What is Data Acquisition ?
Data acquisition involves gathering information from diverse sources to uncover insights, answer research questions, and evaluate outcomes. It’s essential for creating high-quality datasets that fuel machine learning and AI. Effective data acquisition ensures that the data used for training models is accurate, relevant, and representative of the problem domain, setting the stage for exceptional AI performance.
Aya Data offers broad data acquisition services to help clients build high-quality datasets for their AI and machine learning projects. We are adept at sourcing the valuable, hard-to-find data that powers successful AI models.
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Our Data Acquisition Services
Aya Data offers broad data acquisition services to help clients build high-quality datasets for their AI and machine learning projects. We are adept at sourcing the valuable, hard-to-find data that powers successful AI models.
Data Collection
We use advanced methods to gather data, including drones for aerial imagery, surveys to capture target audience insights, and our extensive partner network to source information across a broad range of industries. Our team ensures the data is diverse, relevant, and perfectly aligned with your project requirements.
Data Procurement
Access exclusive datasets through our strategic partnerships and collaborations. Aya Data’s alliances with academic institutions, research centres, and industry leaders allow us to procure high-quality data assets unavailable elsewhere.
Web Scraping
Leverage our web scraping expertise to extract valuable insights from online sources efficiently and at scale. We use the latest the techniques to collect data from complex websites and handle dynamic content, all while adhering to ethical standards and legal requirements.
Data Curation
Data curation involves cleaning, splitting, and augmenting datasets. Cleaning corrects errors, handles missing values, and standardises formats. Data is split into training, validation, and testing sets, with sampling ensuring representativeness. Augmentation techniques like rotation or noise injection can increase dataset size and variance when data is limited.
Why Use Aya for Your Data Acquisition Project
Exceptional Customer EXPERIENCE
- 1. Customised data collection strategy
- 2. Regular updates and communication
- 3. Flexible engagement models
- 4. Tailored to project requirements
- 5. Responsive customer support team
Unwavering QUALITY
- 1. Multi-domain data collection experience
- 2. Diverse data-gathering methods
- 3. Hard-to-find data sourcing
- 4. Rigorous quality control processes
- 5. Skilled and trained annotators
Unparalleled EXPERTISE
- 1. Strict data security protocols
- 2. Regulatory compliance (GDPR, ISO, HIPAA)
- 3. Secure transmission and storage
- 4. Experienced data science team
- 5. Cutting-edge annotation tools employed
What Our Clients
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Data Acquisition Process
Data Acquisition Case Study
Revolutionising Farming with AI and Drones: A Success Story
A farm needed to optimise monitoring operations and processes. Using drone-powered data collection and AI analysis, Aya Data transformed a 6,000-hectare Ghanaian palm plantation's operations, developing a machine learning model with 98% accuracy in tree counting and location assessment, plus an interactive dashboard for real-time visualisations of plant counts and farm data. Learn more about how Aya's drone-powered ML solutions significantly improved crop management, decision-making and operational efficiency.
Collecting and Processing Cocoa Field Data for Automated Crop Monitoring
A major agricultural company collaborated with Aya Data to transform crop monitoring on their 3000-hectare cocoa plantation. Traditional methods were challenging, and they lacked an in-house data science team. Aya deployed drones with advanced sensors to capture high-resolution imagery, LiDAR, and multispectral data, enabling detailed plantation mapping, plant health assessment, and structural analysis. Learn more about how Aya's automated crop monitoring solution helped.
Data Acquisition Blogs
Semantic Segmentation for Computer Vision Projects Explained
Semantic segmentation is an image-labeling technique to create data for supervised computer vision (CV) models. In its simplest terms, the objective is to assign a class label to each pixel in an image. For example, if you’re labeling a cat against a plain background, semantic segmentation labels every single pixel of the cat as a “cat”. Pixel-level labels are required for image segmentation tasks, which differs from object detection.
News, Research, and Social Media as Sources for Datasets
In the age of big data, datasets are crucial for research, analysis, and decision-making in various industries. But where do these datasets come from? Traditional sources, such as government agencies and academic institutions, are still important, but there is a wide variety of new sources available through news, research, and social media.
Basic Data Collection Methods for Machine Learning Projects Explained
Data collection might seem like a simple process on the surface. But because it’s the basic building block of all ML projects, the data needs to be accurate, relevant, and cover all iterations of a problem. Consequently, it is crucial which data collection methods for machine learning are employed to gather it. Without a good dataset, you can’t have good training data, and without good training data, an ML algorithm can’t serve its intended purpose.
The Challenges of Text, Audio, Photo, and Video Data Collection for ML Training Models
he basis of all machine learning projects is data collection and acquisition. But that is also the first stumbling block where many ML projects fail. In this article, we will discuss the challenges of text, audio, photo, and video data collection for ML training models so that you can predict and avoid the many pitfalls. By the end, you will understand why data collection is not as easy as it may seem.
What Is AI Training Data? – And Why It Is the Basis of All AI Projects
Artificial Intelligence is a transformative technology that has found its way into various aspects of our lives, from voice assistants on our smartphones to autonomous vehicles navigating our streets. But have you ever wondered how AI systems learn and improve their performance? The answer lies in the crucial role of AI training data. An AI project without a good training data set simply won’t perform its intended function.
Ready to bring your AI project to life?
Do you need help with data acquisition, data annotation or building a custom model, Aya Data is ready to partner with you. Talk to us today!