Shivaan Asset Management

Data Standardisation and AI Readiness Framework

Standardised asset data, trusted decisions, AI-ready foundations.

A maintenance or investment decision is only as good as the records it is read from. Standardising asset data is the work that makes those records dependable, at every site and in the same terms.

This framework is how Shivaan Asset Management does that work. Five phases and nine steps take an asset base from records kept a different way at every site to standardised data that reporting, optimisation and AI can stand on. It is written to be learned and applied by anyone, in any heavy asset industry.

Diagram of the Data Standardisation and AI Readiness Framework: the five phases set out left to right as cards on a dark navy background, each card carrying its phase name, its one-line descriptor and its numbered steps, all of which are listed as text below.
The Data Standardisation and AI Readiness Framework: five phases and nine numbered steps, from understanding the asset portfolio through to reporting, optimisation and AI readiness.

The five phases

The phases run in sequence. Each layer makes the next one possible, so nothing downstream is built on data that has not been agreed.

  • Discover

    Understand what exists and how to classify it.

    1. 01Understand Asset Portfolio
    2. 02Classify Asset Types

  • Structure

    Break assets down and agree the terminology.

    1. 03Build Equipment Hierarchies
    2. 04Standardise Items and Components

  • Standardise

    Turn knowledge into reusable libraries and CMMS-ready codes.

    1. 05Create Reusable Knowledge Library
    2. 06Generate CMMS-ready Failure Codes

  • Govern

    Keep the data trusted, owned and improving.

    1. 07Improve Data Feedback Loop
    2. 08Steward Ownership and Governance

  • Activate

    Use standardised data for reporting, optimisation and AI readiness.

    1. 09Activate Digital and AI Readiness

What this gives you

Three engagements built on this sequence. Every figure below is the figure already published on that project page.

  • 78%

    Failure-code adoption within 8 months, from no prior use of codes

    A global mining operation standardised 100+ asset classes and 150+ asset class type variations, then crews began coding failures with the standardised codes and left less of the history in free text.

    Read the global standardisation case study

  • 5.0 / 5.0

    Every one of 11 independent client assessment dimensions

    A Tier 1 oil and gas operator standardised 5 asset classes and 44 asset class types, ending with 100% standardisation across all deliverables and a Net Promoter Score of 10 out of 10.

    Read the SAP failure codes case study

  • 2 sites

    Coal handling and preparation plant functional locations standardised

    An audit across two coal mine plants produced one governed enterprise master data standard covering how functional locations are created, owned and audited, positioning the client's asset management system for ISO 55001 alignment.

    Read the functional location audit case study

The foundations, explained

The building blocks the phases rely on, written up in full in our resources.

Start with the data you already have

Tell us where your asset data stands today and we will show you which phase to start in.

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