What is Data Collection And What Are Its Uses?

EM Admin
Mar 30, 2019
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Data collection is a process of gathering and evaluating information on a variety of interests. It is streamlined in a well-defined layout to easily understand and answer the queries of research, test hypotheses, analysis and evaluating outcomes by the data collection company.

Data can be collected from multiple disciplines, like insurance, health, automotive, logistics and supply chain etc. for diverse industries. A unique motto underlies every data collection. It can be business or market research or gaining insights or analyzing trends through data mining. These are the purposes of data warehousing that define their usability.

Types of data and uses of their collection

1. Personal Data: It specifically refers to you. It can be your demographics, location, email address and other identifying factors.

Almost all banking, eCommerce, social networks and service providers sneak peek into your data. You might be wondering to know how it is possible. It doesn’t require any brainstorming. Just recall where you have shared your debit or credit card or a unique identity number. Sharing it means distributing personal data.

Although they churn personal information, they aim at providing you with personalised suggestions. Their ultimate motive is to consistently engage you. Facebook, banking and eCommerce apps are its finest examples. However, many companies extract data for selling it to advertising agencies and research companies. But, it’s not as easy as it were before the implementation of GDPR directives, which prohibit sharing of personal data sans willingness of the data subjects.

2. Transactional Data: These data are related to the action of monetory transactions. Your online purchases, card swiping for payment, utility and other bill payment through web or native applications tap on what transaction you did at a particular time.

The data miners collect such details through snooping in Google Analytics, another third party and in-house data extraction systems. This collection helps in combating challenges that generally stem in passive data collection methods.

The related companies come across variability. This is how they get some clues to optimize operations that don’t live up to expectations. The data scientist examines and recognizes hidden patterns and their correlations. This is how he recommends some game-changing business intelligence to insert cutting edge in the least yielding operations for invoking a competitive advantage. It helps to strategize an effective marketing and maximize revenue.

3. Sensor Data: It is pulled through the Internet of Things or the objects connected with the internet. It can be a smart watch, an elevator, a kiosk or mobile data. The sensor-fit electronic devices are trending to optimize business processes. Amazon, for example, leverages its customers in the US & the UK to come and take away from Amazon Go Stores whatever they like to have. The shopper should log into the app on their mobile phone to collect data. It can automatically detect when the products are picked up or returned to the shelves. The virtual cart keeps track of them. It means that the machines can collect data for you to make changes and appreciate productivity.

4. Web Data: Web data is the information that you pull from the World Wide Web for conducting market research or catching business insight. It could be related to online selling, government data or social networking data.

This data is a pool of information wherein intelligence resides. Simply put, it’s extremely informative. Diverse companies and government agencies can’t generate all kinds of information for research. It is crucial for creating productive business models. Those models ground up from the business intelligence decisions, which stem from what is happening internally and externally in the market.

The web data enable you to monitor competitors, track customers for upselling and cross selling, generate leads and a lot more. But, the biggest challenge is dealing with unstructured data. However, web scraping tools and techniques, like Python and R, are there to not only extract but also structure the data sets.

Importance of data collection in research methodology:

The accuracy is vital. It’s not necessary that the pan data are useful. A computer manufacturer would be interested in raw materials like chips, metal, circuits and cables-whatever is required for a final product. The information about aircrafts or web designing will not be his cup of tea because he doesn’t delve into it. So, maintaining accuracy should be a priority.

It’s not a walkover to collect flawlessly. The data entry staff should have access to appropriate data collection instruments. Besides, they should be well-informed with the protocols to reduce the likelihood of errors.

There is no need to brag about the consequences of inaccurate data. It is obvious that they adversely impact mining, as the analysis will be carried out on wrong information that often misleads. However, you can’t evaluate the degree of its impact from faulty data since it varies by disciplines. Eventually, what patterns you get, they produce disproportionate recommendations because they comprise: