Analysis Prototyping in Investigative Intelligence

The Holy Grail of Analysis

The holy grail of modern data analysis is a highly agile analytical capability—one that can operate across an organization’s constantly evolving data while fully complying with IT requirements. Organizations in finance, law enforcement, fraud detection, and national security rely on timely, accurate insights to mitigate risks and drive strategic actions. However, despite having access to vast amounts of data, most lack the agility to effectively address constantly evolving needs, such as performing hypothesis testing in enterprise business analytics. Typically, prototyping represents only a fraction of what is truly needed, is reserved for technical experts who rely on coding, and is difficult to convert into a ready-to-use analytical product—adding significant time to the process.

Modern data analysis platforms can fuse vast amounts of data and enable large-scale analysis, but they come with critical limitations. Adding or modifying data sources is cumbersome, and enabling prototyping without breaking IT compliance rules is often impossible. Traditional enterprise solutions force organizations to choose between innovation and governance, when in reality, they need both.

DataWalk changes the game. Its unique technology enables a breakthrough platform that unifies all an organization's data into a highly flexible structure. Users can easily add or modify data sources, conduct rapid, code-free analysis, and take advantage of agile prototyping—all while maintaining full compliance with enterprise IT requirements.

With DataWalk, organizations no longer have to sacrifice flexibility for control—they can finally have both.

Figure 1 - Modern data analysis: Enterprise Analytical Prototyping - the concept that combines desktop agility with enterprise compliance.

This document explores DataWalk’s unique approach to Enterprise Analytical Prototyping, which eliminates this trade-off. By bridging the gap between enterprise-wide intelligence and investigative agility, DataWalk enables organizations to innovate, uncover actionable insights, and make data-driven decisions faster than ever. This capability is particularly vital in areas where the stakes are highest—financial crime, fraud prevention, and national security—where every decision can impact lives, health, security, and wealth, and where fast analysis prototyping is crucial for quick results.

The Challenge: Achieving extreme agility with enterprise IT compliance in analysis prototyping

1. Data Silos: The First Barrier to Holistic Insights

Data fragmentation remains one of the biggest obstacles to achieving enterprise agility. For example, a global insurer faced a new type of insurance fraud scheme, where fraudulent claims were being made on a large scale, causing daily losses of hundreds of thousands of dollars. Investigators needed to verify their hypotheses and identify perpetrators, which required integrating existing data sources with new ones—such as logs from a mobile application that had never been utilized before. Existing systems were rigid and the insurer’s data was spread across various systems that could take many months to combine. 

2. IT Bottlenecks: Stifling Innovation

For good reason, enterprises often impose rigid IT processes and compliance rules. However, this makes data-related requests—from generating reports and performing more sophisticated queries to testing new analytical models or functions —dependent on IT teams, extensive coding or engineering, and lengthy enterprise processes. For example, a leading global financial institution responding to a regulatory inquiry regarding the Pandora Papers faced similar constraints. Analysts needed to assess whether individuals and entities listed in the leaked documents were linked to their clients. Existing entity resolution tools lacked the agility required for such an urgent task, forcing analysts to depend on IT for every query. The estimated time required to process the data was six months, a timeline incompatible with regulatory expectations. 

3. Rigid Enterprise Tools: The Constraint on Exploration

Most enterprise analytics tools are designed for structured, predefined workflows, which lack the flexibility required for ad-hoc analyses and investigations. For example, a national intelligence agency developing a situational awareness system struggled with OSINT (open-source intelligence) data. Their existing tools couldn’t adapt to rapidly changing geopolitical risks, such as protests, government instability, and security threats. Analysts needed the ability to integrate new data sources and test emerging analytical techniques like text scoring, Natural Language Processing (NLP) or Jaccard index on demand, making enterprise software prototyping essential to test these approaches quickly.

4. The Speed vs. Accuracy Dilemma: A Troublesome Trade-off

Organizations often must choose between quick, superficial analysis and time-consuming, precise analytics. This trade-off is no longer viable. A top U.S. bank using AI-driven fraud detection faced a stark reality: Traditional fraud detection models were too slow to adapt to new schemes. By the time fraud was detected, millions of dollars had already been lost. The bank needed a system that could prototype fraud detection rules in minutes, not weeks.  

The Solution: Enterprise Analytical Prototyping—Unlocking Agility at Scale

DataWalk Enterprise Analytical Prototyping is a unique, transformative approach that enables organizations to fuse data from large disparate sources, resolve entities, rapidly test hypotheses, integrate new data sources, and uncover hidden patterns and connections—without the lengthy development cycles of traditional analytics. This method provides the agility of desktop-like analysis while meeting enterprise requirements for governance, security, and scalability, and enabling fast analysis prototyping.

Key Capabilities and Benefits of Enterprise Analytical Prototyping:

• Rapid Data Fusion and Exploration – A unified knowledge graph enables seamless linking of disparate data sources, regardless of format, location or quality. When data is connected, matching records can be efficiently identified (i.e., entity resolution), the system enables traversal operations, complex business queries, and easy data extensions without extensive coding or engineering, ensuring alignment between enterprise architecture, business, and IT. The system accepts imperfect data, allowing analysts to prototype without constraints.

• Agile Hypothesis Testing and Development of New Features—DataWalk provides a comprehensive set of configurable no-code analytical tools, such as knowledge graphs, graph analytics, AI/ML, search, visual queries, and pivot operations (OLAP). Moreover, the system provides the framework for accelerating the testing of new functions. These tools empower both business and technical users to rapidly test ideas, validate assumptions, and develop analytical models, significantly reducing time and effort while fostering a dynamic enterprise architecture.