Assetic. Cloud Platform Managing Big Data for Local Governments The Next Shift for Local Governments Across the Globe

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1 Assetic Cloud Platform Managing Big Data for Local Governments The Next Shift for Local Governments Across the Globe

2 Big Data is Getting Bigger

3 LGA Metrics > 80,000 Population: 60,000 asset records with attribute and condition info Documents and Photos = 5 each 10 MB per item 3 Terabytes < 20,000 Population: 20,000 asset records with attribute and condition info Documents and Photos = 5 each 10 MB per item 1 Terabyte Depending on IT backup policy of say 3m-4w-5d, this may require storage capacity of up to 36TB

4 What is Big Data? Myths Simplified Big data: Datasets where size and complexity is beyond typical database tools to collect, store, analyse data A moving target in terms of size and complexity technology advances, big data shifts. Depending on size and sector, the actual size may be multiple hundreds of terabytes, to multiples hundreds of petabytes

5 An Example of Big Data Infrastructure 1200km linear infrastructure Data at 10m intervals 11 discrete parameters Over 3 million images 3 terabytes per pickup Updated annually Over 5 years = 20TB

6 An Example of Big Data Infrastructure

7 An Example of Big Data Underground Infrastructure Tweed Shire Council Per 100m of pipe network: 25+ attributes Camera images = 500 plus 5000 MB size Multiple data points Now extrapolate over a typical 1000km = multiple Terrabytes

8 An Example of Big Data Facilities Housing and Public Works QLD: 3000 schools =1.2 million data items 250,000 spaces Annually updated 45 components Multiple hundred Terabytes with photos

9 Big Data Asset Genetics Maintenance strategies are changing: Shift from traditional programmed maintenance to Now real-time service driven budgets based on asset utilisation e.g. If utilisation of a pump increases, maintenance will be determined by operational performance (i.e. flow) rather than time. Assets become smarter: Capable of generating real-time performance data e.g. A Water Treatment Plant might contain over sensors, meters, etc. to capture real-time data with time intervals between 0.01 and 24hrs. This results in terabytes of data per year per plant. Historical data can be used to predict the behaviour of an asset in the future to determine which scenario adds the most value to the organisation (TotEx).This data is continually verified against the model to confirm model effectiveness

10 Using Real Time Data Asset Genetics

11 Evolution of Big Data - Infrastructure Gen 1 Gen 2 Gen 3 Gen 4 Basic Asset Register Basic Attribute Data Desktop condition and performance data Asset Register at high level Core Attribute Data including asset age Visual Condition Data say 2-3 criteria Detailed Componentised Asset Register Detailed Attribute Data Severity & Extent of Condition recorded as well as other performance measures Industry leading Asset Register Comprehensive Attribute Data Well understood and current performance data Gaps identified and completed with Industry Benchmarks. Allows for network level 25 year financial modelling. Provides direction in terms of overall funding needs. Allows holistic LoS analysis and what-ifs. Lower reliance on desktop assessments. Allows for renewal planning and performance projection at network level. 80% plus confidence level. Performance data mainly driven by visual assessments. Determine costs for delivering to establishing levels of services >75% validity. Not valid for project level candidate selection. All data utilised relates to the business. Allows for sophisticated modelling at the component level. Renewal planning for complex assets. Performance data based on visual and machine captured data. Detailed analysis of how to spend money on what projects. Perform granular what if? analyses. Mature Asset Management organisation. Contribute to industry best practice. Able to predict asset and component failure. Very reliable in all three levels i.e. financial modelling, project strategy and candidate capital works.

12 Infrastructure Genetics in a Catalogue Dynamic Data Smart Assets

13 Optimised Decision Making: The Science Dynamic big data with analytics and modelling. With Analytics Tools Genetic behaviour of assets can be modelled.

14 Optimised Decision Making: The Science

15 Renewal Frequency / Cost Optimised Decision Making Principles Using big data, asset genetic trends can be extracted for the asset classes based on: Condition Failure Rates / Renewal Intervals Replacement Costs Maintenance Strategy e.g. - Run To Fail - Fixed Time - Predictive Techniques Operating factors e.g. hours run etc Example: Electrical Switchgear Run To Failure Fixed Interval Maintenance Sample Point Predictive Maintenance Time

16 Failure Likelihood % Asset Condition Asset Life Cycle Curve Sample ODF Analysis Score 0 Asset condition distribution at Year 0 (FL: Failure Likelihood) Simulated Scenarios: - Partial Maintenance Strategy, Option 1 - Run To Fail Strategy, Option 2 Score 5 - Fixed Interval Strategy, Option 3 - Predictive Condition AgeStrategy, Option 4 64% Asset Failure Likelihood Curve 22% 10% 0% 3% 0% Age Score 0 1% FL Score 1 3% FL Score 2 10% FL Score 3 20% FL Score 4 25% FL Score 5 30% FL

17 Infrastructure Genetics Optimisation Model Alternatives with Big Data

18 Optimised Decision Making: The Process and Tools Using our world-leading prediction analytics and algorithms, Assetic is already consuming and applying big data to be at the forefront of Infrastructure Optimisation. Cloud-based platform now means big data becomes science through collaboration.

19 Optimised Decision Making: The Process and Tools

20 Assetic ODF SaaS Tool Suite

21 Questions?

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