Challenges in Data Gathering and the Ingestion Process

If we explore any Artificial Intelligence (AI), data is the fuel that powers today's development and deployment of those intelligent systems. However, the process of gathering and ingesting data for those complex systems poses a unique set of challenges. With the diversity of data sources, increasing complexity as well as a primary need to maintain data quality and consistency, the data gathering and ingestion process has become a critical factor for AI development. In this blog, we will explore the challenges commonly faced by businesses and organisations while gathering and ingesting data for an AI model. Along with that, we will discuss the best practices for overcoming these challenges and ensuring the effective use of data in AI development.

The challenges faced during the Data Gathering and Ingestion process are as follows:

1. Data Quality and Consistency: Making sure the data is of the highest quality and consistent across various sources is one of the major challenges in data gathering and ingestion for AI. This is essential when dealing with large amounts of data from various sources, which frequently contain mistakes, duplicates, or inconsistencies. To overcome this challenge, businesses need to invest in robust data cleaning and normalization processes, which can help identify and remove errors and inconsistencies in the data.
2. Data Integration: Another challenge in data gathering and ingestion is merging data from various sources into a single, unified dataset. This can be particularly challenging when interacting with unstructured data, such as text or images, which are required for complex Natural Language Processing (NLP) and Computer Vision applications. To address this challenge, businesses need to utilise various data integration tools and techniques such as data wrangling and feature engineering which help to ensure that the data is properly structured and formatted for the AI applications.
3. Data Scalability: As the volume and complexity of data continue to increase, another challenge is ensuring that the process can scale to meet the needs of the organization. In order to ensure that the data can be ingested and processed accurately and effectively, businesses should invest in scalable data infrastructure and architecture, including distributed storage and processing systems.
   
4. Data Privacy and Security: Further challenge is to ensure the privacy and security of the data. With increasing concerns around data breaches and cyber threats, businesses need to implement robust data security measures to protect the sensitive data they are collecting. To ensure that the data is protected from unauthorized access and use, various measures such as encryption, access controls and data anonymization should be implemented by businesses and organisations.

The challenges mentioned above might not include all the challenges encountered during any specific project as each project have their own sources and way of collecting data. The ingestion platform and methodology also might differ from project to project. If we compare these challenges to the ones we encountered during the creation of our own AI Image Process Automation (IPA) solution, there would be additional challenges such as: 

1. Data transmission: Since interference, signal deterioration, and loss of connection can have an impact on data transmission from drone, we need to ensure that the data is reliably transmitted from the drone to the cloud platform. Using reliable communication protocols like MQTT or XBee, implementing error detection and correction method etc. can be utilised to address this issue. Alternatively, the drone can also be fitted with a backup cellular connectivity for redundancy. 
2. Data volume and storage: The data collected by the drone will likely be high volume as the drone takes high quality video files and requires significant storage capacity on the cloud platform. This can be solved by using cloud storage services that provide scalable storage options, such as Amazon S3 or Google Cloud Storage. Additionally, data compression techniques can be used to reduce the size of the data before storage.
3. Data security: Another challenge will be to secure the data during transmission and storage to prevent unauthorized access or data breaches. This can be addressed by using encryption techniques to securely transmit and store the data. Additionally, access to the stored data can be restricted using authentication methods, such as multi-factor authentication.
4. Latency and real-time processing: The drone will be capturing data in real-time, and there may be latency issues in transmitting that data to the cloud platform for processing. This can be addressed by optimizing the data transmission protocol, reducing the size of data transfers, and implementing edge processing to perform some processing on the drone before sending data to the cloud platform. Additionally, periodical batch processing can also be implemented, which helps towards both latency and data transmission.
5. Integration with existing systems: The custom pipeline for gathering and ingesting data needs to be integrated with the existing systems and processes used by various AI applications. This can be achieved by using APIs or other integration techniques to seamlessly connect the new data collection process with existing systems.
6. Scalability and efficiency: As the data gathering and ingestion process increases, it may become necessary to scale the solution to handle larger amounts of data and the higher demand while still maintaining efficiency. This can be addressed by using cloud computing services that provide scalable computing resources, such as Amazon Lambda or Google Cloud Platform. Additionally, serverless data processing and ingesting can be performed (e.g.: using AWS Lambda Function) to scale on-demand and reduce the cost of operation at the same time.

In conclusion, the process of gathering and ingesting data for AI presents a range of challenges for businesses and organizations. However, by investing in robust data cleaning and normalization processes, advanced data integration tools and techniques, scalable data infrastructure and architecture, and robust data privacy and security measures, organizations can overcome these challenges and effectively leverage data for AI development. With the right approach, businesses can transform data from a challenge into a powerful tool for innovation and growth.

Net Reply is a company that has experience in Network Automation, Cloud Connectivity and Artificial Intelligence. If you would like to know more about the AI Image Process Automation (IPA) tool, or be given a demo, please reach out to us , follow us on LinkedIn or contact the author of this article,  , for more information.