The Data-Centric AI Revolution: Engineering Better Data



The Data-Centric AI Revolution

The Data-Centric AI Revolution

Shifting the focus from tweaking model code to systematically engineering the data that fuels it.

Why the Shift? The Model-Centric Bottleneck

Data Quality is King

85%

of AI projects fail to deliver, not due to flawed models, but due to poor data quality and management.

Data Availability is Shrinking

2026

is the projected year for the exhaustion of high-quality public text data, forcing a move to data engineering.

The Core Principle: The Data Flywheel

Data-Centric AI treats data as a living asset. The goal is a continuous, iterative loop where the model and data improve each other.

1. Train Model

2. Analyze Errors

3. Improve Data

4. Retrain

The Data-Centric AI Toolkit

1. Programmatic Labeling

Use Weak Supervision to programmatically generate noisy labels for massive datasets using expert rules, or "Labeling Functions" (LFs).

The Weak Supervision pipeline transforms noisy rules into a large-scale training set for a powerful end model.

2. Efficient Labeling

Use Active Learning to intelligently select the most informative data points for manual labeling, maximizing model improvement while minimizing cost.

Comparing AL strategies reveals a trade-off between exploiting uncertainty and exploring for diversity.

3. Data Creation

Use Augmentation to modify existing data or Synthetic Generation to create new data from scratch, filling gaps and covering edge cases.

Synthetic data offers more flexibility and better privacy, but augmentation is lower risk.

The Accelerator: LLMs as Universal Data Engines

Large Language Models (LLMs) have become a unifying force in DCAI, capable of performing nearly every data engineering task through natural language prompts.

🏷️

As a Labeler

Replacing coded rules with natural language prompts (PromptedWS).

🔍

As a Selector

Solving the active learning cold-start problem (ActiveLLM).

As a Generator

Creating high-quality, diverse synthetic text data.

⚖️

As an Evaluator

Providing nuanced, human-like judgments on model outputs.

An infographic summarizing the key principles and methodologies of Data-Centric AI.

Source: "A Comprehensive Report on the Principles, Methodologies, and Future of Data-Centric Artificial Intelligence"




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Dataknobs Blog

10 Use Cases Built

10 Use Cases Built By Dataknobs

Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

AI Agent Tutorial

Agent AI Tutorial

Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

Build Dataproducts

How Dataknobs help in building data products

Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

KreateHub

Create New knowledge with Prompt library

At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

Our Products

KreateBots

  • Pre built front end that you can configure
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  • AI powered websites to domainte search
  • Premium Hosting - Azure, GCP,AWS
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