SBIR Phase II: Democratizing Data Science Through Conversation

这个小型企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力是增加组织内可以进行复杂数据分析的用户数量。如果被证明是成功的，所提出的方法还可以在分析市场中开辟一个新的垂直领域，其中基于文本的聊天机器人可以帮助人类执行创建、部署和运行复杂数据科学管道的任务。这种积极的结果可能会导致在现有的分析软件市场中创建一个子市场，它也可以帮助提高生产力的（非技术）经济部门越来越需要从存档和实时数据集中获得高质量和快速的见解。第二阶段项目针对的问题是，目前可能需要大量的人力和时间来从数据中提取有意义的见解。该公司旨在通过训练基于文本的聊天机器人来对企业数据执行复杂的分析任务，从而改变这一繁琐的过程。然后，这些聊天机器人允许用户通过用书面英语（的受控子集）聊天来获取有关其数据的答案。而不是花几个小时甚至几天的时间来回答一个问题，大型数据集可以在几分钟内多次查询，使企业能够实时做出明智的决策。因此，该技术旨在通过向企业内的广泛用户提供数据，大幅提高人类在收集数据洞察方面的生产力，并使数据分析民主化。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to increase the number of users within an organization that can carry out sophisticated data analysis. The proposed approach, if proven successful, can also open a new vertical in the analytics market in which text-based chatbots aid humans in carrying out the task of creating, deploying and running complex data science pipelines. Such a positive outcome could lead to the creation of a sub-market in the existing analytic software market, and it could also help improve the productivity of the (non-technology) sectors of the economy that increasingly require high-quality and fast insights from both archival and real-time datasets.This Small Business Innovation Research (SBIR) Phase II project targets the issue that it currently can take substantial human effort and time to extract meaningful insights from data. The company aims to change this cumbersome process by training text-based chatbots to perform complex analysis tasks on enterprise data. These chatbots then allow users to acquire answers about their data by chatting in (a controlled subset of) written English. Instead of dedicating hours or even days to answer a single question, large datasets could then be queried multiple times in minutes, enabling businesses to make informed decisions in real-time. Thus, this technology aims to dramatically improve human productivity in gathering insights from data and democratize data analytics by making it available to a broad class of users within an enterprise.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.