Smart data discovery is an AI-driven approach to finding actionable insights from complex datasets. Unlike traditional methods that rely heavily on manual processes, smart data discovery automates much of the work. It uses machine learning and artificial intelligence to analyze patterns and highlight relationships in data that may otherwise go unnoticed.
AI-driven tools can sift through millions of data points in moments to identify key patterns and trends to support better decision-making. By automating data discovery, organizations can ensure their data is both actionable and ready to power AI systems. Simply put, it bridges the gap between raw data and meaningful insights.
The evolution of data discovery
Data discovery has come a long way from its traditional roots, where we had to extract insights manually. It required technical expertise and a good amount of time investment, which delayed the analyses and increased the chances of human error.
As data grew exponentially, traditional methods became impractical because they couldn’t scale to meet the demands of modern businesses. To address this challenge, Gartner came up with an AI-driven approach, which it referred to as a next-generation data discovery capability.
Powered by machine learning, smart data discovery transformed how we analyze large volumes of data. Its tools made it possible to analyze huge data assets in real time and provide actionable insights based on the patterns between data.
Let’s take the example of Lyft — they developed Amundsen — a data discovery and metadata engine. Amundsen provides a centralized search interface through which data scientists can quickly find datasets and identify data owners. This streamlined access and refined their analyses based on accurate and well-documented data sources.
Benefits of smart data discovery
Smart data discovery makes AI-driven business growth possible using modern data management practices. Here’s what your business can also achieve with smart data discovery:
Efficiency: Automates repetitive tasks like data preparation and visualization to free your team from more important tasks.
Actionable insights: Highlights important patterns and anomalies within data to help you act on opportunities and challenges. For example, Netflix uses smart data discovery to identify viewing trends and provide real-time recommendations based on viewers interests.
Accessibility: Allows even non-technical users to access and understand data. This creates a highly data-driven organization that is 3x more likely to make better decisions.
Scalability: Integrates data from multiple sources and adapts quickly to your growing demands.
Improved decision-making: Generates high-quality insights to provide a reliable foundation for data-driven strategies. Take UPS for example — it saved 10 million gallons of fuel annually by leveraging AI-powered tools to optimize delivery routes.
Common use cases
Smart data discovery has already become popular across several industries, helping them automate multiple business processes. Let’s look at some of them:
Healthcare
Smart data discovery integrates unstructured data, such as patient records and medical details, with structured datasets. Then, it analyzes patterns in this raw data so healthcare providers can predict patient needs.
One of the top healthcare organizations in the US, Mayo Clinic, developed a multimodal, spatiotemporal graph neural network (MM-STGNN). It predicts 30-day all-cause hospital readmissions by fusing in-patient longitudinal data and modeling patient similarity using a graph. It provided more comprehensive and personalized insights into patient health, which enabled proactive patient care and reduced unnecessary hospital stays.
Finance
Financial institutions use smart data discovery to identify patterns in transaction data for fraud detection and risk management.
JPMorgan Chase built their platform called OmniAI to solve data scientists' issues. By doing so, they standardized data management processes and provided the security and controls needed for working with sensitive information.
Retail
Retailers use data discovery to analyze customer sentiments from social media posts and feedback. These insights help tailor marketing strategies and better inventory management.
Walmart uses digital twin technology to optimize store layouts. It simulates different configurations to improve customer shopping experiences and store efficiency.
Marketing
Great marketing strategies require extensive research of customer behaviors. Smart data discovery can make this easy by analyzing large datasets from multiple sources, like purchase history, social media activity, and website interactions. This analysis helps marketers target different customer segments based on their preferences.
How smart data discovery work
Here’s how smart data discovery simplifies data discovery and classification with AI-driven automation:
Integrate multiple data sources
Smart data discovery tools collect data from multiple sources like databases, data silos, cloud storage, and third-party APIs by using ETL (Extract, Transform, Load) pipelines.
To make this data ready for analysis, it uses the following techniques:
Schema mapping: Aligns and translates data structures from different sources into a consistent format.
Data virtualization: Provides a unified view of data across multiple sources without physically moving or duplicating it.
Prepare and cleanse data
Several cleaning techniques are used to improve data quality, such as:
String matching and regex parsing: Standardize inconsistent text inputs.
Statistical models: Predict and replace missing numerical data from historical trends or related fields.
Outlier detection: Identify and flag inconsistent data points using Z-score or IQR-based filtering techniques.
Identify similar patterns
After data cleansing, machine learning models analyze datasets to reveal trends and anomalies. Here’s how this process works:
Unsupervised learning methods: Group similar data points or reduce dimensionality for pattern recognition using clustering (e.g., k-means) or principal component analysis (PCA).
Time series analysis: Detects unusual spikes or dips in temporal data using models like ARIMA or LSTM.
Anomaly detection algorithms: Identify data points that deviate from the norm, which could signal errors or opportunities.
Presents insights visually
The final step is to convert complex analysis into actionable insights through visualization. Smart data discovery tools have features like dynamic dashboards and reports that support the following:
Drill-down capabilities: Allows users to explore granular data points.
Customizable charts: Tailors visual outputs to business needs with heatmaps, scatter plots, and geospatial maps.
BI tools integration: Increases interactivity in visualizations with tools like Tableau or Power BI.
Let’s say you work with a financial organization that wants to detect fraudulent activities. Here’s how they’d use smart data discovery for this:
First, they will integrate all customer transaction data from both internal records and external fraud detection systems using API pipelines. Next, they would use data preparation tools to cleanse the records by resolving duplicate transactions and inconsistent formats.
Once the data is ready, ML models will analyze it to identify suspicious patterns, such as clusters of high-value transactions in unusual locations and rapid withdrawals from multiple accounts. Anomalies are flagged for further investigation, and predictive analytics estimates the likelihood of future fraud. Then, the data analysis team will represent the insights in an intuitive dashboard to highlight fraud trends and high-risk customer segments.
Key features of smart data discovery
Let’s explore the key features that make smart data discovery different from traditional data discovery:
Automated data preparation and cleansing: Automatically cleans and organizes raw data for analysis through AI, which quickly removes duplicates and fills in missing values.
Advanced pattern recognition and correlation identification: Finds hidden patterns and correlations in big datasets using machine learning algorithms to identify opportunities and risks.
Automated visualization and insight generation: Transforms complex data into interpretable visuals and smart insights with the help of AI powered dashboards and reporting tools. This makes it easy for users with different technical expertise to understand data.
Natural language processing: Uses NLP to allow users to interact with data using conversational language, like typing or speaking questions.
Implementing smart data discovery
50% of organizations reported competing on data and analytics in 2023, while 77.6% claimed to drive innovation through data. These numbers highlight a growing reliance on data to make business decisions. To remain competitive, organizations must implement strategies like smart data discovery so they can turn raw data into actionable insights.
So, if your organization also wants to leverage the full potential of your data assets, here is how to implement smart data discovery:
Assess business needs: Identify specific challenges and goals to determine where smart data discovery can add value. For example, are you struggling to understand customer behavior or streamline operations?
Choose the right tools: Select platforms with AI-driven features, user-friendly interfaces, and scalability to discover data without limits.
Integrate with existing systems: Create smooth connectivity between smart data discovery tools and current databases or CRMs using data cataloging platforms.
Prepare data infrastructure: Clean and organize existing data to create a strong foundation for analysis.
Train teams: Provide technical and non-technical users training to maximize adoption and effectiveness and promote a data-driven culture.
Monitor and iterate: Evaluate your smart data discovery tool's performance and optimize workflows for better insights and ROI.
The role of data catalogs in smart data discovery
A data catalog is like a central hub for all your data assets where you can manage, organize, and easily access them. Its primary purpose is to provide a clear and structured data overview where every piece is categorized and labeled for quick discovery.
By organizing and contextualizing data, catalogs make smart data discovery faster and more efficient. They categorize datasets, add metadata tags, and provide clear descriptions. For example, if your team needs regional sales figures, a catalog will direct them to the relevant dataset and explain its structure or updates.
Metadata management is a key feature of data catalogs that improves the searchability of data. Its relationship with data catalogs enhances how teams analyze and leverage data. When metadata highlights relationships between datasets, smart data discovery tools identify patterns and correlations more effectively. This connection reduces manual data preparation so teams can easily interpret results and generate insights.
While there are many platforms available, data.world provides AI-driven smart data discovery solutions. It provides a knowledge graph-powered data catalog that makes it easier to discover insights from piles of data assets with natural language queries.
Our AI-driven capabilities automate data discovery and eliminate the need for tedious manual tasks. And our centralized data catalog can integrate with your workflows to gather data from different storage locations. Then, you can use its user-friendly interface and robust collaboration tools to make data-driven decisions.
Ready to see data.world in action? Schedule a demo today and leverage the advantages of smart data discovery now!
