Data Collection, Just Another Way To Gather Information

Data collection just does not help the companies to launch new products or know about the public reaction to a specific issue, it is a very useful tool for statistical inferences, once the collected data is compiled. The process of data collection is the third step of the six step market research processes. Data collection can be done in two ways involving various technicalities. In this article, we shall give a brief overview of the same.

Data collection can be done in two ways - secondary data and primary data. Secondary data collection involves is the information available in books, journals, previous researches or studies and the Internet. It basically involves making use of the data already present to build or substantiate a concept.

On the other hand, primary data collection is the process of data collection through questionnaire by directly asking respondents of their opinions. Forming the right questionnaire is the most important aspect of data collection. The researcher conducting the data collection just has to be aware of the process. He should have a clear idea about the information sought by the concerned party.

Besides, the data collection officer should be able to construct the questionnaire in such a way so as to elicit the responses needed. Having constructed the questionnaire the researcher should identify the target sample. To illustrate the point clearly, we shall look into the following example.

Suppose, data collection is aimed from an area A, then, if all the residents of the data are given the questionnaire, it is called a census or in other words data collection is done from all the individuals of the specified area. One of the most common examples of data collection done by the government is census. For example the population census conducted by the US Census Bureau every ten years. On the other hand, if only twenty or thirty percent of the population living in area A are given the questionnaire, the mode of data collection would be called sampling.

The data collected from the target sample with a well-defined questionnaire will project the response of the entire population living in the area. Data collected from a sample helps to control the cost and time spent on collecting data from the population. Sample is a part of population.

Data collection just gets easier from the target sample with the help of a pretested questionnaire, which is later analyzed using statistical tests like ANOVA, Chi Square test and so on. These tests help the researcher to infer the result obtained from the data collection.

Market research/data collection is a fast growing and lucrative career option now days. One has to undertake a course in marketing, statistics and research before starting out. It is indeed very important to have a through understanding of various concepts and the theories related. Some basic terminologies related to data collection are: census, incidence, sample, population, parameters, sampling frames and so on.

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Data Collection - Make a Plan

Planning for the data collection activity provides a stable and reliable data collection process in the Measure phase.

A well-planned activity ensures that your efforts and costs will not be in vain. Data collection typically involves three phases: pre-collection, collection and post-collection.

Pre-collection activities: Goal setting and forming operational definitions are some of the pre-collection activities that form the basis for systematic and precise data collection.

1.  Setting goals and objectives: Goal setting and defining objectives is the most important part of the pre-collection phase.

It enables teams to give direction to the data to be collected. The plan includes description of the Six Sigma project being planned. It lists out specific data that is required for the further steps in the process.

If there are no specific details as to the data needs, the data collection activity will not be within scope - and may become irrelevant over a period of time.

The plan must mention the rationale of data being collected as well as the final utilization.

2.  Define operational definitions: The team must clearly define what and how data has to be collected. An operational definition of scope, time interval and the number of observations required is very important.

If it mentions the methodology to be used, it can act a very important guideline to all data collection team members.

An understanding of all applicable information can help ensure that there no misleading data is collected, which may be loosely interpreted leading to a disastrous outcome.

3.  Repeatability, stability and accuracy of data: The repeatability of the data being collected is very important.

This means that when the same operator undertakes that same activity on a later date, it should produce the same output. Additionally, it is reproducible if all operators reach the same outcome.

Measurement systems should be accurate and stable, such that outcomes are the same with similar equipment over a period of time.

The team may carry out testing to ensure that there is no reduction in these factors.

Collection Activity

After planning and defining goals, the actual data collection process starts according to plan. Going by the plan ensures that teams achieve expected results consistently and accurately.

Training can be undertaken so as to ensure that all data collection agents have a common understanding of data being collected. Black Belts or team leaders can look over the process initially to provide any support needed.

For data collection over a longer period, teams need to ensure regular oversight to ensure that no collection activities are overlooked.

Post collection activities

Once collection activities are completed, the accuracy and reliability of the data has to be reviewed.

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Various Methods of Data Collection

Professionals in all the business industries widely use research, whether it is education, medical, or manufacturing, etc. In order to perform a thorough research, you need to follow few suitable steps regarding data collection. Data collection services play an important role in performing research. Here data is gathered with appropriate medium.

Types of Data

Research could be divided in two basic techniques of collecting data, namely: Qualitative collection of data and quantitative collection. Qualitative data is descriptive in nature and it does not include statistics or numbers. Quantitative data is numerical and includes a lot of figures and numbers. They are classified depending on the methods of its collection and its characteristics.

Data collected primarily by the researcher without depending on pre-researched data is called primary data. Interviews as well as questionnaires are generally found primary data/information collection techniques. Data collected from other means, other than by the researcher is secondary data. Company surveys and government census are examples of secondary collection of information.

Let us understand in detail the methods of qualitative data collection techniques in research.

Internet Data: Here there is a huge collection of data where one gets a huge amount of information for research. Researchers remember that they depend on reliable sources on the web for precise information.

Books and Guides: This traditional technique is authentically used in today's research.

Observational data: Data is gathered using observational skills. Here the data is collected by visiting the place and noting down details of all that the researcher observes which is needed for essential for his research.

Personal Interviews: Increases authenticity of data as it helps to collect first hand information. It does not serve fruitful when a big number of people are to be interviewed.

Questionnaires: Serves best when questioning a particular class. A questionnaire is prepared by the researcher as per the need of data-collection and forwarded to responders.

Group Discussions: A technique of collecting data where the researcher notes down details of what people in a group has to think. He comes to a conclusion depending on the group discussion that involves debate on topics of research.

Use of experiments: To obtain the complete understanding researchers conduct real experiments in the field used mainly in manufacturing and science. It is used to obtain an in-depth understanding of the researching subject.

Data collection services use many techniques including the above mentioned for collection. These techniques are helpful to the researcher in drawing conceptual and statistical conclusions. In order to obtain precise data researchers combine two or more of the data collection techniques.

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How We Optimized Our Web Crawling Pipeline for Faster and Efficient Data Extraction

Big data is now an essential component of business intelligence, competitor monitoring and customer experience enhancement practices in most organizations. Internal data available in organizations is limited by its scope, which makes companies turn towards the web to meet their data requirements. The web being a vast ocean of data, the possibilities it opens to the business world are endless. However, extracting this data in a way that will make sense for business applications remains a challenging process.

The need for efficient web data extraction

Web crawling and data extraction is something that can be carried out through more than one route. In fact, there are so many different technologies, tools and methodologies you can use when it comes to web scraping. However, not all of these deliver the same results. While using browser automation tools to control a web browser is one of the easier ways of scraping, it’s significantly slower since rendering takes  a considerable amount of time.

There are DIY tools and libraries that can be readily incorporated into the web scraping pipeline. Apart from this, there is always the option of building most of it from scratch to ensure maximum efficiency and flexibility. Since this offers far more customization options which is vital for a dynamic process like web scraping, we have a custom built infrastructure to crawl and scrape the web.

How we cater to the rising and complex requirements

Every web scraping requirement that we receive each day is one of a kind. The websites that we scrape on a constant basis are different in terms of the backend technology, coding practices and navigation structure. Despite all the complexities involved, eliminating the pain points associated with web scraping and delivering ready-to-use data to the clients is our priority.

Some applications of web data demand the data to be scraped in low latency. This means, the data should be extracted as and when it’s updated in the target website with minimal delay. Price comparison, for example requires data in low latency. The optimal method of crawler setup is chosen depending on the application of the data. We ensure that the data delivered actually helps your application, in all of its entirety.

How we tuned our pipeline for highly efficient web scraping

We constantly tweak and tune our web scraping infrastructure to push the limits and improve its performance including the turnaround time and data quality. Here are some of the performance enhancing improvements that we recently made.

1. Optimized DB query for improved time complexity of the whole system

All the crawl stats metadata is stored in a database and together, this piles up to become a considerable amount of data to manage. Our crawlers have to make queries to this database to fetch the details that would direct them to the next scrape task to be done. This usually takes a few seconds as the meta data is fetched from the database. We recently optimized this database query which essentially reduced the fetch time to merely a fraction of seconds from about 4 seconds. This has made the crawling process significantly faster and smoother than before.

2. Purely distributed approach with servers running on various geographies

Instead of using a single server to scrape millions of records, we deploy the crawler across multiple servers located in different geographies. Since multiple machines are performing the extraction, the load on each server will be significantly lower which in turn helps speed up the extraction process. Another advantage is that certain sites that can only be accessed from a particular geography can be scraped while using the distributed approach. Since there is a significant boost in the speed while going with the distributed server approach, our clients can enjoy a faster turnaround time.

3. Bulk indexing for faster deduplication

Duplicate records is never a trait associated with a good data set. This is why we have a data processing system that identifies and eliminates duplicate records from the data before delivering it to the clients. A NoSQL database is dedicated to this deduplication task. We recently updated this system to perform bulk indexing of the records which will give a substantial boost to the data processing time which again ultimately reduces the overall time taken between crawling and data delivery.

Bottom line

As web data has become an inevitable resource for businesses operating across various industries, the demand for efficient and streamlined web scraping has gone up. We strive hard to make this possible by experimenting, fine tuning and learning from every project that we embark upon. This helps us maintain a consistent supply of clean, structured data that’s ready to use to our clients in record time.

Source:https://www.promptcloud.com/blog/how-we-optimized-web-scraping-setup-for-efficiency