## Agentic Data Engineering

# A fully equipped data engineer at your disposal 24/7.

##### Problem

## Organizations Lack Sufficient Data Engineers for Effective Analysis

#### 80% of time is spent on menial tasks

Data scientists spend 80% of their working hours on menial tasks like data cleaning and transformation.

#### Only 10% of data is accessible

Enterprise has only accessed 10% of its massive amount of data, leaving business insights unrealized.

#### 6+ week wait time

Data engineering teams take an average of 6+ weeks to prepare data for analysis.

##### Solution

## Make data preparation accessible to all of enterprise with Howso’s Agentic Data Engineering.

#### No-Code

Perform complex data preparation through intuitive interfaces and simple natural language queries.

#### No Models

Howso built the industry’s only explainable, performant alternative to neural networks. We eliminate bias, hallucinations, and drift, so you can get results you can understand and trust.

#### Complete Data Democratization

Why wait months for decision intelligence? Now, managers, marketers, analysts, and operators have the tools they need to prepare the data they need in hours.

##### Core Capabilities

## The only AI Agent with explainability and attribution at every turn.

#### Intelligent Feature Engineering

Derive insight directly from your data and uncover predictability of features, feature importance, anomalies, and marginal statistics right out of the box.

#### Generate & Validate Synthetic Data

Generate synthetic data using the industry’s only solution that doesn’t use neural networks. Audit each data point for privacy and utility.

#### Embed Hidden Watermarks

Establish origin, authenticity, and demonstrate that data has been securely handled. The mark remains intact even if the data is modified, providing a persistent security layer.

## Explore Agentic Data Engineering Solutions

Ready to enhance your data engineering capabilities? Discover how our agentic solutions can help you build trust in data/AI and maximize ROI.

##### Benefits

## The Business Value of Agentic Data Engineering

### 5x

the # of employees making data-driven decisions.

### 93%

reduction in time to gather decision intelligence.

### 22%

improved decision accuracy.

### 100%

visibility into Generative AI decision making.

##### Use Cases

## Marketing

A marketing team automates the integration and preparation of real-time customer data from multiple sources, enabling them to launch personalized campaigns within hours instead of days, boosting campaign effectiveness by **20-35%**.

## Banking

A banking team streamlines real-time data pipelines to monitor employment and regional economic indicators, enabling proactive loan restructuring programs that reduce defaults by an additional **10%** and improve customer retention by **25%**.

## Supply Chain

Factory managers build dynamic, scalable data pipelines for supply chain simulations, reducing the time required for impact analysis by **50%** and enabling cost savings of up to **30%** through more agile logistical adjustments.

## Understanding the Challenges of Data Preparation

Data preparation is often fraught with challenges that can hinder effective analysis. Organizations frequently struggle with data silos, inconsistent data formats, and inadequate access to skilled data engineers, leading to delays in obtaining actionable insights.

## The Role of Automation in Data Engineering

Automation plays a pivotal role in modern data engineering by reducing the burden of manual data preparation tasks. Howso's Agentic Data Engineering solutions automate routine processes like data cleaning, transformation, and feature engineering.

## Best Practices for Successful Data Integration

Effective data integration is crucial for realizing the full potential of enterprise data. Best practices include establishing clear data governance policies, ensuring data quality at every stage, and utilizing advanced integration tools to manage disparate data sources seamlessly.

## Future Trends in Data Engineering

The field of data engineering is evolving rapidly, with emerging trends that promise to shape its future. Innovations such as augmented analytics, where machine learning algorithms assist in data analysis, are transforming how businesses approach data challenges.