Inbar Goldstein , Product Manager
July 02, 2023

From Raw Data to Informed Decisions:
How Data Fusion Empowers Decision Intelligence

Data fusion systems are essential for law enforcement and intelligence agencies who wish to effectively utilize vast amounts of data from diverse sources, leading to improved decision-making, crime prevention, and enhanced national security. These systems empower users to manage how data is accessed, combined, analyzed, and reported by transforming and standardizing data into a unified format before loading it into a central repository. The fusion process exposes connections between suspects in a records management system to entities within disparate databases, such as call data records, social media posts, and financial transactions. By aligning the content, the objective is to ensure consistent and reliable data loading.  

Although some data representation standards exist, they often do not address the actual content or raw data values because the data quality can vary significantly from source to source. Data are prone to all sorts of misrepresentations. Simple mistakes such as typos, spelling errors, transpositions, transliterations, or abbreviations (qualifiers, prefixes, and suffixes) account for many inconsistencies; for example, 123 Elm Street is different from 123 Elm St., and this difference will affect the outcome. The quality, consistency, accuracy, and precision of data impact the reliability of analytical results.  

To help mitigate these issues, most platforms incorporate a combination of ETL (extract-transform-load), federated search, entity resolution, and value-added/metadata calculations to produce actionable results. Each approach enhances the analytics and should be used to improve the outcomes. 

  • ETL automates the data ingestion, cleansing, enrichment, and correlation of structured, semi-structured, and unstructured data, including media sources. ETL helps standardize and improve the content of the data before it is ingested to increase its analytical efficiency. This helps align the structures, ensure values are all in the same format (dates, numbers), remove extraneous characters (commas, dashes, quotes, etc.), capitalize text, trim values (Zip+4), and replace content (Street->St). Some platforms even implement ELT and transform the data after load. 
  • Federated search supplements data from other remote systems without the need to ingest and manage the entire database by only pulling back the specific information for a designated value or entity. To retrieve the desired information, a federated search is submitted through an API call to a specified source (e.g., public records, subscription services, external sources), which can quickly provide insight to answer a targeted question.  
  • Entity resolution helps consolidate the different values and representations of specific types of data (e.g., person, address, phone, etc.) into one authoritative entity that incorporates all its variations. This simplifies the analytics that can be applied against the data since it is standardized and consolidated into a well-defined and unique format, making integrating and representing multiple data sources more accurate and applicable. 
  • Value-added and metadata calculations are specific transformations an analytical platform runs to extract embedded content. This includes machine learning models that categorize content from an image, classify suspicious behaviors, or sequence patterns-of-life. Custom algorithms and heuristics include LUHN formulas, DEA number validation checks, and address standardization. These transformations are often designed (containerized) for a specific purpose and are embeddable into a fusion system. 

All these transformations are significant because everything is interrelated. A missing period, an abbreviation, an extra letter, a dash or parenthesis, or any other disparity could be the difference between finding a target or missing an opportunity with potentially devastating results. To ensure high-quality analytics, a well-balanced system incorporates various methods to enhance the value of data. The system improves its effectiveness by establishing a strong foundation characterized by quality, accuracy, and completeness.  

Are you curious about the evolving debate between ETL and ELT approaches in data integration? Stay tuned for our upcoming blog article, Better Data, Better Analytics: Transforming Data Through ETL & ELT for Consistent Outcomes, as we delve into the advantages and disadvantages of each method, uncovering which one holds the key to optimizing your data workflows and improving your decision intelligence. Don’t miss out on understanding the ETL vs. ELT argument and how it impacts your data strategy! 

Visit NEXYTE.AI to learn more about NEXYTE, the data fusion and machine learning platform revolutionizing decision intelligence. 

Let's Empower Decision Intelligence

Inbar Goldstein , Product Manager

Inbar is a Product Manager for NEXYTE, Cognyte’s decision intelligence platform. She has over two decades of experience in the Defense and Intelligence sectors. Inbar holds a BA in Computer Science from the Academic College of Tel-Aviv and is currently pursuing a Master’s degree in Cyber, Politics, and Government at Tel-Aviv University .
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