Data is the information provided by tools used to measure, record, and extract information from phenomena. These tools are known as big data. They are designed to answer questions based on their experience with prior data sets instead of being instructed to look for specific patterns in new data. The volume of data captured has exponentially increased over recent years due to an increase in electronic devices collecting data. For instance, Facebook receives 10 billion+ status updates per day! There is a growing range of sources, including sensor networks, personal digital assistants, smartphones – even cars and washing machines.
Big Data refers to the large volume of data that companies can now capture about their customers’ behaviors, preferences, and thoughts. Big Data allows companies to consider the needs of their customers in new ways, respond quickly, measure what matters, and find even small opportunities for improvement.
Big data makes it possible to store information on an unprecedented scale. It also enables machine learning at a massive scale where computers can process large amounts of data without explicitly programmed how to do so. Big data can be gathered from social media posts, website clickstreams, mobile phone GPS traces, point-of-sale purchases, or customer service transactions.
Volume refers to the vast amount of data that is available today. It includes structured data stored in relational databases and unstructured data stored on file systems. Big Data has driven the need to develop scalable storage systems capable of supporting quick retrieval of data.
Big Data is becoming more diverse in terms of data formats and sources. For instance, there is an abundance of images available on Facebook, YouTube, Pinterest, etc. A valuable feature of images is that they can be run through OCR (optical character recognition) tools to make the images’ text searchable through keywords, e.g., when you upload a photo on Flickr or chat with someone on Skype.
Velocity refers to the speed at which Big Data needs for analysis for it to be meaningful. It also refers to the rate at which new data arrives during operation, driven by real-time transaction processing, social media interactions, machine logs, and mobile device location tracking.
Big Data is beneficial for providing insight into trends that can predict future behavior, recommend products, or target ads. This represents a real opportunity for businesses to generate new insights based on the analysis of data collected in real-time, continuously feeding signals back into the business decision-making process. It also involves converting unstructured data (e.g., social media feeds) into structured databases, enabling multiple tools to analyze the same data set.
In conclusion, Big Data is now being used as a tool by companies to serve their customers better and gain a competitive edge over rivals. In many cases, this means giving their existing customer base what they want, when they want it, and how.
The volume, velocity, and variety of online data make Big Data a potential force to drive significant changes in business operations. Here are some key benefits:
Similar to the way smartphones can access weather updates, customer feedback, and news updates, companies now have instant information about what their customers like and dislike, where they come from and where they spend their time (location data), and who their closest friends or family members are (social network data). This allows businesses to personalize products and services based on specific preferences. For instance, Google analyses search terms in real-time across millions of queries and the user’s location (if permitted) to provide real-time traffic updates, weather forecasts, and suggestions for restaurants that are “trending” near the user.
Big data presents a unique opportunity for companies to explore entirely new lines of business or product ideas. For example, mobile phone companies have used their customer records to launch mobile payment services. Another example is how Netflix uses big data to recommend movies based on customers’ past viewing history.
Using big data can help businesses operate more efficiently by minimizing errors, improving workflows, optimizing assets, driving down costs, and enhancing planning capabilities. By analyzing transactions across multiple business units such as supply chains and distribution channels, a company can reduce costs/cut overhead by identifying where waste is occurring.
It allows companies to gather market intelligence which enables them to stay ahead of competitors. Big data provides an opportunity for businesses to gather unique insights into what customers worldwide are purchasing and doing online and offline, empowering them with valuable information that competitors do not have. For instance, Walmart mines social media sites such as Twitter to understand what products consumers prefer to adjust its stocking accordingly.
Through analyzing big data such as customer purchase histories or search trends, companies can better pinpoint any issues with their products or services which may be leading some customers to raise complaints. For instance, a bank can resolve simple mistakes such as misspelled account names with a single click of a mouse rather than manually going through all customer records according to RemoteDBA.com.
By analyzing big data created from Internet-connected devices, businesses can better understand how their employees use resources in the workplace, enabling them to cut costs and save time by reducing unnecessary expenses such as printing reports/documents or traveling unnecessarily between locations.
By analyzing big data from social media feeds and search logs, businesses can identify patterns that could indicate future trends or changes in consumer behavior, i.e., determining which products/services may be on the rise or about to decline so they can be stocked accordingly. For example, Walmart was able to identify that demand for monster-truck style tires would rise before the release of “Cars 2” in 2011 by analyzing trends from social media sources.
Similar to how weather forecasts are improved through collecting data about current conditions and using statistical models, companies are improving their forecasting capabilities across multiple business areas such as market research, manufacturing, inventory management, etc., by analyzing big data sets.
For example, by identifying consumer trends based on historical shopping patterns, a fashion retailer can better anticipate customer behavior by creating more accurate sales forecasts, which can help it make more informed decisions when ordering future products/stock levels.