CONTACT US

deloitte_logo
PROCESS AREA
Autonomous Data Operations
MICROSOLUTIONS
Autonomous Data Management (Vendor, Material)

Autonomous Data Management (Vendor, Material)

Autonomous Data Management – Is your Vendor and Material data managing costs, or a cost driver?

Poor reference data, manual data population, and non-standardized data governance can lead to an inconsistent record of project costs and redundant data entry, driving missed vendor negotiating opportunities.

Leverage AI-optimized workflow to automate, validate, and correct material data management data across the enterprise. This is AIOPS.D Autonomous Data Management.

The AIOPS.D Advantage
Standard Practice

Validate Data Entry

Common Challenges:
  1. Users manage manual data handling for every operation, including re-entry of baseline attribute data
  2. Inability to integrate data quality measures via trusted 3rd party data sources; inability to leverage historic data to improve data accuracy

Complete and Coordinate Data Handling

Common Challenges:
  1. Multiple components within ERP data objectives are handled and reviewed manually, driving inaccuracy and increased time to identify and resolve data quality issues
  2. Lack of centralized system to identify and correct data issues prompts further data inconsistencies

Monitor Data Quality, Provide and Manage Corrective Actions

Common Challenges:
  1. Complex multi-screen navigation path for simple data task, followed by high number of clicks to complete a unique task. Users have single desktop channel for data engagement
  2. Lack of workflow drives limited tracking and follow-up transparency, driving lengthy data activity completion times
  3. Multi-team coordination for data resolution is manual, driving complexity, inconsistency, and delays in resolution

 

 

  1. Automation will pre-fill and/or recommend values for different attributes, with recognition of unique business processes
  2. Automations built on new, historic, and third party data sources (e.g. D&B, Equifax) improve predictive performance
  1. ML-based automation of data monitoring identifies inaccuracies and other data quality issues
  2. Centralized console enables users to review issues and recommendations for corrective actions
  1. User experience-based design process and touchless operations simplify navigation and task-completion paths. Mobile-device adaptive screens expand user modes of interaction
  2. Conversational AI, automated workflow and notification prompts drive completion of requests and up-to date tracking
  3. Touchless operations with autonomous decision-making reduces the count of complex coordination resolutions

OVERVIEW
DESCRIPTION
The Autonomous Data Management microsolution automates, validates, and corrects material data management data across the enterprise through accelerated data handling, ERP data monitoring, and data healing recommendations. The solution leverages AI-optimized workflows, ML-based automation, and autonomous decision-making to improve touchless operations, data self-generation, and predictive performance with an aim to standardize data governance and simplify data management.
STORIES FROM THE AI VAULT

Real-Time Supplier Data

Are you demanding enough from your AI data management strategy, and can ML solve data gaps?

SEE THE STORY

CAPABILITIES

Centralized Console for Data Management
Data Healing Recommendations
Data Self-Generation
ERP Data Monitoring

MICROSERVICES
Autonomous Data Management (Vendor, Material)

Slide to see more.

DESCRIPTION
The Autonomous Data Management microsolution automates, validates, and corrects material data management data across the enterprise through accelerated data handling, ERP data monitoring, and data healing recommendations. The solution leverages AI-optimized workflows, ML-based automation, and autonomous decision-making to improve touchless operations, data self-generation, and predictive performance with an aim to standardize data governance and simplify data management.
STORIES FROM THE AI VAULT

Real-Time Supplier Data

Are you demanding enough from your AI data management strategy, and can ML solve data gaps?

SEE THE STORY

PROCESS AREA
CAPABILITIES

Centralized Console for Data Management
Data Healing Recommendations
Data Self-Generation
ERP Data Monitoring

Scroll to Top