We Thrive in Complexity — And Love a Challenge
If there’s one thing that’s permanent in the training data space, it’s that demands on data are constantly changing. As artificial intelligence evolves and broadens in scope, data collection and annotation requirements will continue to evolve with it. These changes can affect the type of data to be collected, methods for collecting data, the data source, the kind of information to be annotated, or the annotation criteria. A broader scope of application, alongside improvements in AI capability, will lead to greater ambiguity in the tasks to be performed. This brings the depth and complexity of data collection and annotation to new levels.
While some data annotation providers are tied to a specific tool or method, we design and continuously adapt our processes and methodology with this ever-changing reality in mind. We have our roots in academic research but applied them in practice for more than 30 years. This combination of scientific curiosity paired with decades-long real-world experience shines through in our drive to solve complex annotation data challenges for our clients and advise them on effective strategies in this shifting space. We thank our clients expressly for coming to us with interesting and fulfilling projects and allowing us to continually develop our expertise.
Digital transformation of traditional industries is leading to new and interesting applications of AI. This poses new challenges for data collection. For example, data needs to be gathered from sources that are less frequent or occur only randomly. The data may be challenging to access because it contains confidential or personal information, requiring compliance with regulations that are becoming more and more complex. We approach each of these challenges with a solution that works for the client, be it augmenting datasets with synthetic data, sourcing data from the real world, or assisting with anonymizing datasets.
As artificial intelligence evolves, more detailed, complex, and even specialized annotations are needed. Therefore, data annotation professionals have to be flexible and continuously adapt. Training programs have to be comprehensive, so annotators understand and correctly follow the annotation guidelines’ nuances. The training programs not only have to include an explanation of the procedures and tools, but they also have to prepare candidates to be an active part of this constantly evolving ecosystem. This strategy provides Sigma with the capability to adapt to new conditions quickly.
Beyond the state of change in the market, demands on data variety and domain can expand within a client project as their own goals and AI applications grow. It can be smart to plan strategically for a future where various data types will be processed, for example, if a company expands its global footprint and needs to provide its product or services in different languages — or if they have a computer vision product, but want to incorporate natural language technology into future releases to interpret text within images. This requires the data collection and annotation teams to work with a variety of data types and sources.
Many annotation companies are focused on a particular data type because their tool, processes, and hiring is adapted to a specific data source. In contrast, at Sigma we have extensive experience in collecting and annotating data from many different sources. We have specialized departments for text and speech, covering 300+ languages and dialects, and for image and video, including RGB images, satellite, thermal, x-ray and more. This is possible because Sigma has progressively built experienced teams, efficient processes, and a suite of customizable tools to collect and process any structured and unstructured data. Our senior team has over 30 years of experience in data collection and annotation, in research and academia as well as practical development and implementation. Working with a company like Sigma means you can quickly scale across data types as your project grows and develops.