What is "DaaS"?
Data as a service (DaaS) is a cloud-based software solution that provides businesses with the ability to request continuous pipelines of fresh, complete, and high quality ready-to-use data. This feeds directly into an organisation’s data storages and strengthens the datasets which they base their insights on.
DaaS platforms hold large amounts of data in a single location, compiled from different sources, including web data scraped from the Internet as well as data from specialised third-party data providers or other organisations that share data.
Erez Naveh is VP Products, Bright Data
How can you tap into DaaS potential?
DaaS platforms allow customers to access and query datasets as well as extract data from other data services all within the platform to compile the data needed, which can then be shaped into valuable insights.
This includes public web data from various categories such as all the listings for a certain product sold on Amazon. The provider scrapes and manages the dataset internally, and once ready, customers can choose to query specific points of the dataset to draw their own insights. Typically, organisations who perform these operations alone in-house do so with a lot of resources, including IT, development teams and so on, each responsible for collecting, cleaning, structuring/indexing and analysing the data.
DaaS platforms, however, perform all these actions for their customers. They regularly refresh the datasets, discovering new relevant data to add to the datasets, as well as offer enrichment options for certain fields. Furthermore, the platform cleans and structures the data so that they can integrate seamlessly with customer data lakes, warehouses and other storage platforms.
How do businesses use DaaS?
Essentially, DaaS platforms provide businesses with the ability to query continuous pipelines of fresh, complete, and high quality ready-to-use data that feeds directly into their own data storages. These queries filter the data into a subset that can be downloaded multiple times. When new data arrives, running the query again will update the internal datasets in real-time, providing a perpetual feed of updating information to make better decisions in the moment, across all departments. The platforms also have the capability of discovering new data to add to the subset and even alert the customer to the discovery of new data when it surfaces.
Businesses can also download historical data from the platform to match up against fresher insights, in order to determine historical trends like pricing fluctuations over time.
Use Cases:
eCommerce
Retailers who need to monitor a category of products across all eCommerce platforms could query data from eCommerce datasets to perform pricing comparison, improve product positioning, market fit and consumer sentiment as well as discover new market opportunities to generate new revenue streams.
Supply Chain
Businesses can use DaaS platforms to sidestep supply chain challenges and minimise the impact disruptions have on operations by querying data from weather and climate data providers, satellites, competitor products, logistical data providers as well as shipping and freight handlers.
Talent Sourcing Data
DaaS can be used by companies to query employees' professional and educational background across the public web for key positions, helping organisations reach a greater talent pool faster. HR managers can also use it to benchmark their HR strategies against competitors.
What are the benefits of DaaS?
No hassle with public web scraping
DaaS provides organisations with an ability to sidestep the challenges typically attached to in-house data collection, such as getting blocked, deploying suitable infrastructure, hiring staff and performing quality assurance.
Efficient and time saving
Using DaaS platforms, businesses send a query for the requested data and the provider searches for the data and delivers it upon the customer's request - saving the business time searching through different sources to compile the different sets of information.
Structured delivery of data
The queried data requested from the DaaS platform is delivered to the customer in a structured manner that feeds directly into customer data lakes, without need for further integration.
Less management of data storages
Real-time web data and newly discovered data can be set up to automatically feed in customer data lakes and storages to update customer datasets in real-time. This saves the risk of copying and deleting old data, where instead the fields just repopulate with the relevant information.
Experience of DaaS providers
DaaS providers service many organisations and verticals andthey are able to find commonalities between the needs of their customers. Therefore, providers can keep these datasets fresher and more relevant compared to an individual organisation’s capabilities. They can enrich the data, easily discover, and add new or missing data, as well as ensure the accurate collection and structured presentation of the results.
Cost effective
The query system ensures that customers are only receiving, and paying for, the data they need. This not only cuts costs, but it also reduces the time spent on cleaning, refreshing and analysing oversized datasets with irrelevant parameters included.
The big question: why DaaS?
The DaaS industry is essentially democratizing access to data, making sure that it is readily and easily available for businesses. We know that organisational and operational success can be closely correlated with a capability to effectively perform real-time data collection and analysis. However, not every company has the manpower, resources, or knowledge to be able to perform these functions.
DaaS providers are essentially digging new wells, setting up new pipelines,to ensure everyone has access to clean and consumable data. But it’s also about providing convenience to the data collection industry, making it easier for any business to locate and use data from multiple sources. The goal is to enhance the data operations of organisations across the globe, remove some of the costs involved in collecting the data individually, and provide a one stop shop where all users can find, sample, buy, and sometimes even share data all in one place.
It's the same principle as if you were to shop on Amazon for a pair of AirPods…data should be just as easy as that.
