Part One: People in Data Management and AI
In a world full of numbers, statistics, automation, and machine learning, it can be easy to overlook the impact of hiring the right talent. But as we mentioned in this post:
“Big data isn’t about bits, it’s about talent.”
— Douglas Merrill
Talent is integral to ensuring appropriate maintenance, processing, and data management for your business, whether you are an early-stage startup or a Fortune 500 company.
Why People Matter in Data Management
Data management is a continuous process that extends over a long period of time. As information flows in and out of companies and datasets fluctuate, workers can spend hundreds of hours processing and organizing the data that is eventually used to develop AI systems. In fact, this study conducted by Forbes reveals that data scientists spend around 80% of their time on data management, preparing for interpretation and analysis.
This work is irreplaceable. While your AI system might be your core product or SaaS offering, it is only as good as the data you supply it. If your data is poorly managed or unstructured, your company risks making misinformed decisions with faulty analysis, or selling an inefficient product.
All of this goes to show that talent is not just important to a company's data tech stack — it is essential. Without the right talent collecting, cleaning, and processing data, AI systems cannot properly function! It takes human talent to manage and prepare the data that AI systems use to operate. For companies providing software or services to other companies, this means that finding the right talent is especially important.
In Part One of this two-part article series, we will take a look at some of the roles people play in data management, and how that work informs AI.
How People Impact Computer Vision
People are critically important when it comes to training computer vision software. In fact, human logic in this line of work is irreplaceable. The work of your data talent ultimately makes your core product or service possible. For example, by drawing bounding boxes around identifiable objects and creating similar training data sets, humans critically determine the ability of self-driving cars to perceive environments and make appropriate decisions.
In the case of self-driving cars, companies have to depend on their data talent to accurately and efficiently produce reliable training data sets in order to maximize the safety of their products. As self-driving cars approach wider-scale feasibility, it becomes increasingly important for 3d object detection software to both perceive and predict the positions of objects in an environment.
For self-driving cars, that environment might include trees, other cars, and even nearby pedestrians. In other words, when it comes to self-driving cars, human logic quite literally can mean the difference between life or death. Training data sets with reliable information is essential to the future of autonomous vehicles.
That future begins directly with the data, where accuracy must be second-to-none.
AI is an incredibly promising field, but it will only go as far as the data that is used and the people who manage it. In Part Two of this two-part series, we will take a look into predictive analytics and natural language processing to understand the role of talent more in-depth!