What is Data as a Service [DaaS]
In this post, we will cover:
- Understanding Data as a Service
- The advantages of integrating DaaS into business operations
- Data as a Service integration challenges
- Companies that have successfully adopted DaaS solutions
- How your company can get started with a Data as a Service tool
Understanding Data as a Service
Bright Data is one of the first companies to introduce DaaS into the b2b digital commerce space. DaaS, much like SaaS (Software as a Service), is a web-based data collection, delivery, storage, and analytics solution. It is typically cloud-based and reliant on a third party for both the hardware and software.
DaaS’s main objective is to create a live feed of information for local corporate data consumers without the need for developing, managing, and troubleshooting the data collection process.
The advantages of integrating DaaS into business operations
Many companies spend countless hours reviewing web scraping guides, but once they have chosen a DaaS solution, these research projects become obsolete. The main advantages of DaaS business integrations include:
One: Less overhead
One of the major advantages of a DaaS solution is its ability to minimize overhead expenses. This means eliminating the need to spend money on dedicated technical data collection teams, maintaining servers/Datacenters, and developing in-house data collection software.
Two: Increased volume flexibility
When using rigid, in-house solutions, scalability can be slow to come about. If data collection capabilities need to be ramped up, then new team members need to be hired, more servers need to be bought or leased, etc. When using a DaaS solution, however, companies can increase/decrease data collection volumes on a per-project basis. This can mean daily, weekly, or even monthly fluctuations. The ease with which this can be accomplished means fewer headaches for project managers, as well as increased real-time control over budgets and team capabilities.
Three: System integration and expansion
When building or buying data collection systems for an organization, there is typically a lengthy system integration process. Beyond that, once the new system has been integrated, when the company wishes to collect a new type of Dataset or wishes to target new websites, these capabilities need to be ‘acquired’.
With DaaS, however, you are relying on a data collection network that possesses the experience and technical know-how of hundreds of thousands of target sites. They have already mapped complex site architectures and use Machine Learning (ML) capabilities in order to adapt their scrapers to real-time blocks and structural site changes.
Data as a Service integration challenges
Although Data as a Service adoption is on the rise, it is not without its challenges. These include:
For some companies, hooking up their internal, local systems, which contain troves of classified, offline information, can be a challenge. If a company decides to hook up these systems directly to the web in order to receive data, this can open them up to cybersecurity threats. That is why it is important to properly compartmentalize activity, having a data management strategy in place that has both external and internal facing servers, as well as firewalls that protect against any potential cyber-attacks.
Companies wishing to work with data collection networks will need to undergo a rigorous compliance process, which may feel invasive and lengthy at times. On the flip side, companies need not only pass provider Know Your Customer (KYC) processes but should also vet potential providers based on their own standards and criteria. Companies should look to see that data networks:
- Are CCPA and GDPR compliant
- Use ethical business practices when building their peer-to-peer networks (such as fair compensation and easy opt-in/opr-out policies)
- Do not engage in illegal/harmful activity such as ad fraud or enabling DDOS attacks (Denial of Service Attack).
Three: Formatting and cleaning
The last thing to ensure when choosing a DaaS solution is that the data being collected is, in fact, clean. This means that there are no duplicate or corrupted files, for example, that the data has been vetted for quality and authenticity, and that it can be obtained and delivered to teams/systems in the format in which they currently work. This may be JSON, CSV, HTML, or Microsoft Excel.
Companies that have successfully adopted DaaS solutions
Companies who are successfully leveraging website scraping technology include:
Windward: Maritime traffic predictive intelligence
Windward currently uses Bright Data’s Data Collector in order to automatically feed their algorithms with information in order to more accurately predict shipping ETAs (Estimated Times of Arrival). These data points include things such as vessel schedules posted on shipping carrier websites as well as alternative geospatial data indicating the vessel’s current location in the open seas.
The outcome: Windward is now able to more efficiently consume and analyze the layers of public web data that are involved in bringing full visibility to their clients’ maritime activities.
Manatal: Cloud-based recruitment Software as a Service
Mantal is a recruitment solution that aims at reducing friction and improving hiring processes. In order to provide value to companies, their Artificial Intelligence (AI) needs real-time data in order to source new and unique candidates and enrich potential candidate profiles.
The outcome: They started using Bright Data’s DaaS solutions in order to collect information from social media in order to get a more holistic view of potential candidates whilst increasing non-biased matchmaking capacity and accuracy.
How your company can get started with a Data as a Service tool
One of the best ways to get started with a DaaS solution is by choosing a reputable data collection service. The next step is to figure out which data points will bring the most value to your business and from which target site. So let’s say your company dabbles in digital commerce and is looking to better compete in a new marketplace. You have determined that the market you are attempting to enter is extremely sensitive to quality and therefore decide to collect customer reviews of similar products being sold by competitors.
Once the target is clear, you will now need to decide which Data as a Service workflow works best for you. The two options are: