Data has become essential in today’s world. More specifically, big data has become very important for the way business organizations operate. It’s simply not possible any longer for companies to make strategic decisions based on “business experience”.
Companies need to gather and analyze large volumes of data that can help them understand what the best course of action is. However, it’s not easy to do – gathering data is a complex process that requires time and knowledge.
Here’s what you need to know about data gathering, what structure it needs to have, its challenges, and how to overcome these issues.
What is Data Gathering?
Data gathering, data collection, or data scraping is a systematic gathering, analyzing, and measuring of insights with predetermined sets of techniques. Businesses can evaluate their potential moves and strategies using data.
Data gathering is an essential step for data research, regardless of the industry a business is in. The most crucial goal of any data gathering process is to ensure that the data collected is reliable and carries relevant information that you can use during analysis to come off with valuable conclusions.
There are different approaches to data collection depending on what kind of information is needed and how it needs to be structured. Here are some of the standard data gathering approaches:
- Surveys
- Phone calls
- Interviews
- Web scraping
How Data Helps Businesses
Data helps companies understand their processes from within. At the same time, data can help you understand the aspects outside of a business relevant to its performance. There are all kinds of data that companies can use to their advantage.
Data is often used to understand a target market. It can be related to consumers, how they behave online, what their demographics are, what kind of customer experience they need, market demand, competitor performance, etc.
Companies can learn about basically anything, including product prices, how others are marketing themselves, which sites perform the best, and so much more. It’s all possible through gathering publicly available data. All that information can guide organizations in the right direction and help them boost their results.
Challenges of Gathering Data
The issue with data gathering is that it’s not always possible to extract data in a structured way. Unstructured data isn’t stored logically within databases, and there’s no organization in terms of categories like addresses, names, types of products, etc.
It’s a form of free information that gets extracted from websites, social media posts, emails, and so on. Even though this data is unstructured, it doesn’t mean that it doesn’t have valuable information. However, it can be difficult to analyze it, and turning it into something structured could take a lot of time.
Luckily there is a solution to this problem, and it’s called data parsing. This process makes unstructured data more readable and ready for use.
What is parsing?
It’s a process that converts strings of data from one type into another. For example, if you’ve gathered raw HTML data, data parsing can be used for turning it into something understandable. A good parser tool will recognize what HTML information is required and convert it into a table, CSV, or JSON format.
Parsers have rules and codes to perform these operations, and they aren’t tied to a single format. They can be designed to turn all kinds of formats into something else. It just depends on the purpose for which the parser was built.
So what is parsing? It’s basically transforming one type of data into another. Parsers can work with HTTP, script and modeling languages, various databases like SQL, XML, programming languages, and interactive data language.
How Data Parsing Helps Gather Data
Now that we’ve answered what parsing is, let’s see how it helps the whole data gathering process. First of all, data parsing automates data transformation. When you set up the right algorithm and process, everything is done automatically.
The process saves a lot of time on tasks that you would have to do manually. Additionally, it also saves money that would have to be spent on so much manual work. Data parsing makes data accessible and searchable. Everything is presented neatly, making the data easy to read and manage.
In the end, lots of companies use old data that has outdated formats. A data parsing format lets companies modernize their data and turn it into a usable format for the future.
Conclusion
If you want your business to be ready for future challenges, you must rely on data. Analytics can help you make informed decisions and make the most out of your investments. Data parsing is an integral aspect of data gathering and analytics.