Organizations are often faced with the issue of analyzing massive amounts of data. This task can quickly become a logistic nightmare if solving the business problem requires simultaneously using data stored in very different formats, such as video files, PDFs, and machine data. The purpose of ScryCollatio® is to alleviate this pressure by carefully collecting, organizing, refining and munging this data into one accessible virtual location. Some of the highlighted features of ScryCollatio® are given below.

Enterprises often have disparate data sets in variety of systems and at times there is no single system that is a master. The data definitions differ across different groups within organization and the data quality process is manual, time intensive and usually not comprehensive. ScrySSoT® Enterprise uses proprietary AI algorithms to automate data quality & business rules and to create consistent and high quality Intelligent Data Lakes. This solution also provides descriptive, predictive and prescriptive analytics to enable human (data driven) decisions.

Benefits of ScrySSoT® Enterprise

  • High accuracy and automated data quality using proprietary machine learning and natural language processing algorithms.
  • Unstructured data processing in addition to structured data sources provides comprehensive insights for predictive and prescriptive analytics.
  • Quick and easy configurations for internal & external compliance. This system automatically detects anomalies in data to prevent breaches with respect to internal and external regulations.
  • Pre-built Ontology and Live Manual for several financial services verticals.
  • Swift integration with wide range of data sources using pre-built connectors and scrapers library.
  • Reduced risk and improve compliance with granular role based access.
Enterprises often use error-prone, manual and time intensive processes to record and manage changes to reference data sets. This manual process is usually not comprehensive and leads to data definitions that are significantly different across multiple groups within an organization. Such errors in reference data impact downstream applications and associated business decisions. ScrySSoT® Reference Data uses proprietary AI-based algorithms to automate data quality checks and prepare consistent and high quality reference data for enterprise applications.

Benefits of ScrySSoT® Reference Data

  • High accuracy and automated data quality using proprietary machine learning and natural language processing algorithms
  • Swift integration with wide range of data sources using pre-built connectors and scrapers library
  • Integration with external sources like Bloomberg, D&B and Google to verify and fill missing or incomplete information such as names, address, organizational hierarchy etc.
  • Easy to search, review and cleanse reference data using Live Manual with advanced search features, exception management and automated cleansing
  • Reduced risk and better compliance with role based granular access
Enterprises have large amounts of data in variety of sources that can provide business insights; however the data needs to be processed and combined from multiple sources before it can be used for advanced analytics. Business Analysts spend large amount of time working with various technical groups to get the data in required form to identify and fulfill the analytical requirements for business units. ScryExplore® incorporates proprietary AI techniques to provide intelligent data exploration and preparation using hypergraphs that pre-compute similarities among documents and data sets and provide links and connections among structured & unstructured data sources & objects for large numbers of databases, tables and documents. ScryExplore® works in single processor as well as distributed and parallel computing environments (e.g., Hadoop, Spark).

Benefits of ScryExplore®

  • Improves efficiency in data exploration & preparation for Business Analysts by using automatically generated insights related to data links, connections and similarity as well as potential data quality exceptions.
  • Helps in generating comprehensive data sets by combining structured and unstructured data from wide range of sources.
  • Automated processing of digital documents generating summary, keywords, extracting structured data & its categorization.
  • Easily integrates scanned documents (Images, PDF) using ScryDigitize®.
  • Provides a simple GUI to perform complex data operations for preparing data for advanced analytics including the combining of data from various sources, identifying similarity, cleansing, harmonizing and extracting meta-data & computing mean, variance, range and unique values.


Most business problems require substantial amounts of data whose attributes have some qualitative aspects. An incomplete understanding of these aspects often leads to distorted data models, thereby producing erroneous results. Thus, our data scientists work with clients to combine domain expertise with database engineering to design a central coherent data model that is more accurate for solving the business problem. The ScryCollatio® platform provides a graphical user interface (GUI) and visualization functionalities for experts to view, improve quality, and transform raw data so as to achieve such a comprehensive data model.


Through the ScryCollatio® platform, the entire journey from disparate data sources to the comprehensive data model is automated and codified in a secure environment. The data elements are labeled so that any new, incoming data is automatically assigned appropriate security entitlements. This platform guarantees proper documentation of the corresponding data structures for various governance and audit needs and also ensures that any dataset can be viewed – or improved upon – by only those who are entitled to do so.


After the data has been processed by Scry-Collatio, it can be used for descriptive, predictive, and prescriptive statistical computations; machine learning; natural language processing; and information retrieval. These tasks can be either done manually or through ScryJidoka®. In some cases, users may need simple visualization, whereas in others, actionable insights may be desired; indeed, this refined data can be used in all such cases.


Our computational platform includes distributed and parallel processing and uses our proprietary algorithms as well as Open Source software. Our proprietary algorithms are designed to maximize in-memory computation so as to reduce computation time and the number of transfers within the hierarchical memory (i.e., among random access, solid state, and disk memories).


ScryCollatio® is designed to handle the logistic nightmare of managing data (that has varying levels of volume, variety, velocity and veracity) so as to make it ready for analysis. This platform supports almost all data formats including raw files; relational and non-relational databases; streaming audio, video and images data; machine data; social networking data; emails data; JSON data; XML data; healthcare data; and various forms of textual and presentations data. Using hypergraphs and related structures, ScryCollatio® ensures that users can move seamlessly among various data formats. In addition, more than 25 different web crawlers and scrapers constitute one of the libraries in ScryCollatio® that is used to scrape data from public and private clouds and from enterprise networks.


We import privacy and security related settings from where the data has been originally sourced (so as to maintain permissions that are already in place). Furthermore, we create “persistent data structures” to ensure that such entitlements are maintained in subsequent versions unless they are modified by the client. Finally, we have a strict role based access for all data that is imported into our platform so as to impose custom restrictions (in addition to those present), thereby making sure that this data is secure and obeys regulatory compliance.


Once data has been loaded and collated, ScryCollatio® displays potential problems and noise that may exist in this data. For this purpose, this platform uses in-built transformation algorithms and depicts different outputs such as pie-charts, bar-charts, two dimensional graphs, and graphs with nodes and edges. Such depictions usually describe the dataset mapping, data quality distribution, and relationships among various objects. After studying these outputs, users can modify the original data so as to reduce noise and potential problems that were present in this data.


Scry-Collatio provides multiuser collaboration that allows (a) simultaneous editing of a dataset that is consistent, (b) sharing previously created scripts and transformations, (c) creating new versions of the dataset and saving it in a “persistent manner” for all entitled users, and (d) transforming the dataset on an individual basis and then saving it for individual use. Hence, this platform minimizes the time required by multiple users in an organization to do the same data transformations.

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