How Self-Service AI Data Labeling Platforms Support Business Transformation

    Leading businesses already know that data fuels their ability to predict and respond to change by embracing transformation on an ongoing basis. Data is the foundation of the intelligence that businesses need to adapt how they operate—whether they’re revamping their supply chains or creating a more personal customer experience. However, an ability to sense and respond constantly (not just once) requires companies to process data sets rapidly. This is why an emerging field, self-service data labeling, is capturing the interest of more businesses. Let’s take a closer look.

    Why Self-Service Data Labeling Platforms are Appealing

    Businesses need access to large data sets to, say, test the effectiveness of a new eCommerce engine that uses machine learning to provide more personalized content to customers. It may not make economic sense for a business to hire a team of resources to do all the heavy lifting required to label data sets required to test the personalized eCommerce engine—especially for lower-volume projects in the early stages of development. In this case, the enterprise may want to consider a self-service platform for data labeling. These platforms give a business the ability to create and curate data sets with the help of humans in the loop in a self-directed way. With self-service data labeling, a business can more easily accomplish important tasks such as:

    • Data collection: an effective self-service AI data labeling platform collects and labels text, images, audio, video, speech, and other data to improve machine learning algorithms. The data assets can be collected by an open global talent pool or by very specific demographics, to ensure diversity. 
    • Data curation: companies can annotate and curate their data for all their AI training needs. To do that, self-service AI data labeling platforms need to leverage annotators and contributors with proficiency in multiple languages and deep domain expertise.

    By contrast, more complex, higher volume projects, requiring program management, probably require a managed services approach from a partner that can provide hands-on consulting as well as tasks such as data labeling.

    Also read: Steps to Improving Your Data Architecture

    How a Self-Service AI Data Labeling Platform Can Help

    Let’s say a retailer wants to change its service model to a more personalized chat experience on its site. The retailer, in the early stages of testing its chat model, may need to do multiple tests to see how well its chatbot responds to complex customer scenarios. The retailer might rely on the self-service data labeling platform to collect the data required for the scenario testing and then label the data correctly so it is usable. A self-service data labeling platform should provide both data collection and curation. 

    In this scenario, the retailer enjoys several benefits from self-service data labeling, such as:

    • Control: businesses define their own data labeling and annotation projects according to their needs.
    • Rapid set-up: AI enablement projects such as data curation can be set up rapidly by tapping into our platform and global talent pool at the ready.
    • Speedy results: clients achieve results faster than if they’d not had a platform to manage data labeling needs.
    • Quality control: customers can tap directly into the data and perform quality review on each task as well as use ground truth to automatically score the work performed by the contributors.

    However, companies need to also be mindful of a common blind spot with self-service AI data labeling.

    Also read: 7 Smart Retail Solutions that are Changing Ecommerce

    Self-Service AI Blind Spots

    Self-service AI data labeling platforms rely on a pool of individuals, but they’re not always as diverse as they should be. This is a problem. To address as broad a customer set as possible, a product or service needs to be inclusive, which means relying on a talent pool that reflects the diversity of the population that an enterprise is trying to reach.

    For example, a product that relies on a voice assistant must understand nuances in languages and accents spoken around the world. Only by relying on a diverse pool of resources can the enterprise collect an inclusive data set to properly test the voice assistant. In addition, relying on a diverse talent pool guards against bias creeping into data collection and labeling. 

    Businesses need to vet their self-service AI data labeling platform carefully, including a thorough assessment of factors such as:

    • How many markets are represented.
    • How diverse the contributors are, including people of color, different nationalities, genders, abilities, and so on. 

    The above list is just a start, and each business may possess different needs depending on how diverse their audience is and how many different countries and cultures they might be trying to reach. However, the groundwork needs to be done or else the platform will introduce new problems.

    Self-service data labeling is an exciting field with enormous potential to unlock innovation. But businesses need to take a thoughtful approach. By being mindful, the enterprise can lean on self-service AI data labeling to effect change rapidly and in an inclusive way. 

    Read next: Data Management with AI: Making Big Data Manageable

    Venkat Rangapuram
    Venkat Rangapuram
    Venkat Rangapuram joined in 2015 and has held multiple senior positions at both Pactera and Pactera EDGE. He brings a wealth of knowledge and experience to his new role and has had a successful track record in transforming the company’s various business units into the digital paradigm over the last few years. Most significantly, Venkat has demonstrated the unique capability to effectively translate complex business and technology challenges to strategic business solutions, and then communicate these solutions to Pactera EDGE’s enterprise clients.

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