Introduction
It’s finally the time of the year when you get to go on vacation with your family, and you have begrudgingly taken on the responsibility of ensuring everyone’s documentation is in place. After incessant reminders and some threatening phone calls, you finally receive the passport-sized photographs you had asked for. But what is this? Some have sent the photographs in .png, some in .jpg, some in .heic, and only a few kind souls in a .pdf format. But to upload it onto the immigration website, you need all of them to be in a pdf format. Now you have to sit and convert each of these files into the desired format to upload them. Such an inconvenience!
Well, imagine this but on a much larger scale, for a massive data set, which is comprised of thousands of SKUs, innumerable annotations, and multiple duplicates, not to mention a myriad of different formats, all disorganized. This can be detrimental to the growth of a company, unless urgently addressed.
To aid this, companies are increasingly looking at automating the process of bringing disparate data together in a standardized format.
What does ‘bridging’ encompass exactly?
- Data ingestion: This includes sourcing data from a variety of domains, such as electronic appliances, lifestyle products, and more. Moreover, these models also ensure efficient and accurate product matching, as per their clients’ requirements.
- Data annotation and labelling: Even though the processes are automated, bridging ensures space for human intervention with a Human-in-the-Loop (HITL) Annotation. This helps the training of ML models, which learn from every intervention.
- Integration with AI/ML Pipelines: Companies that offer the bridging feature also usually provide APIs and platforms for seamless integration into ML workflows.
Potential Challenges
- Scalability: Since most companies that require bridging have data at a large scale, efficiency becomes a key requirement.
- Room for Errors: With such large datasets, there are also higher chances of errors occurring, which can hurt the companies’ profits greatly.
How can dataX.ai help?
- Diversified Data Ingestion: This means that we are not restricted to a specific industry, or to a specific type of format.
- Intelligent Transformation: When it comes to mapping, our model does not require any templates, and can seamlessly figure out the target mapping from a sample output file.
- Configurable Mapping: This allows for flexibility even within an automated process, for our data mapping is tailored to the specific requirements of our clients’ target outputs.
- Self-learning model: Our model allows for human intervention, and learns with every such step, which further tailors its performance to align with the desired results of our clients.
- Error Reduction: Even with a system that adapts easily, and ensures that there exists room for intervention, our model ensures minimal human error, and the churning out of accurate and reliable data to be used further.
- Efficiency and Organization: Not only are resources such as time and manual labour saved, but our system also organises data into a standardized structure which can be easily analyzed by other digital applications as well.
Transform the way your data is mapped and matched with dataX.ai Bridge!
