What goes on behind that Visualisation Its more than meets the eye - - PowerPoint PPT Presentation

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Sep 26, 2022 238 likes •459 views

What goes on behind that Visualisation Its more than meets the eye So what does this mean? The most eye catching or ple leasing visualisation ..gets the most attention How do we bala lance this How can we trust what we see What happens behind

SLIDE 1

What goes on behind that Visualisation

Its more than meets the eye

SLIDE 2

So what does this mean?

SLIDE 3

The most eye catching or ple

leasing visualisation

…..gets the most attention

SLIDE 4

How do we bala

lance this

…..behind the vis

isualis isation

How can we trust what we see What happens behind the scenes

SLIDE 5

Building from the ground up

Getting th the rig right data a in in

Ingestion
Migration

Mak aking su sure th the rig right data a is is avai ailable

Monitoring
Treatment
Reconciliation

Getting th the rig right data a ou

ut
Transformation
Optimisation for the Target and Objective

SLIDE 6

Getting the right data in

SAS Ingestion Process
Receive Data from State and Commonwealth entities
Data Matching between entities and source system
Golden Record / Record Survivorship
Data Management Schemes
Restful services, JSON, ODATA and SAP

SLIDE 7

Getting the right data in - STCL

State 1 Data State 2 Data State 3 Data INCREMENTAL SAP LOAD PROCESS, with STATE-BASED GOLDEN RECORDS SAP SAP DAILY DATA EXTRACT Daily Extract – Match & ID data only REGULAR FEEDS CLUSTER SAP EXTRACT, NEW NATIONAL GOLDEN RECORDS, PREVIOUSLY ESTABLISHED GOLDEN RECORDS Record has SAP ID? No Create New NDIS ID, Add Golden Record Execute SAP Upload New records accepted END Yes MASTERSOFT MASTER GOLDEN RECORDS Match & ID Fields

Match & ID fields only APPLY DQ AND CLEANSING RULES, CREATE MATCH FIELDS APPLY DQ AND CLEANSING RULES, PRE-CLUSTERING by STATE All data Incl. Match fields NDIA Agencies Prepare data for SAP Upload (FUTURE!!) OTHER SOURCES TBD Web Service Call, or Other Access TBD SAP UPLOAD AUDIT Pass, Fail, Timeout Plus more States, and Commonwealth, as applicable STATE 1 GOLDEN RECORDS STATE 2 GOLDEN RECORDS STATE 3 GOLDEN RECORDS CLUSTER GOLDEN RECORDS FROM ALL STATES INTO SINGLE MASTER All data Incl. Match fields CRITICAL DATA CHECK, e.g. DOD Status? Timeout = Retry Assess and Remediate Data Fail Resubmit for Processing Excel Spreadsheets OTHER SOURCES TBD Web Service Call, or Other Access TBD Resubmitted Data MASTERSOFT NATIONAL GOLDEN RECORDS All Client Data CLUSTER TO CREATE GOLDEN RECORDS FOR EACH STATE Clustering Rules TBD Clustering Rules TBD – Pre-Cluster by STATE Such as Centrelink Separate Master File for each state Clustering Rules TBD From failed SAP uploads

SLIDE 8

Ingestion - Rest

SLIDE 9

Ingestion - Methods

Methods

GET
POST
PUT
MERGE

Data Cleansing

Address ( REST web services)

SLIDE 10

Ingestion – Sample JSON

SLIDE 11

Ingestion - Results

SLIDE 12

Ingestion - Generated Sample

SLIDE 13

The right data is available

Automated Checks
Treatment of problem data areas, default values, manual

adjustment

Reconciliation

SLIDE 14

The right data is available - Checks

SLIDE 15

The right data is available - Change

SLIDE 16

Getting the right data out

Analysis vs Analytics
Optimisation for target and objectives
Role types
Role Metadata

SLIDE 17

Getting the right data out

SLIDE 18

Getting the right data out

SLIDE 19

Getting the right data out - Integrate

SLIDE 20

Visualise

Cas ase 1: : Build ild Cal all Cen entre dash ashboar ard Features – auto reload/refresh every 5 mins that reflects Call Centre stats. i.e. Number of calls, severity etc Cas ase 2: : Regional Med edicare serv service ar area eas visu visualisation Features – maps with shapes and regional outlines (integration with ESRI). E.g. With Dashboard that displayed summarised data such as total Medicare services rendered etc.