How NLQ and Kyvos are Empowering Organizations with Self-Serve Analytics

Ever heard the saying, “Give a man a fish and you feed him for a day; teach him to fish and you feed him for a lifetime?” It perfectly captures the essence of self-serve analytics. Today, organizations realize that self-serve analytics isn’t just convenience; it’s a cultural shift that empowers everyone in the organization to “speak with data” in their own way.

Traditionally, when employees depended on data teams for insights, it usually led to delays and felt like running in slow motion. All the requests for analysis reports had to go through multiple levels, from specifying requirements to waiting for data teams to deliver results. By the time insights reached business users, they were often too late to act on. This dependence on data teams didn’t just slow things down but also confined the scope of data exploration for employees. Valuable questions went unanswered due to time and resource constraints and hindered organizational growth. Then, organizations turned to self-serve BI tools, thinking this might be the end of their struggles as they gave them the power to create their own reports. However, these tools too came with a steep learning curve which required hours of training to get the hang of things. Even then, business users were stuck working with predefined filters and rigid query structures that limited their accessibility. If they want answers to complex questions, it requires writing calculations or manipulating data in ways that were challenging for non-technical users.

This made organizations realize that they should opt for a technology like natural language processing that can help business users who don’t have technical knowledge interact with their data for insights.

What Is NLP?

While reading any text, the reader forms some sort of comprehension in their mind and when we ask a computer to do that, it is called natural language processing or NLP. The process of making sense of the language starts with unstructured text. For instance, if we say, “Add bread and milk to my shopping list,” now we know what this sentence actually means, but for a computer, this is unstructured text. The structured text will look something like this:

Bread</> <Item>Milk</> </>NLP translates unstructured data into structured data. It finds the relationships between letters, words and sentences in a dataset and recognizes the context, intent and even sentiment of the question asked. Because of this understanding, a computer can interpret and manipulate text or spoken words. Ever asked Alexa or Siri to play a song or set an alarm? That’s NLP in action.

But this isn’t all. The technology gets more interesting. Imagine asking, “What were the top-performing products last year?” and getting instant answers without the hustle of going through several dashboards or writing complex queries. That’s the magic of natural language querying (NLQ).

How Does NLQ Work?

While NLP is the foundation for making sense of human language, natural language querying is one of its applications that focuses on using this linguistic understanding to identify the intent behind the question asked and translate it into a structured query that a database or analytical system can understand. For instance, picture a marketing manager having a massive dataset filled with details about every email sent over the past year gearing up for a campaign review. The dataset has all the details like open rates, click-through rates and conversions. The manager needs to identify the campaigns where customers in Pittsburgh had an open rate above 30%, which led to at least 50 purchases during the holiday season. There are two ways to find the answer:

Option A involves writing an SQL query for a database, which might be difficult for a manager without a technical background. Even with traditional BI tools, non-technical users require some degree of familiarity with the tool and have some knowledge of SQL. It’s challenging for them to perform complex analysis like querying data with multiple conditions where joins, aggregations or relationships are involved. So, no matter how self-serve the BI tool is, they still rely on technical teams somehow. On the other hand, option B facilitates the manager to simply ask this question in everyday language and gives them access to organizational data for insights without any barrier.

How is this happening? It’s a combination of two key players, NLQ and NER. When a user asks a query, the system processes it by using NLP techniques such as tokenization, part-of-speech tagging, lemmatization, etc., to understand the context. It first breaks the query into meaningful components, identifies verbs, nouns, etc., and understands the root forms of words. Then named entity recognition (NER) helps the system make sense of specific categories of words in a sentence, like names, places, dates or numbers.

For instance, if a user asks, “What were the sales in New York last month?” NER would highlight that “New York” is a location and “last month” is a time frame. This is how the system determines the intent of the question. Then, it converts natural language into a structured query that can interact with databases or data sources. The system executes this query and extracts relevant insights from the data source and presents it in a user-friendly format like charts, tables or even conversational responses. This helps all users to create informative reports and make effective decisions—without the help of data scientists.

So far, we have looked at how natural language querying enables users to ask a single question and get an answer, but is that enough to build a self-serve culture in an organization? What if they want to dive deeper into their data and ask ensuing questions as new insights emerge? This is where conversational analytics comes into play.

How Kyvos Uses NLQ to Enhance Self-Serve Analytics

Kyvos, with the help of NLQ, allows users to have a conversation with their data. For instance, after asking “What were the sales in Q2?”, users can ask related questions like “Which products contributed the most to these sales?” It offers business-friendly insights at scale and empowers business users to interact with KPIs more meaningfully and get a complete story from their data, which can help them make data-backed decisions on time.

Here’s how:

Kyvos Dialogs: a chat interface for business users

When users ask questions in natural language, Kyvos Dialogs understands these questions and identifies the most pertinent semantic model to provide relevant answers. In addition, it remembers the context of previously asked questions and generates responses keeping that in mind. Because of this ability, users can backtrack to any previous point in their conversations, perform in-depth analysis, revise queries and explore parallel tracks. Kyvos Dialogs also suggests prompts automatically based on previously asked questions.

Natural language summarization

Imagine how simple a decision-maker’s life would be if they received custom summaries sent directly to them via email. Kyvos Dialogs scans the complete data, identifies issues or patterns and generates summaries. It eliminates the need to sift through multiple dashboards manually.

Text-to-insight

Kyvos also offers a copilot that acts as a KPI designer assistant for simplifying the process of generating metrics. With this capability, data teams can explain which KPI they need and Kyvos Copilot will convert it into MDX or SQL formulas and calculations. This way, users get insights themselves without any need to learn query languages or manual query writing. Once these expressions are created, users can leverage Kyvos’ query playground to query their data and get insights in the form of visualizations like charts, tables and more. It’s perfect for power users who need insights without having to write complex queries.

Final Thoughts

Organizations today are well aware that scaling analytics capabilities across the enterprise can boost productivity, making every employee focus on strategic tasks with faster access to insights. Kyvos, with the help of NLQ, can strengthen the data democratization culture by making data accessible to all users and enabling them to focus on high-impact tasks, all while enhancing collaboration and agility across the business.