Data Discovery Proof of Concept: 4-Wk Proof of Value

Information First

Optimize and de-risk your Azure footprint or migration via an AI-powered Data Discovery Assessment which will identify sensitive data and ROT (redundant, obsolete, trivial data).

The Data Discovery Proof of Value (PoV) is intended for any organization contemplating a migration to Azure, or that has already migrated to Azure and needs to eliminate redundant, obsolete and trivial data, and/or identify and protect sensitive data such as PII or PHI. In addition to optimizing performance within the Azure environment, this PoV can also identify potential non-compliance with data / records retention policies. Common customer pain points addressed in the PoV include the following:

  • Lack of governance over exponentially increasing unstructured data
  • Lack of visibility of data across disparate silos and repositories
  • Lack of protection over privacy or other sensitive data
  • Unnecessary computing infrastructure needed to manage data with little or no value (E.g., ROT – Redundant, Obsolete or Trivial)
  • Non-compliance with data/records retention policies

The PoV will follow a standard methodology and focus on Discovery and Insight. Deliverables include the following:

  1. A Data Discovery and Protection Program Roadmap

    • Executive Summary
    • Discovery and Insight Analysis
    • Recommendations
  2. Discovery & Insight 360 Report

    • Scanning Metrics
    • Visualization Dashboards
    • Risk, ROT, and Sensitive Data Findings
    • Information Governance Non-compliance Results

The POV will focus on file shares initially, up to 1 Terabyte, with the option to extend the engagement to include Exchange, SharePoint/365, and OneDrive, as well as higher data volumes.

Prerequisites include the following:

  • Client to provide an on-premise Server to run the File Share scanning agent.
  • Client to provide a laptop, account, and/or controlled access in order to perform and monitor scanning and resulting analytics.
  • This project will be performed in a single environment (e.g., production, development, or training) for analysis of files/data.