Data Collection-Methods, Types and Techniques
How do you collect data?
Collecting data manually is an uphill climb. You have to search through files and lots of documents. It’s a hectic business.
So! What’s next?
Automation is around. You don’t need to tussle with offbeat practices. With automation, you can reserve time and money consistently. Moreover, you can put them in accomplishing potential growth& goals.
Does data collection only help in achieving prospects and targets?
Certainly, it’s essential, but not limited to the goals. You can harness them for procuring business intelligence. Let’s have a cursory lookover what you can get through data collection.
Why do you collect data?
There is a strong motto behind data pooling. Data collection, basically, is a set of datasets that you extract from various resources. Thereby, a pool of data gets generated. It’s what we call a data warehouse. Various outsourcing data mining companies exploit it for hitting the bull’s eye. That bull’s eye can be any of these:
A. Primary Data Collection for:
· Selling them to the third parties
· Validating authenticity
· De-duplication
· Standardisation
· Normalisation
B. Secondary Data Collection for:
· Determining unseen patterns
· Developing business intelligence
· Analyzing patterns
· Predicting strategies (sales/purchase/marketing/ operations)
· Comparative analysis
Types of Data Collection:
You can take data collected as a problem identifier and its savior. A data repository carries tons of information to understand the root cause of a business problem; where it stems from, what its causes are, what the hypothetical way is to resolve and how to test its effectiveness. These are a kind of milestone that you can achieve by distributing it into two types of data collection.
Have a look to know how they differ from each other:
| Points of Difference | Qualitative Data | Quantitative Data |
|---|---|---|
| Definition | These are about classifying data on the basis of their attributes and properties | They are expressed numerically that can be measured up. |
| Motive | Helps in developing initial & in-depth understanding | Helps in determining level of occurrence & final course of action |
| Type | Non-statistical explanatory | Statisticalconclusive data |
| Based On | Why? | How many or how much? |
| Data Size | Small sets of data | Large data repositories |
| Layout | Unstructured data | Structured data |
Data Collection Tools/Techniques/Methods:
| Techniques | Benefits |
|---|---|
| Interviews | · Face to face communication · Gather exact information · Clarity of facts · Encourage open-ended responses · Help in qualitative research & understanding |
| Questionnaire & Surveys | · Help in quantitative research · Numeric data pull analysis · Easy to assess & analyze · Develop platform for comparative analysis · Gather opinion |
| Observations | · Help in the study of dynamics and behaviour analysis · Avail resources to gather additional information of a group · Provide with qualitative & quantitative data |
| Focus Groups | · Help in extracting the opinions of a group · Ascertain combined perspectives and opinions · Help in categorizing and analyzing responses conclusively |
| Ethnographies/Oral History/Case Study | · Help in examining people in their cultural & natural setting · Prove valuable in observing, interviews and surveys · Evaluate holistic approach · Help in deriving insight |
| Documents & Records | · Handy in examining databases, meeting minutes, reports, attendance logs, financial records and newsletter etc.. · Inexpensive |
Online Searching Tricks for Data Collection:
| Online Searching Trick | How? | |
|---|---|---|
| a. | Use double quotes | What is “secondary research”? |
| b. | Asterisk within double quotes for unspecific variables | “* is harder than mountains” |
| c. | Define negative keywords using minus for excluding its prefix | Data re-consolidation |
| d. | Search website using “site:” | “site:eminenture.com” |
| e. | Use “Vs” for comparison | “Primary Research Vs Secondary Research” |
| f. | Google News archive | Select from News Archives |
| g. | “DEFINE:…” for searching meaning & slang | “DEFINE: Data Warehousing” |
| h. | Image search | Go to image menu of Google & click |
| i. | Click Mic icon for voice search |  Select for voice recognition near Google search bar |
Image Collection for Facial Recognition Data Mining:
Do you know about “10 Year Challenge”?
It’s a Facebook trend to scan the “you today” and “you a decade ago”. However, the participant netizens are happy to take this challenge for entertainment and nostalgia proposition. But, senior researchers and data scientists don’t take it mere a challenge. They see beyond it. That underlying reason can be a monitory benefit by selling it to the third parties.
· Data Theft-A Repercussion of Uninformed Online Data Collection:
The world has already witnessed the severe consequences of online surveys and polls on, let’s say, Facebook through Cambridge Analytica(CA) scam. Data analysts had harnessed and capitalized on their skills to pullout predictions. What they did was spying on other people using predictive analysis algorithms. They exploited it for commercial benefits and marketing purposes.
· Privacy Threat:
After the CA scandal, the user or data subject has become a silent observer. Even, the legislative body of the European Union has devised General Data Protection Regulation (GDPR). It has defined a thin line between data security and breaching. But still, the aforementioned challenges haveal ready triggered collection of multitudes of data. But, this practice may have some hidden intentions. That can be training algorithm for better understanding of human intentions.
But, it has pressed the alarm button for the data surveillance team. The biometrics can be breached if such algorithms would be evolved. If so happens, the footprints of cyber spies will be unstoppable. The bank details won’t be secured ever.
