Nishkarsh Products and Training

“It is a capital mistake to theorize before one has data” – Arthur Conan Doyle

The number one challenge faced by businesses today is not being able to discover and consume their own data in a timely manner. Organizations need on-demand capabilities to unlock and distribute their data. To provide access to data and make it available for consumption in a self-service manner, organizations are looking to build data assets catalogs and facilitate data lineage, context, and dictionaries around their data. Unlock your data with our Nishkarsh products and training for a richer data analytics and decision making experience.

Product: Nishkarsh EIM v1.1

The Nishkarsh Enterprise Information Management (EIM) tool offers a simple GUI driven interface to its customers which aids in classifying and managing their data assets and facilitates the building of enterprise data assets catalogs. The tool focuses on creating a standards-based hierarchical classification for your metadata, which then allows you to create a line of sight into your data assets. This lineage can subsequently be leveraged through our tool to facilitate self-service and easily select sections of your data that are needed for decision making. Additionally, the tool has features to import/export your current classification, mapping, and data assets and to generate impact reports. 


Create a customized hierarchical classification for your data. With the ability to organize your data into subject areas and link across them, you can establish a line of sight that helps you find the right data at the right time. 


Connect your data assets to your established classification to take full advantage of the line of sight you have prepared. 


The ability to get insights into your connected data assets means that you can create the dataset that you require to achieve your mission smoothly.

      National Information Exchange Model

Organize your data with mappings to established data models to simplify information exchange. Generate reports and information exchange package documents (IEPD) that help others understand your data.

Nishkarsh EIM v1.1 Features:

  • Classification and Mapping of Data
  • Seamless Connection to Data Assets
  • Self-service via Data Assets Catalog
  • Data Dictionary for Data Assets
  • Scalable Deployments
  • Flexible API for Metadata Integration
  • Permissions Management
  • Change Impact and Other Reporting
  • Search Data Assets
  • Visualization of Data Assets Catalog
  • Data Assets Collectors
  • Import/Export and Impact Reports

Nishkarsh EIM v1.1 Benefits:

  • Optimization of Data Environment
  • Visibility of Data Architecture
  • Self-service to Unlock Data
  • Facilitate Change using Impact Reports
  • Define Single Source of Truth
  • Data Warehousing
  • Data Privacy
  • Redefine Data Stewardship
  • Records Information Management
  • NIEM Standardization

Product: Nishkarsh NIEM PE v1.1

**Please contact us below for a free trial. 

The Nishkarsh National Information Exchange Model (NIEM) tool offers a simple GUI driven interface to its customers which aids in creating context to your data and helps distribute it to internal and external partners in a machine-readable format such as XML or JSON. The product offers a version of the NIEM dictionary built into the software that can be searched and NIEM elements mapped to the imported data assets to build information exchanges.


    NIEM Management:

The software provides the ability to search individual NIEM domains or search through all NIEM domains. It further provides the ability to add and remove elements for easy facilitation of information exchanges with new elements.

     Data Asset Management:

The ability to import data assets is provided via CSV files. It further provides the ability to view, edit, and map data assets.

    IEPD Life-cycle Management:

Our tool offers the ability to step through planning, analysis, building/loading of selection list, schema subset generation, editing and validation.

Nishkarsh NIEM PE v1.1 Features

  • Preloaded NIEM
  • Search/Edit NIEM
  • Import, Browse, Edit Data Assets
  • Map Data Assets to NIEM
  • Development Workspace for IEPDs
  • Full Lifecycle Support for IEPDs
  • NIEM Conformance Checks
  • Reporting
  • Built-in XML Editor

Nishkarsh NIEM PE v1.1 Benefits:

  • Standard Vocabulary for Data
  • Common Context for Data
  • Persistent Data Assets to NIEM Mapping
  • Machine Readable Data Format
  • Validate Information Exchanges
  • End-to-end Support for NIEM Processes
  • Platform Agnostic (Windows, Linux, MacOS)
  • Out-of-the-box Start
  • Low Cost Personal Edition


EeS National Information Exchange Model (NIEM) 101 Training – 1 Day

Problem: A lack of context and common vocabulary can make data exchange difficult between organizations.

Outcome: An understanding of the NIEM common vocabulary and why it makes data easy to understand and share.

Target Audience: Business users, information exchange developers, and data architects.


Module 1
NIEM Concepts
  • What is NIEM and what are its benefits?
  • How is NIEM structured?
  • Organization data assets management
  • Mapping data assets to NIEM
  • Use cases and IEPD/IEP
  • How to build IEPD/IEP
Module 2
NIEM Management
  • Reference schema
  • Core, domains
  • Namespaces, types, and properties
  • Extension schema
Module 3
IEPD Lifecycle
  • Scenario planning and requirements analysis
  • Element mapping and model search and selection
  • Want-list, schema subset building, and conformance targets
  • IEPD assembly
Module 4
Hands-on Exercises
  • Step-by-step IEPD development life-cycle
  • NIEM tools to support IEPD development


Enterprise Information Management 101 Training – 1 Day

Problem: Poor governance of data can result in data redundancies and confusion which translate to decision-making related inefficiencies.

Outcome: Authoritative data sources and an optimized data environment may provide better visibility and control of data.

Target Audience: Business and technical personnel

Module 1
Concepts of Enterprise Data Management and Benefits
  • Disadvantages of stovepipe data
  • Single source of truth, optimization of data environment
  • Data discovery, stewardship, and privacy
  • Metadata, master and reference data management
  • Data governance and its benefits and relationship to metadata management
Module 2
Development of Data Taxonomy, Collection of Data Assets, Mapping of Data Assets into Data Taxonomy and Self-service
  • Development of data taxonomies, collection of data assets, mapping of data assets into data taxonomies, and self-service
  • Concepts of data-centricity and the cross-linking of topics across subject areas
  • Collection of data assets
  • Mapping of data assets into an enterprise data taxonomy
  • Data assets catalogs
  • Unlock data for decision-making through self-service
Module 3
Modeling Concepts
  • Metamodeling best practices and EA models
  • Data paradigm and its touchpoints to business and technology layers
Module 4
Hands-on Exercises
  • Creating classifications
  • Ingesting data assets
  • Mapping data assets and generating assets catalogs
  • Selecting data elements and creating datasets

Big Data and Data Lakes 101 Training – 1 Day

Problem: The lack of a central place to host data can cause data redundancies and data consistency issues.

Outcome: Build data lakes to integrate your enterprise solution with a holistic view of your data.

Target Audience: Data architects, modelers, analysts, and developers


Module 1
Big Data Concept, Tools, and Technologies
  • What is big data? The storage and processing challenge
  • HDFS and map reduce
  • Data science and new insights
  • AI: the road ahead
  • Tools and technologies
Module 2
Data Lakes, ELT, and Design Considerations
  • What are data lakes? Why data lakes?
  • ETL vs ELT, data governance and metadata management
  • Tools for ingestion, access and analysis of data
Module 3
Hands-on Exercises
  • Using tools to ingest data into Hadoop data lakes
  • Setting up access to Hadoop data
  • Using tools to analyze data in Hadoop
Module 4
Hands-on Exercises
  • Creating classifications
  • Ingesting data assets
  • Mapping data assets and generating assets catalogs
  • Selecting data elements and creating datasets



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