Focus Area 1: Future State Data Architecture & Engineering

RDs data landscape includes Structured Data SemiStructured Data Unstructured Data and Geospatial Data. All of these data types must be considered in any efforts taken under this Focus Area.

Required Consultant tasks in this area may include requirements such as:

Design of FEAFconsistent conceptual and logical data models for industryfocused business segments such as loan origination loan servicing or loan accounting; analysis of a current state risks and mitigation recommendations;

Analysis and solution architecture recommendations for RDs future state data management environment RD Enterprise Data Complex (REDC) based on RDs future state data architecture vision;

Developing and implementing strategies for implementation of REDC associated governance and standard nonfunctional requirements for inclusion in various solicitations;

Design and implementation of Master Data Management (MDM) governance;

Analysis and solution architecture recommendations for a MDM platform;

Analysis and solution architecture recommendations for EDW Platform Data Complex;

Analysis and solution architecture recommendations for Enterprise Reference Data Management Platform

Develop architecture and framework for structured semistructured and unstructured data data capture data inclusion augmentation of data sets that provide enhanced business insights and provide associated recommendations; and

Address any previous findings regarding inconsistent data handling practices duplications errors and areas that lack technology support.

Develop enterprise data mapping that provides transparency for data lifecycle within RDs ecosystem and can be utilized in data triage modernization and data analytics initiatives.

This entails developing highlevel depictions of the endtoend data lifecycle at the RDenterprise level enabling a visualization of both internal and external data interactions.

Develop a data asset inventory and catalog that allows RD subagencies to understand data dependencies for their mission and automate data location searches either by people or by data processes. This entails developing a live inventory of key data assets through various

sources capturing it in an agreed format and populating a data catalog with relevant metadata.

Design and solution implementation for various data outputs for consumption by RD users other USDA Mission Areas and/or other US Government Agencies. These outputs can take various forms such as formatted electronic reports XML data sets Comma Separated

data sets or other Government to Government defined structures.

Focus Area 2: Future State Data Analytics and Visualization Development

Data analytics support focuses on facilitating enhancing or enabling data users to draw insights

from data by providing them with both facts and tools and/or drawing the insights for them. RDs

data landscape includes Structured Data SemiStructured Data Unstructured Data and Geospatial

Data. All of these data types must be considered in any efforts taken under this Focus Area.

Required Consultant tasks in this area may include requirements such as:

Discovery and communication of meaningful patterns and trends;

Recommend Design and/or Building simpletouse models in which customers may select

data layers to visualize on a base map in various combinations; and

Application of data science to select and combine appropriate datasets from a variety of

sources to create new insights (e.g. use of crop yield data to predict mortgages that may

become subject to risks).

Focus Area 3: Semistructured Data and Unstructured Data Analysis Design and Solution

Implementation

Required Consultant tasks in this area may include requirements such as:

Providing analytic design and implementation support services to develop solutions for

the management of unstructured and semistructured information assets based on REDC.

Providing analytic design solution recommendations and implementation for digitizing

the entire document management lifecycle as RD systems and applications are modernized.

At a high level this includes:

o Capture identifying inscope documents that are born digital and placing them

under formal management. May include the use of classification schemas

(taxonomy/ontology) and metadata;

o Manage creating and applying business rules that make up enterprise standards.

May include versioning data for curation purposes and access controls;

o Access enabling internal and external users to continuously and reliably retrieve

managed documents within access guidelines via a federated search capability and

a browse feature;

o Publish/Dissemination ensuring the ability of document owners to proactively

distribute selected documents through digital or physical means;

o Archive/Preservation ensuring appropriate longterm preservation of digital

documents in compliance with RD retention policies and NARA guidelines.

Support integration and digitization of documents for Enterprise Content Management

(ECM)