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Why web data acquisition is one of the biggest pain points in the data industry? | PromptCloud
 

Why web data acquisition is one of the biggest pain points in the data industry?

Why web data acquisition is one of the biggest pain points in the data industry?

With Big Data breaking all norms and crafting new ones, it is no surprise that many companies would be keen to and would have already started investing into Big Data technologies and implementation strategies. American marketers view Big Data implementation as the 3rd biggest priority in 2015.

For these businesses, getting the management buy-in is the least of their worries. After all it is Big Data that helps unravel patterns, trends and insights from reams of structured and unstructured data from various sources that would have otherwise remained hidden from the company’s decision makers – a big plus factor for the management who recognize the full impact of Big Data into the business’ fortunes and bottom lines.

However in order to truly justify the value potential of Big Data as a significant tech disruptor to your company, it is important to answer this critical question – “How do I acquire quality data that will give me the answers I need for my business?” Keeping this critical question in mind, today we will cover data acquisition and have an in-depth look into this fascinating aspect of Big Data.

Data-Acquisition

Data? Information? Knowledge?

Imagine you need to go to the local convenience store to buy goods. The first thing you need to know is what to buy. This is data. It tells you what is not present in your house and needs to be bought from the store. Without data, you would simply be roaming all around the supermarket without really knowing what to buy and what not to buy. It will also become an expensive proposition if you purchase something that wasn’t needed in the first place.

Similarly, for any decision making and deploying the prowess of Big Data, data is the fundamental element needed. Without data, all you have are millions of pieces of information that won’t help you in providing actionable insights to help in the management decision making process. Here we need to be aware of the distinction between three terms that seem similar but are vastly different from each other – data, information and knowledge.

  1. Data – Descriptions of various variables (events, transactions or activities) that are present or available in their natural form

For e.g. social media reviews and posts across multiple platforms is basic data

  1. Information – When data is collected and organized to lend a sense of meaning or value

For e.g. Social media, in general, will not have any meaning. But when we employ scraping to extract data from websites for a business, it becomes information for that business.

  1. Knowledge – Data or information that is processed to convey an answer / understanding of a problem or an activity.

For e.g. After scraping the social media data, you may employ BI and analytics to uncover your customers’ wants or preferences. This provides direction to you to put your efforts to meet these specific requirements, thus leading your company on the right path to growth  

What is data acquisition?

Data Acquisition is the step where you get the data in one or more of the below ways

  • either free found or by buying data,
  • either using a specialist web scraper technology or by simple copy pasting,
  • either from internal sources (sales reports, financial performance sheet) or external (trade journals, third party web sites)

When looking to acquire data it will be wise to look at a few considerations –

  1. IP – Some data may be protected by Intellectual Property or Copyright. Make sure that the data doesn’t cross the line in this context
  2. Terms of Sale – Check the terms of sale of the website from which the data is acquired. Web scrapers shouldn’t extract information that violates the Terms of Sale
  3. Volume – Running a data scraping program fetching huge loads of information from the background might slow down the target site. Make sure you respect the target website’s business too.

Factoring this will help getting your house in order with data acquisition that you can use for your analytics and insights needs.

Benefits of data acquisition

With help of targeted and timely data acquisition multiple business divisions can reap the profit. Let’s look at some ways in which data acquisition provides impetus to your growth aspirations at multiple levels.

  1. Finance – The accounts department can verify if the invoicing is happening accurately. With customer data available to them they can also avoid billing another person with same name.
  2. Sales – The modern day sales rep has to know as much as possible about the customer even before the initial pitching happens. With the right kind of data scraped from public information, they are empowered with helpful information needed to seal the deal.
  3. Operations – Companies like Lenddo analyze the credibility and networking strength of a prospective applicant on social media. They use this platform to see if customers are actually who they claim they are and what is their connections’ strengths.
  4. Marketing – Imagine if you know what topic your leads and prospects are talking about. It will provide a great ice breaker opportunity the next time they communicate. To take an instance, if a marketing rep sees that a customer is talking a lot about his newborn kid online, he can use this info and enquire about the health of the kid when initiating a discussion in the future. Such immense level of personalization is what helps the marketing personnel to get a prospect closer to conversion.

Points to keep in mind to acquire quality data

Looking at the multiple benefits on offer by data acquisition, it becomes important to ensure that the data acquired is of high quality. Quality is measured at four different benchmarks –

  1. Contextual data quality – Data to be acquired needs to be relevant, timely, of correct volume and must adhere to timeliness and completeness
  2. Intrinsic data quality – Data needs to display intrinsic characteristics such as accuracy, believability, reputation, and objectivity
  3. Accessibility data quality – Data must have secure and validated access for security
  4. Representation data quality – Data must be open to interpretation, ease of understanding, have concise as well as uniform representation.

Data that adheres to these norms stands a good chance of helping the data analysts and data scientists to uncover actionable insights from these data. However, the marketplace is fraught with concerns over data quality. As a business owner or marketer, you need to be aware of the following issues that are likely to crop up during data acquisition and make sure you protect yourself from these problems.

Problem#1 – Data is not correct

Cause

  • Data was generated without exercising proper care
  • Raw Data was not entered accurately
  • Data is meddled with

Resolution

  • Devising, testing and rolling out a systematic way of entering data without issues of carelessness coming in
  • Automate the data entry to avoid inaccuracies due to human errors
  • Craft a holistic Quality Assurance process that provides a mechanism to control data issues
  • Introduce tight security measures to avoid data leakages

Problem#2 – Data is not timely

Cause

Data was generated through a system or process that is not fast enough to meet the needs of the business or the big data objective.

Resolution

  • Add extra touchpoints in web data acquisition (in terms of resources or human capital)
  • Enhance the system of generating data with expert help to scrape data from website.

Data-Acquisition

Problem#3 – Data is not indexed

Cause

  • Data was generated without considering the needs or business objectives
  • Implemented complex models that doesn’t augur well for the overall strategy or requirement of the business

Resolution

  • Utilize a data warehouse that helps you to store the data extracted from website
  • Align the management expectations with the data acquisition needs
  • Develop a robust and scalable system to rescale or recombine incorrectly indexed data
  • Embrace a simpler model that uncomplicates things at time of analysis

Problem#4 – Data is simply not present

Cause

  • For specific or niche needs, there was no need to store data until now
  • The data didn’t exist

Resolution

  • Utilize a web scraper to generate data from scratch
  • Use a data warehouse

The unfortunate fact is that if you do go with these issues, the real problem will be magnified manifold at time of data visualization. When the time comes to coming up with future action plan for the company, the ‘garbage-in-garbage-out’ policy will hit the decision makers pretty hard.

To conclude

Now that you are aware of the key problems associated with data acquisition you have taken the right first step toward ensuring right quality and volume of data for gathering insights into your marketing and operational issues. All that remains now is to enlist the help of a web scraping expert to give wings to your data acquisition strategy.

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