There is a huge amount of valuable information hidden in a company's database which is not easily accessible to business people. To query these databases, end users need to know the technical query language SQL as well as the database structure. However, typical end users do not have enough SQL skills to formulate complex queries. Even more so, higher-level analytics, e.g. "trend analysis over last month" or "detect outliers in the price fluctuation of product X over the last year" are hard to formulate even for SQL experts. Hence, the majority of nonexpert users are basically not able to explore the available knowledge of their company.Veezoo currently provides a system that can answer natural language queries against databases, with the goal of empowering all users inside a company to become data-driven and benefit from the available information. However, feedback from existing users shows that a wide range of customers completely lack familiarity with their own company's databases. In practice, this leads to a severely limited adoption of systems that provide a natural language interface for databases, given that most users are not aware apriori which questions to ask or on which regions of data to focus, in order to get the most added value from the large amounts of knowledge made available to them. Therefore, in the lack of proactive suggestions, recommended insights, as well as data exploration guidance, only translating natural language questions to equivalent database queries is simply not enough.In this project we tackle this important open issue to make natural language interfaces to databases more suitable for widespread adoption by designing novel algorithms on top of the current Veezoo system, through a service that proactively guides users in exploring the data and augmenting the company's knowledge base. The service, called NQuest, will provide analytics mechanisms that empower a wide range of users to discover new insights in existing databases.