Data Acquisition

Obtaining high-quality data is one of the most pressing challenges facing AI teams today. Aya Data collects high-quality data via web scraping, manual collection, and exclusive partnerships in the medical, agricultural, and geospatial industries.

Ask us about our off-the-shelf data library.

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Why Aya Data

Alongside a substantial off-the-shelf database, Aya Data has over 150 data acquisition partners covering
industries from healthcare to agriculture, in data formats ranging from DICOM to LiDAR.

man doing data sourcing and collection

Why Is Data Sourcing and Collection a Challenge?

The volume, format, and specificity of data required for machine learning projects is problematic, especially when a model requires a large sample of high-variance, high-dimensionality data.‍

In addition, collecting quality data while navigating privacy and use laws such as GDPR is tricky, especially when dealing with potentially personally identifiable information (PII). Using novel data collection techniques to obtain high-quality, compliant data is imperative to training the next generations of machine learning models.

data collection

Data Collection

Data collection involves retrieving data from a business’s internal systems and databases, pulling data from open and public datasets, scraping web data, collecting physical data from the environment, and creating entirely new, unique data.

Our data collection techniques include:

  • Collecting appropriate data from a business’s pre-existing cloud and relational databases.
  • Collecting data from public and open-source datasets.
  • Using compliant data scraping techniques to extract public data from the internet.
  • Collecting image, video, or sensor data from the real world.
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Data Procurement

Aya Data can procure specialist data from a curated list of partners,
providing you with the data you need to build case-specific models, for
instance:

  • Multi-language call centres
  • Anonymous and compliant medical diagnostic images
  • Textual healthcare datasets
  • Agricultural Drone Data
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data curation
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Data Curation

Data Cleaning
Data cleaning requirements vary from case to case. Aya Data will identify and correct errors and inconsistencies. This process involves fixing typos, handling missing values, removing duplicates, and standardizing formats. It’s necessary to convert raw data into an easily consumed format through encoding, scaling, and normalization.

Data Splitting and Sampling
Training sets are split into training, validation, and testing sets. Sampling techniques ensure the model is trained and evaluated on a representative sample.

Data Augmentation and Feature Engineering
In cases where data is limited, we can augment datasets to artificially increase their size and/or dimensionality and variance. New data can be generated through rotation, flipping, scaling, noise injection, pitch shifting, etc.

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Our Commitments to Our Clients

Our four pillars of commitment to our clients are based on years of experience of what gets
models into production the fastest and with the best results.

Our Commitments to Our Clients

Our four pillars of commitment to our clients are based on years of experience of what gets
models into production the fastest and with the best results.

Our Commitments to Our Clients

Our four pillars of commitment to our clients are based on years of experience of what gets
models into production the fastest and with the best results.

Our Commitments to Our Clients

Our four pillars of commitment to our clients are based on years of experience of what gets
models into production the fastest and with the best results.

Security

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Communication

Quality

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Efficiency

We follow the highest standards of data security and are GDPR and SOC 2 compliant. For sensitive projects, we
provide dedicated high-security Clean Rooms.

The only way to exceed expectations is to understand them in real-time. Effective communication is vital to effectively
complete projects which is why you will always have an open line of communication with us.

Quality is defined by you and delivered by us. Once KPIs are set, we iterate our workflow to deliver the results that you need to get the
most out of your mode.

Delays cost money so efficiency is our highest priority. We operate with 20% slack at all times to ensure you
have the data to meet your deadlines.

Data Acquisition Case Studies

Securely Procuring and Annotating Large Volumes of 3D DICOM Medical Data

Medical/Healthcare

Harnessing AI and ML in medical imaging enables experts to direct their time and skills toward higher-level tasks such as treatment and research, rather than time-consuming diagnostics. Speeding up diagnostic processes helps streamline the entire healthcare system, ensuring patients get timely treatment.

Sourcing and Annotating Vehicle Damage Images for Automated Insurance Claim Validation

Insuretech

The Client is a pan-African insurance company that wishes to utilize cutting-edge tech solutions to verify insurance claims. They assigned their in-house data science to develop a computer vision model that would detect and identify damage on vehicles.

Sourcing and Annotating Large Volumes of Agricultural Imagery for Precision Spraying

AgTech

Client X is a leading AgTech company focused on improving agricultural productivity and sustainability through cutting-edge technology solutions. Their aim was to have their in-house data science team develop a computer vision model that would detect and identify weeds.