Skip to Content

The four factors behind scalable, high-quality data

Vijay Bansal
6 Sep 2022

Everyone wants high-quality data, but making this data scalable is often where a lot of organizations fall short. If you want to avoid this issue, take a look at the four factors underpinning data quality below.

Four ways to guarantee high-quality, scalable data (and faster results)

Research suggests that one of the main reasons why 96% of AI projects fail is a lack of high-quality data. Most companies would agree that for artificial intelligence (AI) models to work as intended, machine learning (ML) algorithms need to be trained with the best possible data available. Otherwise, the solution created could give inaccurate, biased, and inconsistent results.

There are many hurdles to building an AI solution – data collection, preparation, labeling, validation, and testing, to name a few. It’s like running a long-distance race except here, in addition to consuming energy, we’re also incurring enormous expenses while devoting precious time and resources to the project.

Knowing when to pace yourself is key. It can spell the difference between a triumphant win and a debilitating loss. In other words, speeding through preliminary data-related processes with a lack of focus may result in a project that’s over budget and behind schedule, with poor data security safeguards in place.

That’s why aiming for scalable high-quality data should always be a top priority. Cost, scalability, technology, and security, however, play an integral role in reaching that quality milestone – and can directly impact whether a project is destined to succeed or fail.

The four factors underpinning quality

Cost – having your own data science or AI engineering employees prepare and annotate data will put a major dent in your budget as these tasks can be done by a more budget-friendly but also professionally trained workforce. However, taking this approach could also slow down the speed at which you hope to complete the tasks since your own staff could make costly blunders affecting the quality of the training datasets.

Scalability: trying to complete your business objectives by scaling resources up and down depending on data demand may leave you scrambling for additional annotators at key moments when more data is required, or needlessly paying for their services during low peaks. Outsourcing data tasks to a service provider will give you more agility.

Technology: for large-scale projects, it’s simply not feasible to manually label all your data. However, an ML-assisted labeling approach can save the day here. Having an ML model pre-label data can add a high level of consistency, plus the annotation effort can be reduced by up to 70% for single tasks. Even if a medium level of accuracy can be attained with the help of technology, it means less manual work and faster annotation to get to the accuracy level we desire.

Security: if data labeling is not done in a secure business environment – for example in-house – your sensitive data will be exposed meaning it will be easier to compromise. Capgemini’s robust IT infrastructure and network security keeps your data safe. We leverage a trusted delivery platform to track and manage all data changes made by annotators. The platform also provides quality assurance tooling so that data annotation is always up to quality standards.

To learn how our Data Labeling Services leverages frictionless data labeling operations to deliver true scalable, high-quality data contact:

About author

Vijay Bansal

Director – Global Head – Data Labeling Services, Capgemini Business Services
Vijay has extensive experience working in map production, geo-spatial data production, management, data labeling and annotation, and validation roles. In these positions, he aids machine learning and technical support initiatives for Sales teams, coordinates between clients, and leads project teams in a back-office capacity.