Big data is a topic thrown around tech companies that people may not quite understand what it really means. We know it’s good, and we want it, but what is it really?
We’re going to review and discuss precisely what big data is in simple terms so that you’ve got a fundamental understanding of what big data is and why it matters to organizations.
What is Big Data?
Big data is large and diverse information that is collected from multiple sources and is ever-increasing. It most often comes from data mining efforts and comes in all kinds of different formats. Thus, people often refer to the three v’s of big data, the massive volume of information, the velocity it is created and collected, and the variety of data collected.
How does Big Data work?
Big data is broken down into structured and unstructured data. Structured data is information that organizations already control, format, and store in databases. Unstructured data has been data mined from various sources and does not conform to any specific format. This type of information is often dynamic and determined by specific customer requirements.
Big data can be collected from a variety of sources. It can be pulled from social media or other websites; you can request the information through forms people fill out for free products or when purchasing products from stores. You can even gather information by using tracking or information from apps people install on their phones or computers and permit their data to be shared in exchange for using the application.
All information is pulled from its original source and stored in a large database where specialized applications will go to work to analyze the data. Several SaaS companies have sprung up to help organizations pull and analyze this information due to the complexity of doing so, and specialized teams are required to get the most out of the data.
What do you use Big Data for?
Data analysts and the specialized software available for big data look at the correlation between data such as who is buying certain products and what other products they’ve purchased before. Of course, it’s not important what data is being compared, but the point is that with a huge range of data available, you can compare and make decisions based on that data.
Many companies like Facebook, Twitter, TikTok, and any company that sells ads will use big data to use targeted ads at users. So, for example, instead of giving everybody an ad about buying a new computer game, information about what the user has previously viewed, clicked, and potentially other external data that has been data-mined will be used to give them an ad that they are more likely to be interested in and click on.
Any department in an organization can use big data, though not all of them do just yet. However, big data’s primary goal is to get products to market as quickly as possible, reduce time to market and sell your product, target appropriate users that actually want your product, and make sure customers are happy and come back for more products.
What are the pros and cons of big data?
Big data provides an opportunity for companies to pull huge amounts of data about their users, customers, and potential customers so that they can effectively make more money and make it faster. They can ensure that the most appropriate product or information is provided to the right people and not waste ad revenue or potential sales that will never happen.
With so much information being pulled and not all of it being useful, the data can create noise and confusion in some situations. Your software must be able to handle and filter the information to ensure the correct data is stored together and discard data that is not relevant. When dealing with the amount of data available, this can be a challenging task, and not all companies do it as well as others.
A significant issue can be that companies pulling all of this information together have a requirement to keep it safe. In addition, a lot of the data is personal identifying information (PII) which can cause significant legal headaches if made available to the general public or for less scrupulous individuals to use.
As more and more data becomes available, the complexity of analyzing and sorting it becomes more complex as time goes on. You may be able to pull and store the information, but it’s becoming a trickier situation to actually use it. This requires more and more effort and specialized programming, which takes time and money.
Conclusion
We’ve seen that big data provides opportunities for companies to collate and analyze data from a variety of sources. The information collected can be from sources within the company such as customer orders, application tracking that customers have agreed to, and even website traffic that users create when viewing and clicking within applications or the company’s websites.
Other information is data-mined from various sources, and this is the most challenging data to analyze. While it’s easy to pull the data in, specialized software needs to take it, format it, and store it with the right collective of data. This is essentially mining personal information about users from social media and other sources so they can store it with the information they already have about the users.
The information is then used to make money through targeted advertising or other methods to ensure the right people see the right products and make purchases. This not only increases profits for companies but allows them to decide on which products may do well or will only do well in specific markets.
Big data for companies is great, but many users feel big data may be going too far. They don’t necessarily like to be tracked, and in some cases, the authorization to track is hidden in legal terms and conditions the user is not fully aware of. So it’s a fine line between big data and invasion of privacy, and companies need to tread a fine line not to turn users away from them.
