Big Data helps companies to generate valuable insights, develop their marketing campaigns and techniques for a business edge. Enterprises handling vast amounts of data also use it in machine learning projects and advanced analytics applications. They require advanced tools and technologies to handle and store the huge data streaming in real-time. Of the Big Data tools in use, Hadoop is the most commonly used. It is an open-source Big Data analytics software framework used for distributed storage of very large datasets on computer clusters.
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What is Big Data
Very large data sets contain greater variety, complexity, and more velocity. The data contains both structured and unstructured data that have to be analyzed for decision-making and business strategies. These data sets are so vast that traditional data processing software cannot handle them. They need to be analyzed computationally to reveal patterns, trends, and associations.
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Big data is a term applied to these data sets whose size or type is beyond the capacity of traditional relational databases to capture, manage and process the data with low latency. Big data typically displays one or more of the following characteristics: high volume, high velocity, or high variety.
What are Big Data technologies and tools?
Big data technologies are software utilities designed to analyze, process, and extract information from very complex and large data sets which the traditional data processing software cannot handle. They are tools that help to process Big Data concurrently in real-time.
Thousands of Big Data tools exist that perform various tasks and processes to save the company time and money while uncovering business insights. Some of these Big Data tools are proprietary and some are open-source frameworks. They evolved when the need arose for speedy computations of huge data streams from IoT in real-time. Storage and processing power are the critical decision points. Other features that differentiate between various Big Data tools are hardware requirements, data engine type, language and SQL support, coding, computing speed, analytics capabilities, data storage and pipelining, fault tolerance, interactive latency, and security. A good Big Data storage provider should offer an infrastructure to run all your various Big Data tools, and provide a place to store, query, and analyze your data.
What are some examples of Big Data?
As more and more organizations witness digital transformation, they handle more Big Data and work with Big Data tools. Most enterprises across industries are working with Big Data every day.
Here are some examples of how Big Data is shaping businesses and government work:
Marketing and Advertising
One of Big Data’s most prolific use cases is in marketing and advertising. The advertisements on Facebook or Instagram are Big Data at work.
Netflix’s recommendation is another example. Netflix collects data on all its subscribers: what people watch, when they watch it, the device they use, how long a user takes to watch a series, and so on. All this information is fed into their algorithms, to create user profiles based on behavior. These allow recommending movies and shows for a tailored experience.
Amazon too collects vast amounts of data on its users. They track what users buy, how often, how long they stay online, etc. This data is used by Amazon to create highly-specialized segmented user profiles for targeted marketing based on users’ browsing habits.
Travel and Navigation
Navigation and travel have been transformed by technology, with Big Data creating a base for insights and decision-making.
HERE Technologies use location data for navigation. A perpetual stream of data from fleets of roaming self-driving vehicles helps warn drivers about lane closures miles away or blind curves.
Leading courier service carriers use location data points to track packages in real-time. Predictive algorithm factors in traffic and weather data to calculate package expected time of arrival, so customers can be given advance warning about delays and early deliveries.
Supply-chain Management
Big Data offers suppliers better logistics, speed, and insights. Through the application of Big Data analytics, suppliers can gain contextual insights across the supply chains to make their route analysis and plan their warehousing and logistics.
PepsiCo is a packaged goods consumer brand that relies on Big Data for efficient supply chain management. The company has to ensure replenishing the retailers’ shelves with appropriate volumes and products. The company’s clients provide data on warehouse inventory and the point-of-sale stocks to the company, and this data is used to predict the production and shipment needs. This way, the company ensures retailers have the right products, in the right volumes, and at the right time.
Manufacturing
Machine maintenance is a critical line of operations in manufacturing, as it is done in a continuum to ensure there is no stoppage in work. It is based on a huge amount of data, from sensor data at hundreds of sensor points to other data like upgrades, times, repairs, feeds, points of an outage, etc. The data gathered from the devices’ sensors and logbooks help organizations in determining when and how the maintenance is to be done by a specific machine. Big data analytics helps manufacturers keep track of their machines by continually analyzing and reporting on how to improve the efficiency of machines.
Risk Management
The business environment is getting more global and 24/7. This is making business transactions more complex and in real-time, calling for better risk management. It is being able to foresee potential risk and mitigate it before it occurs. Big data analytics is helping develop risk management solutions and tools that allow businesses to quantify and model risks that they face every day.
The UOB Bank in Singapore uses Big Data to drive risk management. As a financial institution, it stands to lose a great deal if the risk management is not well devised. The big data risk management system enables the bank to reduce the calculation time of the value at risk. From the initial 18 hours, it now takes only a few minutes to carry out real-time risk analysis.
Summary
There is no best Big Data platform. Each of the Big Data tools caters to different needs and supports different capabilities. So it is important to choose the Big Data tool that best fits the situation in your company. With Hadoop emerging as a commonly used framework, you must opt for Hadoop training to prepare yourself for a Big Data working environment.
