As we discussed in Part One of People in AI and Data Management, an AI system is only as good as the data it receives. This means that an AI company’s data management talent plays an essential role in ensuring that the business’ AI operates smoothly.
In Part One, we looked at how people impact computer vision, outlining the role of human logic in producing reliable data sets. In this section, we will take a more in-depth look into predictive analytics and natural language processing, and the role data management plays in each.
HOW PEOPLE IMPACT PREDICTIVE ANALYTICS
Predictive analytics use patterns in data to formulate models for identifying future risks or opportunities. These models help to dictate important elements in many fields, including marketing, financial services, retail, and even social networking.
For data sets in fields steeped so heavily in interactions and behaviors, human logic is irreplaceable. While humans are generally predictable, data sets used to exploit this predictability can often become corrupt if not maintained appropriately. Not only does human talent play that essential role in maintaining the viability of specific data sets, but it also significantly affects the accuracy of predictive models. Because those models can hold heavy impact on a company’s decision-making processes, it is essential for businesses to ensure that their models are predicated on clean, well-structured, and viable data sets.
Many companies rely on predictive analytics and intelligence to determine a variety of core product experiences. By analyzing the hundreds of metrics made available through platforms like social media, companies can even define the products they create.
AI data validation by humans
HOW PEOPLE IMPACT NATURAL LANGUAGE PROCESSING
Data management also plays an essential role in natural language processing, a field that inherently relies heavily on human input.
Some of the hardest challenges in NLP — including speech recognition and natural-language generation — would be impossible to tackle without appropriate data sets. That means that trending products like chat bots are ultimately inviable without proper data management.
Chat bots are built with the help of data sets that are used to teach natural language processing algorithms. Without properly trained data sets, chat bots can produce incorrect answers, develop poor response times, and generally weaken an important customer touchpoint.
Unfortunately, in AI, there is no shortcut to natural-language understanding. In a field as diverse and as intricate as natural language processing, data management is an essential and irreplaceable step in producing viable products.
“Big data isn’t about bits, it’s about talent.”
— Douglas Merrill
Data management might seem simple but it serves an important purpose in AI. Your AI systems are only as good as the data you supply, and that data needs to be maintained and structured by human talent.
People matter in data management, whether it comes to predictive systems or natural language processing. If you’re interested in hiring effective data talent at scale, let us know!