As a globally operating IT company, collaborating with developers and clients across diverse regions, we can confidently attest to the foundational significance of databases in our line of work. Indeed, databases stand tall as one of the essential pillars supporting our operations and driving our success. However, we encountered some challenges while handling certain projects, particularly when clients requested numerous reports. The bottleneck arose from the fact that only a few individuals possessed the expertise to efficiently manage databases. As we delved into finding a viable solution, one particular journey stood out – the collaboration with our machine learning department, venturing into the realm of AI. Through this partnership, we embarked on developing a cutting-edge system that empowers users well-versed in business rules and intricacies, but possessing limited technical expertise, to seamlessly access and extract valuable insights from databases, utilizing the power of AI's latest advancements. In this blog post, we will explore this remarkable transformation. Let's embark on this exciting journey together!
The challenge of extracting data from a database
In the realm of IT projects, working with databases can present a multitude of challenges, ranging from identifying the information that is accessible to acquiring the required data, all the way to the necessity of individuals possessing highly specialized technical expertise. These hurdles can significantly impede the efficient and effective utilization of databases.
Another critical issue is associated with the configuration required to access the database and retrieve information accurately. This process necessitates specific authentication data to identify both the target database and the user attempting access. Additionally, varying user permissions add complexity, making it a demanding task to effectively manage authentication data while striking the right balance between permissions and security.
Managing data accessibility emerges as a crucial challenge, especially as the organization grows and generates a vast amount of data. This exponential growth leads to the development of a complex database, making it increasingly difficult to retrieve the necessary information. Additionally, the diversity of information required by different departments within the organization, coupled with limited access to the database, contributes to a time-consuming and tedious process, primarily entrusted to only a select few authorized individuals.
IT projects involving big databases require substantial resources, including hardware, software, and skilled personnel. Also, even experienced developers may encounter situations where they struggle to recall the precise steps needed to retrieve highly specific data, or they might succeed in doing so but end up investing an excessive amount of time in the process.
On the other hand, we found ourselves in situations where we have complex databases with a vast amount of information, accessible to only a few developers and the clients express interest in obtaining a large volume of data that is not readily accessible, leading to bottlenecks and delays in the process.
Taking all the aforementioned factors into consideration when working with databases, in the following section, we will delve into the explored paths and the resulting decisions.
Different paths to face the problem
To tackle this challenge head-on, we conducted a brainstorming meeting, bringing together the collective expertise and insights of our team members. During the session, diverse perspectives were shared, and a multitude of potential paths to follow were explored:
- Internal training: The goal is to equip more individuals within the company with the knowledge and skills to access and maintain the complex database. By expanding the pool of capable individuals, we can lessen the dependency on the few experts currently managing it. This includes conducting workshops, hosting training sessions, and providing online courses to empower our team members with the necessary expertise. Through these training initiatives, we try to enhance the database's efficiency, ensure prompt responses to customer requests, and foster a more agile and self-sufficient approach to database management.
- Documentation: Implement a robust documentation and guides system, which is consistently maintained and updated. By providing comprehensive and up-to-date documentation, we aim to enable developers to quickly and easily grasp the intricacies of the database. This approach ensures that team members can efficiently work with the database, leading to enhanced collaboration, minimized errors, and more streamlined development processes.
- Hire additional experts: By bringing on board individuals with the ability to quickly learn and adapt to the database's structure, we aim to reduce the workload of the current small group of personnel handling it.
- Identify repetitive report requests and automatize them: identify reports that are frequently requested by clients and subsequently automate their generation. By automating these reports, we eliminate the need for manual execution each time, saving valuable time and resources. With an automated system in place, our team can focus on higher-value tasks while meeting customer demands more effectively and providing faster responses overall.
- Implement a system of periodic audits and reviews: Through scheduled assessments, we evaluate the database's performance, configuration, and utilization. By identifying potential bottlenecks, inefficiencies, or areas for improvement, we can promptly address any issues that may hinder the database's responsiveness.
After these discussions, a new alternative arose that caught our attention since it is an area that is being incorporated into almost every problem these days.
As the AI field continues to rapidly grow, we knew that tapping into the expertise of our Machine Learning department would be crucial. With experienced data scientists, we engaged in fruitful discussions to harness their insights and experience. It was through this collaborative effort that we discovered a promising avenue that capitalized on the power of AI. The main idea of this solution was clear: A user-friendly platform that utilizes AI to comprehend the database structure and accept queries in natural language. The goal was to empower not only experienced professionals but also individuals with limited technical expertise to effortlessly access and manipulate data. In the next section we will go deeper into this path.
Introducing AI-DrivenQuery for Instant Business Reports
AI-DrivenQuery, our AI-powered tool transforms natural language queries into actionable insights, eliminating the need for complex SQL processes. Generating business reports faster than ever, with streamlined steps that ensure up-to-date, faster data accessibility whenever you require it.
This tool seamlessly connects users with any database, regardless of technical expertise. Simply ask questions in plain language and witness as the tool instantaneously translates them into SQL queries and retrieves the information you need executing these queries generated by AI-DrivenQuery . The results are visually represented in dynamic tables, revealing crucial trends. Additionally, records can be easily downloaded in a user-friendly format, fostering effortless sharing and collaboration.
In essence, our tool bridges the gap between business intuition and technical intricacies, empowering business professionals to effortlessly extract meaningful insights for data-driven decisions. By freeing up developers from the burdens of report creation, it maximizes efficiency in both realms.
How it Works
Imagine you are the Director of a successful music broadcast company that specializes in providing high-quality products and services related to the music industry. Your company is known for its exceptional offerings, which have garnered a loyal customer base. While your business is thriving, you recognize the need to further expand and grow your operations.
In this case study, we will employ a publicly accessible database called Chinook to showcase the utilization of AI-DrivenQuery. Within this database, you will discover details pertaining to tracks, albums, artists, customers, invoices, and additional information. If you seek more comprehensive insights, you can refer to this link.
One challenge you face is the complexity of your company's systems and databases, which contain a wealth of information that you want to tap into for strategic decision-making. However, you lack the technical expertise to navigate and process this vast amount of data effectively, or even if you know how to do it you may want to empower your analytics thoughts.
Fortunately, you've recently discovered a new tool, AI-DrivenQuery, that has the ability to interface with your existing databases and extract meaningful information in a user-friendly manner. With this tool, you can now address your challenge of gaining insights from the data without requiring technical expertise. In this scenario, the specific information you seek is to identify customers who have made purchases of at least 30 tracks.
By engaging AI-DrivenQuery, you can simply input a query or request, such as "Get the customers that have bought at least 30 tracks. I want only the first name, last name, and the amount of purchased tracks." The system, leveraging its integration with your databases and its natural language processing capabilities, then searches through the records and quickly compiles a list of customers who meet the specified criteria.
This newfound tool allows you to access valuable customer information that was previously untapped. Leveraging the information within the database, you have the ability to pose a variety of inquiries to the tool. AI-DrivenQuery will then formulate and execute the corresponding SQL queries. In the screenshot below, you can observe an example where the user requested:
“I want to get a list of customers with the number of tracks that each person bought on the platform and the total spent money by each customer. Arrange the report in descending order based on the total amount spent”
obtained the results, and viewed the query generated by the tool.
Furthermore, we can gather insights about your top-selling tracks by making an inquiry such as: "Could you please provide me with the list of the ten most frequently purchased tracks?".
In summary, like the Director of the music broadcast company, you may face the challenge of extracting meaningful insights from complex databases without the technical know-how. The AI-DrivenQuery tool solves this problem by providing a user-friendly interface to interact with the data, ultimately allowing you to access critical information. This tool can empower you to make informed decisions and drive your company's growth to the next level.
Conclusions
We've come to the end of this blog post where we've been diving into the significance of databases in the world of software development. We chatted about a common problem our IT company encountered—managing databases in projects with many report requests, and only a small group of developers knowing how to get data from them. Out of the solutions we discussed, we opted for using AI. We made a user-friendly web app that lets non-tech folks get important info from databases by simply asking in everyday language. The outcome? An app that tackles this issue using AI. We even shared some real-life examples to illustrate. We hope you've enjoyed this article, and please stay in touch!
Authors: MARCOS IRIARTE, BRUNO MICHETTI & NICOLAS ALEGRE
