projects by their performance, motivating underperforming workers and challenging the best ones to maintain their interest;
- automatic report generation and sending alerts if any inconsistencies are discovered;
- optimizing the performance of corporate technology; and
- strengthening security.
Several examples of big data:
- Facebook posts, Instagram pictures, and other social media content;
- customers’ names, contact information, and previous purchases;
- trade data from a stock exchange;
- temperature, wearing, and other performance data from a plane engine;
- a database containing patients’ medical records and prescriptions;
- road images used to develop machine learning models for autonomous cars;
- statistics on how many cases of a particular disease are registered daily at specified locations.
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