Data is an inseparable part of every business and organization. All successful operations need to collect analyze and report on pertinent data to grow, or at very least stay relevant. Otherwise, all would have been done through guesswork, and we can make an educated guess regarding the efficacy of doing such.
Now that practically every venture requires a digital presence, the number of web data scraping service providers have vastly multiplied in numbers over a rather short period of time. To say there’s a market demand for web data scraping services would be the understatement of the past hour. 46% of all social media traffic, for example, are web scraping bots.
Collection of data is one thing, but ensuring its value and quality for the customer is of paramount importance and a problem for some companies. Not all the data, collected either manually or by the help of other methods, is valid and useful.
There are several criteria that are used to evaluate the quality of data collected. Besides legal compliance, which should be a non-issue if you hire a reputable data scraping service, these are the basics to hold the service accountable to:
– The overall value of data for your purposes
Adhering to quality control of these criteria will ensure the best quality data for your decision-making.
Let’s get into them one by one, explaining how these simple terms are vital for maintaining data integrity, and ways to check if your data is really landing in your lap with the greatest amount of value.
While it may sound like common sense that web scraping should get you everything that you’re looking for, it’s not quite as simple as it sounds. Whether the data is delivered your team good news or bad news, how can one trust the news if pieces of a story missing?
Completeness shares a lot of theory and value as accuracy does, and we’ll get to the latter in a minute. But, how do you ensure completeness? Let’s take an example from Airbnb’s internal scraping practices:
The service that you use should be not only putting parameters in place to get you the data that you seek, but at the same time having the awareness to exclude certain data parameters that would otherwise not return a full picture for your query – for Airbnb, “search, methodology does not enter ‘trip dates’ in any of the searches, so that the website does not exclude listings that are marked as unavailable,” so that reports of all listings are truly returning all listings on the site.
Uniqueness in this digital practice can be understood as “avoiding data redundancies.” It should be rather obvious why duplicate data is useless, but what is most vital here is the harm that non-unique data causes for both parties.
For the customer, there are two worst case scenarios that play out when a dataset is rife with duplicates:
- You’ve not noticed the errors in data quality, and your set comparisons or desired information is skewed and thus useless.
- Significant time and money on your end will be spent going back to review each entry and bleach the sheets.
For the service provider, the harm caused here is that 1 bad review negates 9 good reviews, and most if not all reputable data scraping services leverage their reputation to win new and returning customers.
To ensure redundancies don’t appear, it requires significant engineering and technical knowledge on their end, but as a customer, simply ask them – “How often have you had issues with uniqueness, and what measures have you in place to prevent redundancies?” If it takes them more than 1.5 seconds to respond, and confidently, you’re in good hands.
By “accuracy” we don’t mean “precision” but rather “does this data give me an accurate understanding of what’s happening in the real world?”
For example, data scraping and web crawling are often used for Reputation Management amongst public figures, such as celebrities and elected officials, to gauge public opinion, whether or not restorative measures are working, etc. Especially for c-suite professionals, your online reputation defines your opportunities.
What would happen if, say, one was to sit in their ivory tower, told week after week by staff that the people love them, only to wake up to a full-scale mutiny one morning? The hyperbole used here liberally, of course, but accuracy is of utmost importance along with completeness in delivering you not only all of the data you desire but all of the right data.
Ensuring this is a bit of a two way street between customer and provider – on one hand, the provider should be doing an exemplary job at their coding and timeliness of deliverables, but the customer needs to make sure that between both parties there is a coherent understanding of the intention for usage in real-life.
In simple terms, data consistency is when there are no variations or deviations from the usability of the data – that when delivered to the end user fields, formatting, etc are consistently adhered to.
Say, one day your team gets 3000 rows of data, and the next day you get another 3000 except the date format has been swapped from MM/DD/YYYY to MM/DD/YY? Talk about a living digital hell, especially if your data collection is (I swear, no pun intended) time sensitive.
TIP: ask the web scraping services that you’re vetting right now what their policy is for delivering inconsistent data, and how they reimburse customers for when it happens. You’ll know the right answer when you hear it, but it sounds a lot like “We have a policy in place for our consistency quality assurance, but we’ve not had an issue yet.”
The overall value of data for your purposes
This final criterion is the simplest to comply with, but essentially, weigh the quality of the aforementioned 4 criteria against how easy it is to deal with the web scraping service you are using, and against the cost of their services.
Ideally, at every corner, the data scraping team should be delighting you with customer service, quality assurance, timeliness and at a reasonable cost. If any factor is lacking, open a dialogue with your service provider. Yet, if you evaluate these criteria with the companies being vetted to move ahead with, you’ll be avoiding an avalanche of potential roadblocks.
To wrap up, we hope that you found this article useful for your data scraping service hunting. Feel free to leave a comment if you think we left anything out, and drop us a line whenever you want.