Data Collection Techniques for a Successful Thesis

Irrespective of the grade of the topic and the subject of research you have chosen, basic requirement and process of all remains same i.e. "research". Re-search in itself means searching on a searched content and this involves some proven fact along with some practical figures reflecting the authenticity and reliability of the study. These facts and figures which are required to prove the fundamentals of study are known as "data's".

These data's are collected according to the demand of research topic and its study undertaken. Also their collection techniques vary along with the topic in detail for example if the topic is like "Changing era of HR policies", the demanded data would be subjective and its technique thus depends on the same. Whereas if the topic is like "Causes of performance appraisal", then the demanded data would be objective and in the terms of figures which shows different parameters, reasons and factors affecting performance appraisal of different number of employees. So, let's have a broader look on the different data collection techniques which gives a reliable ground to your research -

• Primary Technique - Here, the data is collected by the first hand source directly are known as primary data's. Self-analysis is a sub classification of primary data collection - As understood; here you get self-response for a set of questions or a study. For example - personal in-depth interviews and questionnaires are self-analyzed data collection techniques, but its limitation lies in the fact that self-response can be sometimes biased or even confused. On the other, hand the advantage is in the court of most updated data as it is directly collected from the source.

• Secondary Technique - In this technique the data is collected from the pre-collected resources they are called as secondary data's. Data's are collected from articles, bulletins, annual reports, journals, published papers, government and non-government documents and case studies. Limitation of these is that they may not be the updated one or may be manipulated as it is not collected by the researcher itself.

Secondary data is easy to collect as they are pre-collected and are preferred when there is lack of time whereas primary data's are tough to amass. Thus, if researcher wants to bring up to date, reliable and factual data's they should prefer primary source of collection. But, these data collection techniques vary according to problem generated in the thesis. Hence, go through the demands of your thesis first before indulging yourself into data collection.

Source: http://ezinearticles.com/?Data-Collection-Techniques-for-a-Successful-Thesis&id=9178754

Google Sheets vs Web Scraping Services

Google Sheets vs Web Scraping Services

Ever since the data on the web started multiplying in terms of quantity and quality, people have sought out ways to scrape or extract this data for a wide range of applications. Since the scope of extraction was limited back then, the extraction methods mostly comprised of manual methods like copy-pasting text into a local document.

As businesses realized the importance of web scraping as a big data acquisition channel, new technologies and tools surfaced with advanced capabilities to make web scraping easier and efficient.

Today, there are various solutions catering to the web data extraction requirements of companies; DIY tools to managed web scraping services are out there and you can choose one that suits your requirements the best.

Scraping using Google sheets

As we mentioned earlier, there are so many different ways to extract data from the web although not all of these would make sense from a business point of view. You can even use Google docs to extract data from a simple HTML page if you are looking to understand the basics of web scraping. You could check out our guide on using google sheets to scrape a website if you want to learn something that might come handy.

However, Google docs and other web data extraction tools come with their own limitations. For starters, tools aren’t meant for large-scale extraction which is what most businesses will require. Unless you are a hobbyist looking to extract a few web pages for tinkering with a new data visualization tool, you should steer clear from web scraping tools. Scraping tools cannot cater to the requirements of a business as it could be well out of their capabilities.

Enterprise-grade web data extraction

Web scraping is only a common term for the process of saving data from a web page to a local storage or cloud. However, if we consider the practical applications of the data, it’s obvious that there’s a clear distinction between mere web scraping and enterprise-grade web data extraction.

The latter is more inclined towards the extraction of data from the web for real-world applications and hence requires advanced solutions that are built for the same. Following are some of the qualities that an enterprise-grade web scraping solution should have:

- High-end customization options
- Complete automation
- Post-processing options to make the data machine-ready
- Technology to handle dynamic websites
- Capability of handling large-scale extraction

Why DaaS is the best solution for enterprise-grade web scraping

When it comes to extracting data for business use cases, there should be a stark difference in the way things are done. The speed and efficiency matters more in the business world and this demands a managed web scraping solution that takes the complexities and pain points out of the process to provide companies with just the data they need, the way they need it.

Data as a Service is exactly what businesses that are looking to extract web data without losing focus on their core business operations need. Web crawling companies like PromptCloud, that work on the DaaS model does all the heavy lifting associated with extracting web data and deliver only the needed data to the companies in a ready-to-use format.

Source:-https://www.promptcloud.com/blog/google-sheets-vs-web-scraping-services

A guide to data scraping

Data is all the rage these days.

It’s what businesses are utilizing to create an unfair advantage with their customers. The more data you acquire, the easier it becomes to slice it up in a unique way to solve problems for your customers.

But knowing that data can benefit you – and actually getting the data – are two separate items.
Much like technology, data can catapult you to greater heights, or it can bring you to your knees.
That’s why it is essential to pay careful attention and ensure the data you use propels you forward versus holding you back.

Why all data isn’t created equal

The right data can make you a hero. It can keep you at the forefront of your industry, allowing you to use the insights the information uncovers to make better decisions.

Symphony Analytics uses a myriad of patient data from a variety of sources to develop predictive models, enabling them to tailor medication schedules for different patient populations.

Conversely, the wrong data can sink you. It can cause you to take courses of action that just aren’t right. And, if you take enough wrong action based upon wrong data, your credibility and reputation could suffer a blow that’s difficult to recover from.

For instance, one report from the state of California auditor’s office shows that accounting records were off by more than $6 million due to flawed data.

That’s no good. And totally avoidable.

As a result, it is critical you invest the energy in advance to ensure the data you source will make you shine, rather than shrink.
How to get good data

You’ve got to plan for it. You’ve got to be clear about your business objectives, and then you’ve got to find a way to source the information in a consistent and reliable manner.

If your business’ area of expertise is data capture and analysis, then gathering the information you need on your own could be a viable option.

But, if the strength of you and your team isn’t in this specialized area, then it’s best to leave it to the professionals.

That’s why brands performing market research on a larger scale often hire market research firms to administer the surveys, moderate focus groups or conduct one-on-one interviews.

Of late, more companies are turning to data scraping as a means to capture the quantitative information they need to fuel their businesses. And they frequently turn to third-party companies to supply them with the information they need.

While doing so allows them to focus on their core businesses, relinquishing control of a critical asset for their businesses can be a little nerve-racking.

But, it doesn’t have to be. That is if you work with the right data scraping partner.

How to choose the right data scraping partner for you
In the project management world, there’s a triangle that is often used to help prioritize what is most important to you when completing a task.

Data Scraping Group: Good, Fast, Cheap - Pick any two

Although you may want all three choices, you can only pick two.

If you want something done fast, and of good quality, know that it won’t be cheap. If you want it fast and cheap, be aware that you will sacrifice quality. And if you’d like it to be cheap and good, prepare to wait a bit, because speed is a characteristic that will fall off the table.

There are many 3rd party professionals who can offer data scraping services for you. As you begin to evaluate them, it will be helpful to keep this triangle in mind.
Here are six considerations when exploring a partner to work with to ensure you get high-quality
web crawling and data extraction.

1. How does the data fit into your business model?

This one is counter intuitive, but it’s a biggie. And, it plays a major role as you evaluate all the other considerations.

If the data you are receiving is critical to your operations, then obtaining high-quality information exactly when you need it is non-negotiable. Going back to the triangle, “good” has to be one of your two priorities.

For instance, if you’re a daily deal site, and you rely on a third party to provide you all the data for the deals, then having screw-ups with your records just can’t happen.
That would be like a hospital not staffing doctors for the night. It just doesn’t work.
But, if the data you need isn’t mission critical for you to run your business, you may have a little more leeway in terms of how you weight the other factors that go into choosing who best to work with.

2. Cost

A common method numerous businesses use to evaluate costs is just to evaluate vendors based on the prices they quote.

And, too often, companies let the price ranges of the service providers dictate how much they are willing to pay.

A smarter option is to determine your budget in advance … before you even go out to explore who can get you the data you need. Specifically, you should decide how much you are able and willing to pay for the information you want. Those are two different issues.
Most businesses don’t enjoy unlimited budgets. And, even when the information being sourced is critical to operating the business, there is still a ceiling for what you’re able to pay.
This will help you start operating from a position of strength, rather than reacting to the quotes you may receive.

Another thing to consider are the various types of fees. Some companies charge a setup fee, followed by a subsequent monthly fee. Others charge fixed costs. If you’re looking at multiple quotes from vendors, this could make it difficult for you to compare.
A wise way to approach this is to make sure you are clear on what the total cost would be for the project, for whatever specified time period you’d like to contract with someone.
Here are a few questions to ask to make sure you get a full view of the costs and fees in the estimate:

-Is there a setup fee?
-What are the fixed costs associated with this project?
-What are the variable costs, and how are they calculated?
-Are there any other taxes, fees, or things that I could be charged for that are not listed on this quote?
-What are the payment terms?

3. Communication

Even when you’ve got a foolproof system that runs like a well-oiled machine, you still need to interact with your vendors on a regular basis. Ongoing communication confirms things are operating the way you’d like, gives you an opportunity to discuss possible changes and ensures your partner has a firm understanding of your business needs.

The data you are sourcing is important to you and your business. You need to partner with someone who will be receptive to answering questions and responding in a timely manner to inquiries you have.

4. Reputation

This was mentioned before, but it’s worth repeating. All data is not created equal. And, if you are utilizing data as a means to build and grow your business, you need to make sure it’s good.

So, even though data scraping isn’t your area of expertise, it will greatly benefit you to spend time validating the reputation the people vying to deliver it to you.

How do they bake quality in their work? Do they have any certifications or other forms of proof to give you further confidence in their capabilities? Have their previous customers been pleased with the quality of the data they’ve delivered?

You could do so by checking reviews of previous customers to see how pleased they were and why. This method is also helpful because it may assist you in identifying other important criteria that may not have been on your radar.

You could also compare the credentials of each of the vendors, and the teams who will actually be working on your project.

Another highly-effective way could be to simply spend time talking to your potential partners and have them explain to you their processes. While you may not understand all the lingo, you could ask them a few questions about how they engage in quality control and see how they respond.

You’d probably be shocked at the range of answers you get.

Here are a few questions to guide you as you start asking questions about their quality system:

- Are the data spiders customized for the websites you want information from?
- What mechanisms are in place to verify the harvested data is correct?
- How is the performance of the data spiders monitored to verify they haven’t failed?
- How is the data backed up? Is redundancy built into the process so that information is not lost?
- Is internet access high-speed, and how frequently is it monitored?

5. Speed

For those suppliers that are able to deliver data to you fast, make sure you understand why they are able to deliver at such a rapid speed. Are there special systems they have in place that enable them to do so? Or perhaps, is there any level of quality that is sacrificed as a result of getting you information fast.

Often when contracting with a data extraction partner, they’ll deliver your information on a set schedule that you both agree upon.

But, there may be times when you need information outside of your normal schedule, and you may even need it on a brief timeline.

Knowing in advance how quickly your partner is able to turn around a request will help you better prepare project lead times.

6. Scalability

The needs of your business change over time. And, as you work to grow, it is quite possible the data needs of your company will expand as well.

So, it’s helpful to know your data scraping partner is able to grow with you. It would be great to know that as the volume, and perhaps the speed of the information you need to run your business increases, the company providing it is able to keep pace.

Don’t get stuck with bad data
It could spell disaster for your business. So, make sure you do your due diligence to fully vet the companies you’re considering sourcing your data from.
Make a list of requirements in advance and rank them, if necessary, in order of importance to you.
That way, as you begin to evaluate proposals and capabilities, you’ll be in a position to make an informed decision.
You need good data. Your customers need you to have good data, too.
Make sure you work with someone who can give it to you.

Source url :-http://www.data-scraping.com.au/techniques-for-high-quality-web-crawling-and-data-extraction

Scraping Dynamic Websites: How We Tackle the Problem

Scraping Dynamic Websites: How We Tackle the Problem

Acquiring data from the web for business applications has already gained popularity if we look at the sheer number of use cases. Companies have realized the value addition provided by data and are looking for better and efficient ways of data extraction. However, web scraping is a niche technical process that takes years to master given the dynamic nature of the web. Since every website is different and custom coded, it’s not possible to write a single program that can handle multiple websites. The web scraping setup should be coded separately for each target site and this needs a team of skilled programmers.

Web scraping is without doubt a complex trade; however if the target site in question employs dynamic coding practices, this complexity is further multiplied. Over the years, we have understood the technical nuances of web scraping and perfected our modus operandi to to scrape dynamic websites with high accuracy and efficiency. Here are some ways how we tackle the challenge of scraping dynamic websites.

1. Proxies

Some websites have different Geo/Device/OS/browser specific versions that they serve depending on the variables. This could give a great deal of confusion to the crawlers especially while figuring out how to extract the right version. This will need some manual work in terms of finding the different versions provided by the site and configuring proxies to fetch the right version as per the requirement. For geo-specific versions, the crawler is simply deployed on a server from where the required version of the site is accessible.

2. Browser automation

When it comes to websites that use very complex and dynamic code, it’s better have all the page content rendered using a browser first. Selenium can be used for browser automation which will help us do the scraping. It is essentially a handy toolkit that can drive the browser from your favorite programming language. Although it’s primarily used for testing, it can be used for scraping dynamic web pages. It can be used to control a web browser, which is how scraping using selenium is typically done. In this case, the browser first renders the page which will help overcome the problem of reverse engineering JavaScript code to fetch the page content. Once the page content is rendered, it is saved locally to scrape the required data points later. Although this is comparatively easy, there is a high chance of encountering errors while scraping using the browser automation method.

3. Handling POST requests

Many web pages will only display the data that we need after receiving a certain input from the user. Let’s say you are looking for used cars data from a particular geo-location on a classified site. The website would first require you to enter the ZIP code of the location from where you need listings from. This ZIP code must be sent to the website as a post request while scraping. We craft the post request using the appropriate parameters so as to reach the target page that contains all the data points to be scraped.

4. Manufacturing the JSON URL

There are dynamic web pages that use AJAX calls to load and refresh the page content. These are particularly difficult to scrape and extract data from as the triggers that make up the JSON file is difficult to trace. This requires a lot of manual inspection and testing, but once the appropriate parameters are identified, a JSON file that would fetch the target page which includes the desired data points can be manufactured. This JSON file is often tweaked automatically for navigation or fetching varying data points. Manufacturing the JSON URL with apt parameters is the primary pain point with web pages that use AJAX calls.
Bottom-line

Scraping dynamic web pages is extremely complicated and demands deep expertise in the field of web scraping. It also demands an extensive tech stack and well-built infrastructure that can handle the complexities associated with web data extraction. With our years of expertise and well-evolved web scraping infrastructure, we cater to data requirements where dynamic web pages are involved on a daily basis.

Source:https://www.promptcloud.com/blog/scraping-dynamic-websites-web-scraping

How Data Scraping Help Businesses?

Gathering data from diverse internet sources like website and others, the process is called as data scraping. Around the globe such and many describe data scraping as web scraping, data harvesting. Now days the competition is very high in every business and for that the companies required to collect more useful data for their business. 

Research market trends and extracting different types of data is necessary today’s. Data scraping is one of the latest technology that collect diverse data from internet source and make use in the analysis.

By using data scraping any one can quickly classify the any kind of information and also make decision and marketing strategies. Reducing risk and also improving business profit are other advantages of data scraping. Scraping data from website by manually and also using data scraper, website scraper and website data scraper tools.

Now you want to get data scraping solutions for your business?The company offers lowest industry rate data scraping, web data scraping and website data scraping services as the need of clients with never compromise on quality and fast turn around time. For further details about the company send query at info@www.web-scraping-services.com.


Source Url : -http://3idatascraping.weebly.com/blog/how-data-scraping-help-businesses

Applications of Web Data Extraction in Ecommerce

web data mining ecommerceWe all know the importance of data generated by an organisation and its application in improvement of product strategy, customer retention, marketing, business development and more. With the advent of digital age and increase in storage capacity, we have come to a point where the internal data generated by an organisation has become synonymous with Big Data. But, we must understand that by focusing only on the internal data, we are losing out another another crucial source – the web data.

Pricing Strategy

This is one of the most common use cases in Ecommerce. It’s important to correctly price the products in order to get the best margins and that requires continuous evaluation and remodeling of pricing strategy. The very first approach takes into account market condition, consumer behavior, inventory and a lot more. It’s highly probable that you’re already implementing such type of pricing strategy by leveraging your organisational data. That said, it’s also equally important to consider the pricing set by the competitors for similar products as consumers can be price sensitive.

We provide data feeds consisting of product name, type, variant, pricing and more from Ecommerce websites. You can get this structured data according to your preferred format (CSV/XML/JSON) from your competitors’s websites to perform further analysis. Just feed the data into the analytics tool and you are ready to factor in the competitors’ pricing into your pricing strategy. This will answer some the important questions such as: Which product can attract premium price? Where can we give discount without incurring loss? You can also go one step further by using our live crawling solution to implement a robust dynamic (real-time) pricing strategy. Apart from this, you can use the data feed to understand and monitor competitors’ product catalog.

Reseller management

There are many manufacturers who sell via resellers and generally there are terms that restrict the resellers from selling the products on the same set of Ecommerce sites. This ensures that the seller is not competing with others to sell own product. But, it’s practically impossible to manually search the sites to find the resellers who are infringing the terms. Apart from that, there might be some unauthorized sellers selling your product on various sites.
Web data extraction services can automate the data collection process so that you’ll be able to search products and their sellers with less time and efficiently. After that your legal department can take the further action according to the situation.

Demand analysis

Demand analysis is a crucial component for planning and shipping products. It answers important questions such as: Which product will move fast? Which one will be slower? To start off, e-commerce stores can analyze own sales figures to estimate the demand, but it’s always recommended that planning must be done much before the launch. That way you won’t be planning after the customers land on your site; you’d be ready with right number of products to meet the demand.
One great place to get a solid idea of demand is online classified site. Web crawling can be deployed to monitor the most in-demand products, categories and the listing rate. You can also look at the pattern according to different geographical locations. Finally, this data can be used to prioritize the sales of products in different categories as per region-specific demand.

Search Ranking on marketplaces

Many Ecommerce players sell their product on their own website along with marketplaces like Amazon and eBay. These popular marketplaces attract a huge number of consumers and sellers. The sheer volume of sellers on these platforms makes it difficult to compete and rank high for particular search performed on these sites. Search ranking in these marketplaces depends on multiple factors (title, description, brand, images, conversion rate, etc.) and needs continuous optimization. Hence, monitoring ranking for preferred keywords for the specific products via web data extraction can be helpful in measuring the result of optimization efforts.

Campaign monitoring

Many brands are engaging with consumers via different platforms such as YouTube and Twitter. Consumers are also increasingly turning towards various forums to express their views. It has become imperative for businesses to monitor, listen and act on what consumers say. You need to move beyond number of retweets, likes, views, etc. and look at how exactly consumers perceived your messages.
This can be done by crawling forums and sites like YouTube and Twitter to extract all the comments related to your brand and your competitors’ brand. Further analysis can be done by performing sentiment analysis. This will give you additional idea for future campaigns and help you optimize product strategy along with customer support strategy.

Takeaway

We covered some of the practical use cases of web data mining in the e-commerce domain. Now it’s up to you to leverage the web data to ensure growth of your retail store. That said, crawling and extracting data from the web can be technically challenging and resource intensive. You need a strong tech team with domain expertise, data infrastructure and monitoring setup (in case of website structure changes) to ensure steady flow of data. At this point it won’t be out of context to mention that some of our clients had tried to do this in-house and came to us when the results didn’t meet expectation. Hence, it is recommended that you should go with a dedicated Data as a Service provider who can deliver data from any number of sites according to pre-specified format at desired frequency. PromptCloud takes care of end to end data acquisition pipeline and ensures high quality data delivery without interruption. Check out our detailed post of on things to consider when evaluating options for web data extraction.

Source Url:-https://www.promptcloud.com/blog/applications-of-web-data-extraction-in-ecommerce/

How We Maintain Data Quality While Handling Large Scale Extraction

How We Maintain Data Quality While Handling Large Scale Extraction

The demand for high quality data is increasing along with the rise in products and services that require data to run. Although the information available on the web is increasing in terms of quantity and quality, extracting it in a clean, usable format remains challenging to most businesses. Having been in the web data extraction business for long enough, we have come to identify the best practices and tactics that would ensure high quality data from the web.

At PromptCloud, we not only make sure data is accessible to everyone, we make sure it’s of high quality, clean and delivered in a structured format. Here is how we maintain the quality while handling zettabytes of data for hundreds of clients from across the world.

Manual QA process

1. Crawler review

Every web data extraction project starts with the crawler setup. Here, the quality of the crawler code and its stability is of high priority as this will have a direct impact on the data quality. The crawlers are programmed by our tech team members who have high technical acumen and experience. Once the crawler is made, two peers review the code to make sure that the optimal approach is used for extraction and to ensure there are no inherent issues with the code. Once this is done, the crawler is deployed on our dedicated servers.

2. Data review

The initial set of data starts coming in when the crawler is run for the first time. This data is manually inspected, first by the tech team and then by one of our business representatives before the setup is finalized. This manual layer of quality check is thorough and weeds out any possible issues with the crawler or the interaction between the crawler and website. If issues are found, the crawler is tweaked to eliminate them completely before the setup is marked complete.

Automated monitoring

Websites get updated over time, quite frequently than you’d imagine. Some of these changes could break the crawler or cause it to start extracting the wrong data. This is why we have developed a fully automated monitoring system to watch over all the crawling jobs happening on our servers. This monitoring system continuously checks the incoming data for inconsistencies and errors. There are three types of issues it can look for:

1. Data validation errors

Every data point has a defined value type. For example, the data point ‘Price’ will always have a numerical value and not text. In cases of website changes, there can be class name mismatches that might cause the crawler to extract wrong data for a certain field. The monitoring system will check if all the data points are in line with their respective value types. If an inconsistency is found, the system immediately sends out a notification to the team members handling that project and the issue is fixed promptly.

2. Volume based inconsistencies

There can be cases where the volume count for records significantly drop or increase in an irregular fashion. This is a red sign as far as web crawling goes. The monitoring system will already have the expected record count for different projects. If inconsistencies are spotted in the data volumes, the system sends out a prompt notification.

3. Site changes

Structural changes happening to the target websites is the main reason why crawlers break. This is monitored by our dedicated monitoring system, quite aggressively. The tool performs frequent checks on the target site to make sure nothing has changed since the previous crawl. If changes are found, it sends out notifications for the same.
High end servers

It is understood that web crawling is a resource-intensive process that needs high performance servers. The quality of servers will determine how smooth the crawling happens and this in turn has an impact on the quality of data. Having firsthand experience in this, we use high-end servers to deploy and run our crawlers. This helps us avoid instances where crawlers fail due to the heavy load on servers.

Data cleansing

The initially crawled data might have unnecessary elements like HTML tags. In that sense, this data can be called crude. Our cleansing system does an exceptionally good job at eliminating these elements and cleaning up the data thoroughly. The output is clean data without any of the unwanted elements.

Structuring

Structuring is what makes the data compatible with databases and analytics systems by giving it a proper, machine readable syntax. This is the final process before delivering the data to the clients. With structuring done, the data is ready to be consumed either by importing it to a database or plugging to an analytics system. We deliver the data in multiple formats – XML, JSON and CSV which also adds to the convenience of handling it.

Source:https://www.promptcloud.com/blog/how-we-maintain-data-quality-web-data-extraction