Kyvos: Revolutionizing Business Intelligence with AI Models

Today, when information is abundant, but insights are scarce, business intelligence (BI) tools act as a powerful ally for organizations and guide them through the endless maze of data to turn raw information into actionable insights. From financial forecasting to customer behavior analysis, modern enterprises use analytics to identify trends, patterns and opportunities that can guide them towards making strategic choices and staying ahead of competitors.

Modern BI tools such as Kyvos Reporting, Power BI, Tableau, Looker and others offer self-service, advanced analytics capabilities that enable users to analyze data scattered in multiple sources and make informed decisions. However, despite their strengths, many organizations feel more adrift from insights than enlightened because only understanding what already happened is not enough. Anticipating future trends and capitalizing on emerging opportunities is what truly differentiates high-performing businesses.

The Limitations of Traditional BI in the Age of Big Data

The ever-growing data volume fueled by structured as well as non-structured data mustered from disparate sources is putting traditional BI systems to the test. These tools often struggle to identify complex relationships and trends within the data. For instance, suppose a retailer wants to understand why there is a surge in sales of barbecue grills in the northeast region. Such questions require delving deeper into massive datasets. However, BI tools fall short in identifying complex correlations between multiple variables, leading to sluggish response times. Ultimately, the analysts had to manually generate reports by comparing sales figures against various factors, such as comparing sales of grills to other products, weather data, promotional activities, etc. This manual process is time-consuming and yields limited insights.

And what’s making the matter worse is a scarcity of skilled personnel capable of handling advanced data analytics tools to tackle complex datasets. Apart from this, the need to get instant answers from data is overburdening data analysts and IT experts with requests from business users to examine various metrics or datasets.

The advent of AI offers promising solutions to address the shortcomings of existing BI tools. Platforms that leverage AI models have emerged as a potential solution to overcome these challenges and enable businesses to extract actionable insights and make informed decisions with greater speed, accuracy and confidence.

How AI Models are Transforming the Analytics Landscape

Although BI tools are adept at providing insights into historical data, their predictive power is constrained by the need for external integrations and programming expertise, adding complexity to the analytical workflow. Business users need answers at a speed at which they can find out the current stock price of a company by asking Google or Alexa. If they can find out the stock price of companies in seconds why not yesterday’s sales figures?

The question today should not be whether an organization must adopt AI. Instead, they should determine the optimal implementation strategy.

AI and ML harness the power of advanced algorithms and computational processing to accelerate the analysis process and generate accurate insights at unprecedented speeds. Many progressive organizations are harnessing the power of these technologies by using AI models. It’s essential to understand which model to use to drive this transformation since each has its unique strengths and applications.

Let’s have a look at the top AI models that are powering business analytics:

• Linear Regression Model: It is an AI model that uses statistical methods to analyze relationships between dependent and independent variables and make predictions. For instance, banks use this model to assess risks related to financial loss by identifying customers who are more likely to repay their loans. The AI model assesses factors like a customer’s income, debt and credit history to help banks make better decisions about who to lend to and how much risk they are taking on. These independent variables are assigned numerical values and are fed to the linear regression model to calculate coefficients for each variable. The model calculates the coefficient based on the impact each independent variable has on the dependent variable, i.e., the probability of a customer defaulting on a loan.
• Logistic Regression Model: This type of AI model is used to predict a binary outcome based on independent variables. This model is similar to linear regression structurally but differs in its purpose. Instead of predicting continuous values, it is designed for classification problems. For instance, businesses can use a logistic regression model to predict customer lifetime value (CLTV) by quantifying the total revenue a customer is expected to generate throughout their relationship with the company. Businesses can categorize their customers into high, medium and low CLTV segments. Unlike any other AI model, logistic regression model is good at providing probabilities of something happening or not happening.
• Decision Tree Model: Like logistic regression, a decision tree is also a classification algorithm. The difference is logistic regression handles continuous and categorical variables, while decision tree handles numerical and categorical data. It can create a tree-like structure with decision and leaf nodes where each decision node splits the data based on specific features. These splits create branches representing different outcomes. The resulting tree can be translated into a set of if-then rules that clearly outline the conditions. This type of AI model can be used by banks for fraud detection as it offers a balance between accuracy and interpretability and where understanding the reason behind a decision is crucial.
• Neural Networks: These AI models comprise numerous layers of interconnected nodes. A supply chain giant can integrate a neural network model with their BI tool and train it on massive datasets that include historical data like marketing spend, past sales, economic indicators, competitor information and sales during holidays. The AI model can help understand the complex relationships between these factors to optimize inventory levels, production schedules and resource allocation.
• Large Language Model: LLMs are intelligent assistants that understand and can generate responses in human language. These AI models are trained on enormous datasets and can answer questions, generate summaries and even write different kinds of creative text. Businesses can use these generative models to empower non-tech employees to generate reports and identify problems, improve products, create better customer experiences and do much more.
  These AI models are evolving BI from a static reporting tool into a predictive powerhouse. Therefore, organizations should opt for a solution that embraces AI and helps them unlock the full potential of their data.

Kyvos Enhances BI with AI

Kyvos, a Gen AI-powered semantic layer is at the forefront of this revolution. It has been pioneering advancements in BI and analytics for years. It is the first and only semantic layer that provides LangChain connectivity. The layer consolidates data from diverse sources, organizes it into a cohesive structure and serves as a reliable data source for downstream Gen AI applications and LLMs. As a result, LLMs generate context-based outputs, eliminating the risk of hallucinations.

Additionally, its innovative Kyvos Copilot harnesses the power of generative AI to create a seamless and intuitive data exploration experience by:

• Automating data model selection
• Providing context-aware interactions
• Enabling easy data exploration
• Enhancing data trust, governance and decision-making

Essentially, unlike traditional BI tools, Kyvos bridges the gap between technical experts and business users transforming the way users interact with data. Let’s see how Kyvos enhances BI with AI:

Kyvos Enables Businesses to Handle Massive Datasets at Unprecedented Speeds

Data ingestion is the first critical step in the analysis journey. Since BI tools often struggle to keep pace with the explosion of unstructured data, Kyvos overcomes this challenge by seamlessly integrating with preferred data platforms, BI tools, connectors or interfaces. This versatility empowers organizations to scale limitlessly without compromising performance.

Kyvos’ AI-powered smart aggregation technology creates massively scalable data models and its intelligent aggregation with AI-driven self-tuning enables sub-second queries on massive datasets for thousands of concurrent users with no performance degradation. The platform standardizes business logic across different BI tools for consistent cloud-based analysis.

Additionally, Kyvos’ smart recommendations engine brings in the intelligence required to build optimized data models. The platform understands data and query patterns using features like advanced data profiling, partitioning strategy and query analyzer.

AI-Driven User Experience with Kyvos

Just as consumers have grown accustomed to the effortless search experience offered by Google, they now expect the same level of intuitiveness from their BI tools. Why should extracting insights from complex datasets be a convoluted process in a world where finding a recipe or booking a flight is as simple as asking a virtual assistant? AI can make this possible with the help of NLP and AI language models. These technologies can interpret human text, understand the context, identify key points, entities and sentiments to create new text formats, such as summaries, reports or code in response. While many of these technologies are still in the experimental stage, some BI tools have put these features in use. However, the integration of generative AI models into BI tools might present some challenges like hallucinations, lack of context-based responses and knowledge base limitations.

These challenges can be addressed by using Kyvos Copilot. It acts as an intelligent assistant that understands user queries, selects appropriate data models based on the definitions and relationships between data points defined by Kyvos semantic layer and enhances understanding of LLMs. This way, it eliminates the tedious task of scanning countless KPIs and dashboards, empowering users to ask questions using plain business terms and get answers in natural language. Additionally, Kyvos also provide guided data exploration by keeping track of previous questions and responses based on the ongoing conversation to help them discover relevant content or insights.

Uncover Hidden Insights with Kyvos

AI-based intelligent algorithms can automate the sifting process of massive datasets in seconds and provide analysts with hidden patterns and insights that humans might miss. Kyvos addresses this challenge with its AI-powered smart aggregation technology, which processes all combinations in advance, making it possible for analysts to analyze years of historical data in seconds. It empowers enterprise-wide users to perform multidimensional analysis and engage with KPIs more efficiently by building optimized data models with minimal effort. This way, business users can conduct self-service data analysis on complex data structures such as hierarchical schemas, calculated members, many-to-many relationships or even semi-additive measures using the BI tool of their choice.

Summarize and Enhance Analytical Results with Kyvos

Discovering insights and communicating them are two interconnected pillars of successful data analysis. BI tools are good at creating visual representations, but they lack the ability to effectively provide context and interpretation. For instance, a chart showing sales trends over the past year. While the BI tool effectively visualizes the data, it doesn’t explicitly convey the key takeaway: “Sales increased by 20% in the fourth quarter due to the successful launch of the new product line.” Analysts often need to manually add explanatory text beneath the chart to ensure the audience grasps the essential message. If the analysts miss the message, it could lead to misinterpretation or misunderstandings, especially for users who may not have a deep understanding of the data.

Kyvos provides natural language summarization for users to comprehend the key patterns derived from their queries. It generates summaries that include notable changes or streaks. This way, Kyvos makes BI more accessible to non-technical users and enhances analytical results.

While traditional BI tools offer valuable capabilities, their limitations in handling vast datasets, extracting complex patterns, and providing intuitive user experiences have become increasingly apparent. Kyvos emerges as a transformative solution, addressing these challenges head-on. It empowers organizations to unlock the full potential of their data and drive data-driven decision-making. As the landscape of business intelligence continues to evolve, Kyvos stands at the forefront, redefining the possibilities of what BI can achieve.

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