What is big data analytics? Why is it important in 2021? In this article, we’ll give you the answers to these questions and you will get to know more about big data business.
Plus, we’ll explain how to collect big data and use it and the relationship between big data and data mining.
So if you’re looking to harness the power of data-driven decision-making and management, you’ll enjoy this article.
What Is Big Data?
Big data is a term that describes extremely large volumes of data, or data sets, that contain a variety of structured or unstructured information depending on its importance and purpose for the organization that collects it.
Big data innovative forms of data collection, data processing, and data analytics, as well as technologies to perform these tasks, before businesses can use that information.
Companies can use big data to gain enhanced insights that enhance the decision-making, market and operational activities, and process automation capabilities of the organization.
Features of Big Data
To generate the best results and insights from big data, it has to have these four basic characteristics, often referred to as the four V’s of big data:
The sheer volume of data that’s required for gaining insights and business intelligence is what makes big data “big”.
For it to provide valuable business intelligence, big data has to contain a variety of both structured and unstructured data.
Structured data is information that’s defined by a set of rules. For example, money always has a number with two decimal points; names are written in text; dates have patterns that express days, months, and years.
Unstructured data is information without these rules. For example, audio recordings, images, tweets, or blog comments are all unstructured data, as they express human thoughts, ideas, and emotions
Making sense of them is what gives businesses insight into the human behavior of consumers and customers.
Veracity is what defines the trustworthiness of the data, as there are always inconsistencies between the collected data and the real world.
Velocity refers to the frequency at which companies have to process incoming data in a given timeframe.
There is also a fifth characteristic of big data that many overlook:
The value of big data relates to the intelligence gained from collecting, processing, and analyzing information. But also the practicality of making decisions and applying what you learn to real-world situations based on that intelligence.
How Can You Collect Big Data?
Organizations capture data in many ways and from numerous sources. This is called data mining.
Typically, the data that they collect has to do with consumers and their behavior, and can be broken down into four groups:
#1 Personal Data
Personal data refers to identifiable information about individuals such as people’s name, age, gender, date of birth, Social Security numbers, and location. But also unidentifiable information like browser cookies, IP address, device IDs, and type of internet connectivity.
#2 Engagement Data
This kind of data provides information about consumer interaction with a businesses’ site, social media pages, paid ads, emails, text messages, and customer service activities.
#3 Behavioral Data
This group of data details how consumers behave in their relationships with organizations and includes information about purchase history, product use, repeat actions online, and qualitative data such as time spent on page, mouse movement, and on-page heatmaps.
#4 Attitudinal Data
This group includes detailed information about consumers’ experiences when dealing with a company. For example, customer satisfaction, purchase criteria, and desirability of products all represent attitudinal data.
Due to privacy and personal data laws, companies have to ask for permission to collect this data and protect the rights of consumers and users online.
So how do you actually capture all of this data?
Big Data Collection Methods
To capture as much consensual information about users, companies use a variety of ways to collect data online. Here are a few ways companies gather big data business intelligence:
- Sign up forms
- Online accounts
- E-commerce purchases
- Order requests
- Newsletter subscriptions
- Gated content
- Loyalty programs
- Gameplay and quizzes
- Social media activities
- Satellite imagery
- Buying big data from providers
What Is Big Data Mining?
Once you capture and collect big data, it’s time to mine it. But what exactly is data mining?
Data mining is the process of analyzing large volumes of raw data (data sets) to extract meaningful information, including the patterns, irregularities, and connections within that data.
Because of data mining, individuals and organizations can generate statistical forecasts that predict business risks, opportunities, and outcomes within the context of big data.
But also patterns in human behavior that help them attract, persuade, and convince people to use their products, services, or content.
But what is then the difference between data mining and data analytics?
What Is Big Data Analytics?
Data analytics is the process of examining extracted data to identify actionable information that helps organizations make informed business decisions based on the captured data sets.
To do this, data analytics deploys various technologies such as complex software solutions and applications to analyze the data (data processing) and mine it for meaningful inputs and patterns.
But also analytical techniques such as behavioral psychology, statistical algorithms, predictive models, forecasting, and what-if analysis.
Thanks to data analytics solutions, organizations can gather valuable business intelligence that answers queries about their products, services, operational efficiency, and performance.
So what’s the difference between data mining and data analytics?
Think of it like this:
Imagine big data as a really big lake of information. To reach any point on the lake, you need a boat. That’s data analytics. But to make the boat move in any direction you also need a paddle to push the water. That’s data mining.
Big Data Business Intelligence: The Key to Data-Driven Business
Big data in business isn’t a new invention. From Amazon and Walmart, through Netflix and Apple, to American Express and Capital One, there are many examples of companies using big data to reach their business goals.
Technically, it’s the key to unlocking the path to a data-driven business.
So, are you ready to start using big data?
We can help you with that!
At Demakis Technologies, we specialize in cybersecurity, support helpdesk, and managed services that can help you collect data, keep it safe, and operate the tech infrastructure that lets you gather meaningful information from big data.
To find out more, please CONTACT US and get in touch with one of our IT professionals.