Where does data come from?

We live in a world of connected data. The data we see today were very different from information 10,20 years ago. Nowadays, you can’t define data as structured relational tables. Today data is much more complex and cannot be limited to one source only. It is exploding from devices and products and actions.

Data is the main driving asset in data science. It plays an enormous role in how we use raw and complex information to expand our business. We collect data to make useful products and services. We take this massive amount of information and organize it in a valuable way. But the question is, where does all this data come from?

We can place data in two different categories based on where it comes from.

Traditional data

Traditional data is the type of data that arranged and kept in databases. There, data analysts manage it typically from one device only. This type of data often comes in a table format. It has numbers or text values. The traditional kind is mainly stored in relational database management systems. What that means is that it takes time to structure and process. Standard data comes primarily from old and current customer records. It cannot receive data that comes from unorganized structures.

Big data

Big data, on the other hand, comes from various disorganized sources. Emails from devices, photos, videos, customer service insights, temperature, and so much generate big data. Data scientists use big data as a way to collect past and current information in order to predict future behavior patterns. They take this vast source of clutter and organize it in a way that will accurately give real-time analytics.

So, let’s closely analyze where big data comes from and why it is valuable to know.

There are three primary sources of significant data origins:

1. Social or Public Data
2. Data from physical products (Machine Data)
3. Transactional data

1. Social or Public Data

This is the most massive income of data. This is the type of data that comes from your social activity on the Internet. Organizations use your likes, web visits, comments, and search history to get information about your consumer behavior. This is extremely useful in marketing analytics. They take all the public data to create statistics and know what kind of patterns people of similar demographics, nationality, etc. display. This is also used to create personal ads and to suggest products and goods according to your needs.

2. Data from physical products (Machine Data)

This is the type of information that an existing product generates from you. Sensors are integrated into the product as a way to collect data. Once they start working, they collect records about the user’s behavior, habits, and preferences. Not only that, but that same data developers use it to improve the product itself. Self-driving cars use data to train themselves and to get better at finding locations and such. Satellites, road cameras, and even medical devices use human statistics to improve the life of people. They transfer data in microseconds and always give real-life analyses.

3. Transactional data

Like the name itself, transactional data comes from daily money transfers we make. This means that data is collected from both online and offline transactions. These transactions are mostly put in commercial use. Debits, returns, invoices, contracts, trades are just a fraction of that can be counted as transactional data. We can divide transactional data into three groups:

• Financial: orders, invoices, payments
• Work: plans, activity records
• Logistics: deliveries, storage records, travel records, etc.

So what can you do with transactional data?

We can use transactional data to forecast a sale. All transactions have a time period when they have been made. When we collect this type of data, we can have an insight into when and how much people spend money. Then, we can use that information to create a strategically set sale. It can be a weekly or a monthly deal—all that by just knowing the time frame of transactions.

We can also use data mining techniques for customers. We place customers into different customer groups. The name better knows this of customer segmentation.

The whole idea behind customer segmentation is to place individuals into similar groups. All of that is based on their demographic, gender, finances, and interests. This method an effective way to allocate resources. And all that only by using data. Companies and other marketing organizations are the ones that use transactional data for growth purposes.

Transactional data is also useful for improving products and their prices. Many companies can see what the average price range of products people can afford is based on purchases. They can also see what their customer’s needs are solely, thus creating a whole demand-supply cycle.

Big data is valuable. It helps us to build better products, services, and goods. We can use that same data to create more precise results and add a more personal experience to users. We use big data to develop innovative ways to get products closer to people. We use that same information to manage human resources and to improve human life. In the days of technology, data is the most crucial device you should learn to use. If humans are the center of the physical world, then information is the online world center.

References:

https://www.sciencedirect.com/topics/computer-science/transactional-data
https://www.youtube.com/watch?v=Ypq-URGmNws&ab_channel=BusinessScience
https://towardsdatascience.com/why-big-data-bf0d65933782
https://www.cloudmoyo.com/blog/data-architecture/what-is-big-data-and-where-it-comes-from/
https://www.kdnuggets.com/2018/06/what-where-how-data-science.html
https://towardsdatascience.com/why-big-data-bf0d65933782