Improving your Data Models with AI

Dear Reader…

As AI integrates with back office processes and Data Engineering workflows, this week we thought it prudent to examine how conceptual models can be developed better, faster and more efficiently. In 2025 - as data models need to be optimised to work with various LLM’s on a day-to-day basis - this especially relevant.

The advent of low-code/no-code platforms is changing what becomes possible between design, execution and operation phases of managing data. We look at a platform from Dsharp.fi that is bridging the gap between concept and realisation in information management. Here are five ways you can use AI to build better conceptual models.

🏭 1. Automated Data Mapping and Transformation

For Data Engineers, few tasks are as time-consuming as mapping source data from various operational systems to structured data warehouse schemas. AI can dramatically reduce the manual overhead in this process by automatically interpreting source metadata, scanning for patterns, and generating transformations that cleanse and standardise data. Using AI tolls for data mapping means the process can be completed in hours, not weeks, and the ensuing transformations become easier to maintain or extend. Making data pipeline more consistent, reducing human error, and accelerating data warehouse development.

A practical tool for this purpose is Secoda, which leverages generative AI to automate parts of the data mapping and transformation process. Suppose a multinational organisation has dozens of CRM systems feeding a central data platform. Secoda’s AI engine can discover similarities in field naming conventions, identify inconsistent data types, and propose standardised transformations. You can then review and approve these AI-driven mappings, saving hours of coding and improving the final data model’s consistency.

🎱 2. Enhanced Predictive Modelling with AI

Predictive modelling is at the heart of data analytics, helping enterprises forecast demand, detect fraud, or anticipate machine failures. Traditional predictive modeling requires teams to manually select features, tune hyperparameters, and iterate on results. AI — including deep learning approaches — can automate both feature engineering and model selection by learning from historical data in ways that surpass human intuition, delivering more accurate models built in a fraction of the usual time.

This matters because LLMs often operate alongside or in tandem with predictive models, especially in real-time analytics scenarios. When both your predictive models and your LLMs feed on robust data stored in your data warehouse, synergy forms. You might have an LLM summarising insights from the predictive model outputs, offering business executives an easy-to-digest narrative of how certain markets or products will behave.

One prominent platform is Databricks with its AI-driven capabilities for developing advanced analytics pipelines. For instance, a global retailer could use Databricks’ AutoML features to create predictive models for forecasting holiday-season product demand. The result can be fed into an LLM for generating plain-language summaries on which product lines are expected to see the highest uptake, providing an end-to-end analytics and reporting pipeline that merges advanced ML with human-friendly explanations.

💨 3. Real-Time Data Quality Enhancement

At the core of any successful data warehouse is a rigorous commitment to high data quality. However, in large ecosystem environments, data arrives from a myriad of sources: IoT sensors, social media feeds, and legacy transactional systems. AI algorithms can monitor these data streams, detect anomalies, spot duplicates, and correct inconsistencies as they happen. This real-time monitoring ensures that your data warehouse becomes a trustworthy source for downstream analytics.

Platforms like Striim make it possible to stream data from varied sources into a data warehouse while applying AI-driven data quality checks. Picture a logistics company monitoring real-time data from thousands of delivery trucks every second. Using Striim’s streaming pipeline, AI-based rules can flag suspicious readings e.g., a truck apparently traveling twice the legal speed limit, correct erroneous data e.g., isolate entries that are obviously out of normal range, and store only consistent, reliable information in the warehouse. As a result, any LLM that queries this data will have a much truer picture of real-world events.

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🚀 3. Scenario Simulation & Data Structure Optimisation

One challenge in building or scaling an enterprise data warehouse is deciding on the optimal structure such as star schemas, snowflake schemas, data vault, along with the indexing strategies. AI can analyse historical queries, usage patterns, and relationships in your data to propose the most efficient schema or to guide how best to partition data. The same AI can also run scenario simulations to see how your data model might respond if data volume doubles or new data sources appear. Data Engineers can use these simulations to pinpoint bottlenecks, reduce query latency, and optimise future capacity needs.

Using WhereScape’s automation tools, which are designed to optimise data models, enterprises gain consultative AI-driven insights on how to reorganise tables and indexes. For instance, an e-commerce giant analysing billions of rows of historical sales might find queries slow in its star schema. WhereScape’s AI-driven advisor flags which tables to partition or which columns to index more aggressively based on usage patterns, significantly improving query performance. When an LLM is added to the mix, it can seamlessly deliver faster insights on product purchasing trends without timing out or facing major slowdowns.

🤔 5. Natural Language Querying and Contextual Insights

Lastly, AI’s ability to support Natural Language Processing (NLP) has opened the door to new forms of data interactions, including “conversational” queries. Instead of requiring SQL or complex BI tools, business users and engineers alike can simply ask an AI model a question in plain English. The AI interprets the query, identifies relevant tables and joins, and pulls the right data from the warehouse. This will drastically reduce the burden on Data Engineers to write custom queries for every request, enabling more democratised data access across business.

Azure OpenAI integrated with a modern warehouse is one example. Imagine a financial services company that needs on-the-fly reporting on overdue invoices across multiple geography-specific payment platforms. Through Azure OpenAI’s NLP capabilities, a user could simply type: “Show me the outstanding invoices in Q4 by region and highlight the ones overdue by more than 30 days.” The system interprets this query, determines the relevant data from the warehouse, and displays an interactive chart.

The endgame here is a truly agile data warehouse that consistently produces high-fidelity data. Once built, seamless connectivity to LLMs means you can quickly convert raw information into narratives, insights, and real-time decision support. Through self-healing pipelines, AI-based data mapping, and real-time quality checks, we believe this year Data Engineers will spend less time wrangling code and more time innovating. From a business perspective, that translates into faster product launches, fewer errors in customer-facing systems, and the ability to flexibly pivot based on new market data.

#️⃣ 6. DSharp Studio's Game-Changing Approach to Data Modelling

DSharp, is redefining the landscape of data modelling and data warehouse automation with their flagship product, DSharp Studio. This tool is not just another data modelling solution; it's a comprehensive platform that's changing the way organisations approach implementing their data strategies.

Using the Power of Conceptual Modelling

Unlike traditional data modeling tools that focus on the nitty-gritty details of implementation, DSharp Studio allows users to work at a higher level of abstraction. This approach enables data professionals to focus on what truly matters - the relationships and structures within their data - without getting bogged down in the technical minutiae.

The beauty of this approach is its simplicity and efficiency. You can create sophisticated data models using familiar business terms and simplified Unified Modelling Language concepts. This not only speeds up the process but also makes it more accessible to non-technical stakeholders, fostering better collaboration across disparate teams.

Build a Data Vault without Coding

Once you've created your conceptual model, DSharp Studio takes care of the rest. It automatically generates the necessary code to implement your data warehouse using the Data Vault 2.0 methodology, including tables, views, and even orchestration procedures. This level of automation dramatically reduces development time and eliminates many of the error-prone manual tasks typically associated with data warehouse implementation using Data Vault.

Also included are capabilities like real-time class analysis, which identifies unfinished tasks and provides one-click actions to complete them - speeding up delivery and ensuring quality.

This approach enables you to rapidly prototype different models, and scale without wasting valuable time developing code that you throw away as the business need grows or pivots. This architecture ensures that your data pipelines can expand seamlessly as your data volumes and complexity increase.

The platform supports multiple target data systems, including Microsoft SQL Server, Azure SQL Database, Fabric, and PostgreSQL.

Tangible Proof Points

Metsähallitus, a Finnish state-owned enterprise, used DSharp Studio to consolidate data scattered across different sources. The result? A more efficient, data-driven organization with improved decision-making capabilities.

Another success story comes from the collaboration between DSharp and Scalefree. This partnership is bringing DSharp Studio's low-code data modelling solutions using the Data Vault 2.0 methodology to enterprises across Europe, helping them automate the creation of data warehouses and accelerate the development of scalable data systems with the region’s leaders in Enterprise Data Management.

Overall, as either a leader looking to drive more value from your data, or as an engineer wanting build data systems, DSharp Studio offers a unique blend of simplicity, power, and flexibility. Check out how DSharp is changing the game at the Data Innovators Exchange.

That’s a wrap for this week

Happy January Data Pro’s