Using Natural Language Queries to Transform Business Intelligence

Imagine you're a sales manager preparing a presentation for a crucial meeting. You're wrangling with data, trying to determine your company's top-selling products in Asia for Q1 2023. Despite your best efforts, you're struggling with complex SQL queries.

Suddenly, you think, "What if I could just ask the computer?" That's where natural language querying (NLQ) comes into play, bridging the gap between complicated databases and simple human language.

What could this mean for the future of business intelligence? We explore that and more in this article.

Empowering Data Democracy with NLQ

In the world of business intelligence, data is king and accessibility is the key to the kingdom. NLQ, powered by artificial intelligence (AI), is transforming how we interact with data, offering insights previously accessible only to data scientists.

One of the greatest challenges with the concept of Big Data is that it requires multiple key players working in unison to generate good insights. Not only do you need data scientists, but you also need a strong and creative management team that can identify what important insights are worth generating. With NLQ, teams can quickly experiment with the insight they want to generate to unlock actionable data more quickly.

However NLQ technology backed by AI solutions like IBM Watsonx.data and , is changing the game. These platforms use AI to automate data governance, making it easier and more cost-effective to restrict data access based on permissions.

This means small teams can now access only the data they're authorized to see, allowing them to generate valuable insights without the need for expensive development projects or specialized data scientists. The result is a democratized data landscape where all teams, regardless of size, can make informed decisions swiftly and securely.

Using NLQ for More Than Just Data

NLQ is not just about crunching numbers, it's also about understanding emotions. Google Cloud's Dialogflow uses NLQ to provide empathetic customer service, creating an emotional connection that was once missing in automated interactions.

Consider the case of Domino’s Pizza's virtual assistant, "Dom." By understanding and responding to customer queries in natural language, Dom has revolutionized the ordering process. A customer, being able to customize their pizza with a simple conversation instead of cumbersome user interfaces, is a testament to the power of NLQ.

However, the technology isn't flawless. Misinterpretations can occur, and the AI may not fully grasp the context, leading to customer frustration. Striking a balance between efficiency and empathy remains a challenge for NLQ in customer service use cases such as chatbots.

Potential Challenges with NLQ

We've seen IBM's Watson assist doctors by understanding complex medical queries. This is just a glimpse of NLQ's potential. The question isn't whether NLQ will impact our future, but rather how we will harness its potential responsibly and ethically.

The challenges range from ensuring data privacy to dealing with AI biases. As we strive to improve NLQ, we must also ponder on these ethical considerations, recognizing that technology is a tool, and its value depends on how we wield it.

How to Use NLQ Driven Insights

The advent of NLQ isn't just a boon for large corporations; it's also a game-changer for small to medium-sized businesses (SMBs). Platforms like ChatGPT have already laid the groundwork by translating basic prompts into SQL queries. The next wave of NLQ technology will integrate directly with business data infrastructure, speeding up the process of business intelligence development and making it more accessible than ever.

To fully leverage the capabilities of NLQ, the quality of the underlying data and AI models is crucial. Therefore, the processes of data cleansing and model training should be top priorities. SMBs can turn to cost-effective data infrastructure solutions such as IBM’s and to prepare their systems for effective NLQ deployment. By ensuring high-quality data, businesses will be in a better position to gain actionable insights through NLQ.

Implementing NLQ Policies

Starting your journey with NLQ might appear overwhelming, yet taking that initial step is critical. Bring together a multidisciplinary team—comprising technology leaders, digital transformation experts, and representatives who understand your organization's unique needs.

Consider establishing a 'Center of Excellence' focused on your digital initiatives. This specialized group can align organizational objectives and help you navigate toward actionable insights and enhanced operational efficiency.