Whenever a business wants to pick up a large project or feature, the question of build vs buy arises. This is more common when popular solutions already exist in the market. However, the question today has evolved more into a ‘build and maintain’ vs ‘buy’ problem statement since most software solutions run on the cloud and need to be maintained and scaled up with time.
When debating on building a job scraper vs buying a job data feed, the most critical factors will be the size of the company and its tech team’s abilities (even if it has one if it even has one). However, let’s look at both sides of the coin before reaching a conclusion:
Building your web scraping system to capture Job Feed:
Building a job scraper requires experience in programming languages and job scraping tools, the ability to understand cloud infrastructure, and a clear idea of what the final dataset needs to look like. Data cleaning, aggregation, and storage will also need to be worked upon depending on the usage of the final data.
Benefits:
Custom Solutions- Large companies with big and experienced tech teams can create tailored job scraping solutions that use AI and Machine Learning to mine and clean the right job data. Such solutions can end up becoming a single step in a multi-step workflow that is used internally to solve business problems.
Control: Having an in-house solution means that you are holding the horse’s rein. So you can switch to the latest technology, change your cloud provider, or build a more efficient job scraper whenever you want to.
Cost: For large companies with a pre-existing tech team that has experience in building web scrapers, building an in-house solution may be cost-effective. This is more so in the case where the company is scraping millions of job data feed every hour and pulling almost all data points from the web pages.
Cons:
Maintenance Overhead: Whatever infra you set up for scraping the web and creating a real time job feed will break at some point either due to an increase in throughput or because of changes in the UI of a website. You will need to have someone on support 24×7 for such scenarios in case you cannot afford a long downtime.
Time to go live: Building your own job feed solution will take time. It may take multiple iterations. Adding more complicated and new websites to your scraping list can also see a longer turnaround time. All this will introduce sluggishness in your business workflow.
Resource Intensive: You will need dedicated resources in your tech team who can build web scraping solutions at an enterprise level. The absence of such resources would result in hiring them or upskilling existing employees– both of which will delay your project even further.
Purchasing Job Data Feeds:
This usually involves getting job data feed from a DaaS provider. The solution provider scrapes job listings, cleans the data, applies pre-chosen filters, and delivers the data in the format and medium of your choice.
Benefits:
Experience: Given that these DaaS providers have been scraping job posts for years and have experience in fetching data from hundreds of websites that host job data, they are likely to be able to get you the job data that you need with relative ease.
Maintainable and Scalable solutions: Since most job feed providers host the solution in their cloud, they also take care of maintenance and scalability. What this means is that:
- They take care of whatever breaks.
- They upgrade the system as your data requirements increase.
Easy integrations: Since these companies have multiple clients, they have readily available integrations for different services like AWS-S3, Google Drive, REST API integration, and more.
Time and Resources: You save time and resources and can focus on your core business problem.
Cons:
Customization may take time: Since most DaaS providers have a readymade tool that fits almost all businesses, specific customization may take time. This needs to be discussed with the provider beforehand and included within the service level agreement.
Integration: Integrating a new data provider may be complicated especially if they do not support the cloud service provider or storage system or integration medium or file format that you are using. You should preferably choose a DaaS provider which offers a wide range of integrations so that your workflow can easily consume the job data feed and you can be future-proof in case you want to make any changes to the system later on.
Points to consider when buying Job Data Feed
In case you decide to go for a DaaS provider to purchase job data feed, you will need to check a few factors so that you can get past the minor cons and enable your business to make data-backed decisions.
Existing Clients: It is critical to get a look at the existing clients and case studies. This will ensure that the team that will help you is experienced and can process your requirement with ease and finesse.
Data Quality: Scraping data from the web is not difficult. The difficulty lies in ensuring that the data is clean. You will need to talk to your data provider beforehand and check the level of data cleanliness that they guarantee.
Filters: When consuming job feed, filters like industry, geographical locations, job title, salary band, and title may be important. You must check the basic and advanced filters that are readily available so that you do not face issues later on.
Scalability and Integration: Both of these are important if you want to:
- Go live quickly without having to make changes to your current system or cloud architecture.
- Upgrade your systems at a later time and consume more data to design more end products.
Strong SLAs: Every partnership in the software world stands on top of strong SLAs. This is the most vital part that you cannot overlook before adding the final stamp.
Our in-house AI-driven Job Feed solution JobsPikr takes care of all of the above and then some. We have more than 200 companies relying on our job feed, for whom we scan 70k data sources daily.
On top of that, we offer seamless integration in less than an hour while providing you with up to 45 data points for each job post. We also provide custom solutions for job market problem statements like–
- Skill gap analysis
- Matching jobs to candidates
- Designing coursework based on the latest industry requirements
- Salary benchmarking
- Talent intelligence
- Job board backfilling
With ML-based solutions running on top of clean data sources from thousands of websites, we offer you a tried and tested job data feed that will enable your business to grow beyond leaps and bounds surfing on the waves of data.
