Hassan is a data scientist and has obtained his Master of Science in Data Science from Heriot-Watt University.
Big data plays an enormous role in analytics, changing our lives in many ways. Big data analytics is a massive part of business intelligence, making predictions about customer behavior possible.
Many companies use big data for targeted advertising and recommendations. Still, it can also be used to make better-informed decisions concerning product development, sales forecasting, marketing strategies, etc. This article will explain what big data is, why it's important and how it improves our lives.
What Is Big Data?
It is a term used to describe the massive amounts of information that can be processed and analyzed to gain insights into many different aspects of our lives. It's usually associated with traffic patterns, weather data, social media posts, and other digital sources, but it also includes things like credit card transactions and sales reports. The term has been used for a while, but it became popular in the early 2000s when companies realized how much data they were collecting every day and what they could do with it.
The 3 Types of Big Data
The amount of data we're dealing with has grown exponentially since then, and there are many different ways to categorize it. Some of these classifications are pretty straightforward, but others can be confusing. For example, there are three main data types: structured, unstructured, and semi-structured.
- Structured data is the most common type of data available. It's comprised of things like date and time, location, names, and addresses. This information can be quickly sorted and categorized because it follows a specific structure. For example, a person's name will always appear in the same place on their driver's license or passport.
- Unstructured data is a bit more complicated. It's often used in the content of documents or emails, and it doesn't have an apparent structure. An example of unstructured data is the text of an email message that doesn't have any headers or metadata attached.
- Semi-structured data is a type of data that's not as easily categorized as structured or unstructured data. An example of semi-structured data is the content of an email message that doesn't have any headers or metadata attached. Still, the text has been separated from other document parts by using paragraph breaks and line spacing. Semi-structured data is usually more challenging to use and analyze than structured or unstructured data.
The difference between structured, semi-structured, and unstructured data is critical to understand because it can help you find the best tool for your needs.
Why Is Big Data Becoming Important?
Like oil, gold, or water, Big Data is a resource becoming increasingly important as it becomes more scarce. As a result, it is also being called "the new oil" by some.
We are entering an era where the ability to harness and use data effectively will become key to staying ahead of the competition.
And this isn't just true for companies that deal in traditional industries like manufacturing and retail; any business that hopes to survive in today's marketplace must understand how Big Data fits into their strategy.
How Is Big Data Changing Our Lives?
Big Data has many diverse applications in every aspect of our lives today. It is being used by everybody, from students to doctors, from financial analysts to social scientists, and many other people in various fields. Here are some re-emerging uses of Big Data.
In the medical field, Big Data is used by healthcare researchers to indicate trends, patterns, and connections that may not be apparent just through traditional data analysis techniques. It is also being used for the visualization of biomedical research data. For example, Big Data can help predict behaviors like when a patient may get sick, how their disease will progress over time and what treatments might work better for them based on their genetic makeup (basically how responsive they are to drugs) or family history.
Business Products and Services
Businesses and corporations are always trying to figure out how to use big data to their advantage. And this is a great thing! Many companies are finding a direct correlation between the quality of their product and the amount of data collected.
For example, Amazon leverages data science to ensure the products they feature on their retail website are relevant to the needs of their users. Amazon has created a predictive analytics model that uses millions of data points to determine the probability that a user will buy an item. The company uses this model to determine which products are most likely to be purchased by users, and then they display those items on their website. In addition, Amazon also collects data on how customers interact with the site so they can make adjustments if necessary.
Weather patterns are a great example of Big Data. Weather satellites capture vast amounts of data about the earth's atmosphere, which scientists process to create weather forecasts and other helpful information.
Consumer (and Criminal) Information
Social media networks like Twitter and Facebook generate massive amounts of data daily as millions of people share their thoughts, opinions, and experiences online. Retail firms collect consumer information from credit cards, targeting advertisements according to customers' tastes and preferences; law enforcement agencies can use GPS data from mobile phones' built-in GPS chipsets to track criminals.
These are just some scenarios of how data is being used. The key is not just collecting data but also analyzing it and making the right decisions based on its analysis.
The 6 Vs of Big Data
Big Data allows us to see things that weren't possible before and it isn't just about an increase in speed or efficiency. It's also about making more intelligent decisions based on accurate information. The six Vs of big data are:
It is one of the most critical factors in Big Data. It is the amount of data that exists, and it's growing exponentially. In its simplest form, volume refers to the sheer amount of information humans create daily. However, its more subtle form also encompasses how quickly we create content. How much data can be generated with a single action (like taking a photo on your phone), and what kind of interactions are happening between people and their devices (social media).
The variety of data sources is a crucial feature of Big Data. While traditional analytics, for example, might rely on 10 or 20 structured data sources (such as customer records), Big Data can be produced by millions of users and devices daily. In addition to these open-source data sets, organizations are increasingly turning to social media sources such as blogs and Twitter feeds as additional sources of information about customers' preferences and behaviors.
Data veracity is essential in Big Data because it can affect the quality of insights you pull from your data set.
Veracity is how accurate, reliable, and valid the information in a dataset is. It's essential when working with transactional or financial data because you have to ensure that the records are correct and trustworthy before using them for analysis. Otherwise, your conclusions may be inaccurate or misleading.
Veracity has two parts:
Accuracy refers to whether all values are correct (i.e., no typos), while reliability refers to whether the data can be trusted (i.e., does it represent what was intended?). For example, someone enters a wrong amount on their credit card statement and then transmits that record into a database as accurate data. Then it would have low accuracy but high reliability because there's no way for anyone else to know whether they entered their expenses correctly or not.
Velocity is the speed of data generation. Companies must process and analyze massive amounts of data to gain value. However, the amount of data generated has increased exponentially in recent years and may double every two years by 2020. This means that the velocity of data generation is growing as well. As a result, companies must be able to access and analyze this information faster than ever—or risk losing out on valuable insights that could lead them to new business opportunities or innovative products and services.
Variability is all about how data is inconsistent. Data might be different from one user experience or device to another. It also changes over time, so what you see today might not be true tomorrow. And even though there are all these rules of thumb about collecting data properly and maintaining quality, there's no guarantee that your data will always be consistent with those rules. Who knows what kind of variability may arise if you're dealing with a new dataset or a new application of an old dataset?
Value is the most obvious of the six Vs and probably also the one that makes people think of big data as something new and exciting. But for many companies, value can be a bit of a misnomer because it implies that all you need to do is to evaluate the financial impact of your big data efforts.
A better way to think about value is in terms of insights, intelligence, and business outcomes:
Insights: If you're looking at how much money your company makes from sales leads generated by an online ad campaign, then we'd say you're focusing on insight. Insights are critical for understanding how well a marketing strategy works; they help guide future decisions about where to invest resources or make changes that will improve results.
Intelligence: If you know where most customers live but don't know anything else about them (for example), what good does knowing their location do? Intelligence gives us deeper context into who our customers are so we can tailor our strategies accordingly—and achieve better results in the long run. Intelligence helps us understand where someone lives and why they would be interested in our product or service based on demographics like age range or education level. Whether they own pets, have kids, or use social media often, and if so, which platforms do they use most frequently?
Big Data is the new way in which we live and work. However, it is not just about big data but combining different elements to make your business or lifestyle more efficient and effective. We will see a lot more in the future but for now, let's enjoy what we have today!
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This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.
© 2022 Hassan