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.
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
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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
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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
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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
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What Our Clients
Say About Us
"We struggled with sales data visualisation using our existing CRM, Aya built a bespoke dashboard to geolocate our sales and display them on a map with a host of other metrics, in incredible detail. This has informed the revision of our entire sales strategy, it has been an invaluable asset."
Rocco Falconer,
CEO,
Demeter Holdings
"Aya Data has performed complex 3D data labelling tasks with our machine learning team at Cydar Medical and helped us accelerate our research and development. We especially value their diligence, attention to detail, focus on high quality, excellent teamwork and communication, and record of delivering projects on time and on budget."
Tom Carrell,
Founder and Chief Medical Officer, Cydar Medical
"We worked with Ayadata to build and label huge datasets; their team was responsive and did their job as expected. They were flexible and accommodated our changing schedule, we appreciate working with their team."
DP WORLD
"Aya's value is in consistently delivering very high quality of work over a long period at a reasonable price, without dropping standards. They deal well with complex use cases requiring pixel perfect precision, fast communication means very little rework. This helps us to bring our models to production faster. They have become an invaluable part of our annotation process."
Thomas Perry,
Annotations Manager, Dogtooth
"We had a fantastic experience working with Aya Data. Their professionalism, responsiveness, and dedication to our business needs was truly impressive. They delivered the request data project accurately, on time, and within the quoted budget. We highly recommend their services."
TIDAL
"We’re pleased to have a positive relationship with the whole Aya Data team. They are diligent and committed to continuous improvement and our teams enjoy working together. Utilising V7’s leading platform and Aya’s dedicated annotator workforce, we're pleased to partner with this team, and are one of a few companies that have actively put themselves forward to become V7 accredited."
Lauren Hale,
Partnerships Director, V7 Labs
"We approached Aya to build a bespoke computer vision solution to monitor seedling germination rates using drones. They developed, trained and deployed the models extremely quickly and with excellent results. I now have a single source of truth dashboard to monitor. The success of our reforestation project."
Chris Rothera,
CEO, Oko Environmental
"We had many photos to label in a short period of time, so we decided to outsource the task. Aya Data did not only meet our time constraints, but also our complicated annotation requirements.
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Richard Parcell,
CTO, Earth Rover
"Aya Data have been a reliable partner, covering a range of different use cases and sectors with us. We know we can trust them with complexity, meeting tough deadlines and most importantly to communicate clearly and effectively at all times."
Marley Jones,
Senior Project Manager, Labelbox
Data Acquisition Process
STEP 01
Project Scoping
- We work with you to define the scope of your data collection project, identifying key objectives, data sources, and desired outcomes.
STEP 02
Resource Estimation & Project Planning
- We assess the resources required for your project and develop a plan outlining timelines, milestones, and deliverables.
STEP 03
Data Collection
- We employ numerous data collection methods, such as surveys, web scraping, drone imagery, and sensor data, to gather the necessary data for your project.
STEP 04
Quality Control
- Our rigorous quality control processes ensure that the collected data meets the highest standards of accuracy, completeness, and relevance.
STEP 05
Data Delivery
- We deliver the collected data in your preferred format, along with complete documentation and support for integrating it into your machine learning workflows.
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.