Make Sure to Control Small Data First! Want to be Ready for Big Data? SynerTrade Rainer Machek Executive VP sig.org/summit
Make Sure to Control Small Data First!
Want to be Ready for Big
Data?
SynerTradeRainer MachekExecutive VP
sig.org/summit
Want to be ready for Big Data?Make sure to control your Small Data first!
Rainer Machek, Executive VP
2
Glisser Question
Customer situation: We were buying the same things under different codes. And we were paying different prices for the same product, even from the same supplier.“
Question: Does this sound familiar to you?
Audience rank possible answers via Glisser:
• Even worse
• Yes at all
• Not that bad, but its there
• Not at all
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• Most organization’s struggle in data quality
• You do not have to be Fuzzy! There are solutions based on your rules to achieve reliable accurate data
• Big Data is upcoming! Be prepared and clean Small Data first
You will learn:
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Accelerate: Digital Solutions, used by 500+ customers like:
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Reference: Fresenius Medical Care Project
SAP, SAP BW
Sage, Axapta, Infor
4 Regions (EUROPE, APAC, LATAM, NAFTA)
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Source: internal Fresenius Medical Care Newspaper
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All around the world, different taxonomies
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Reference: Lufthansa Group Project
17 SAP, 2 SAP BW
1 Oracle Database
129 subsidiaries out of around 400
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2,800,000 of invoices, 17 SAP Systems
Source: internal Lufthansa Newspaper
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Extraction, Processing and Upload of data
1Data Harmonization
2 Analysis and Reporting
3
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Three Steps to a Sustainable Spend Transparency
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Enriched Data
MG AssignmentSupplier harmonizationD&B InformationCustom Mapping
Master Data
SupplierGL AccountCost CenterMaterial Group
Transaction Data
Purchase OrderInvoiceGoods Receipt
- DUNS Numbers- Mapping to ERP Number- MG Assignment of
transactions / spend
- Supplier Name- Supplier Country- GL Account / CC
Description- …
- Plant- Material Group- Purchasing Org- GL Account- Cost Center
- Incorrect DUNS format- DUNS number
discrepancy- MG / GL Account /
Supplier mismatch
- Duplicate VAT IDs- ZIP Code format incorrect- ZIP Code does not match
address- Non standard country
code
- Savings > Order Amount- Contract Number invalid- Incorrect Currency
Conversion- …
Data Completeness
Data Correctness /
Plausibility
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Data Quality Matrix of Different Quality Dimensions
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Data Cube - Data Quality
Data Quality Management: The most fundamental part of Data Harmonization
• All data are in the data cube all data deficit & inconsistencies are in the data cube as well
• Visibility and transparency about deficits by means of SynerTrade Quality Reports
• The SynerTrade Quality Reports are a special and fundamental view on the data
From whom are we buying?
For whom are we buying?
What arewe buying?
Co
mm
od
ity
TRANSACTIONS
Vendors
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Quality Reports: Analysis of Missing Data
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Supplier Harmonization
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SynerTrade Supplier Harmonization: Background
• Typical problem: Existence of supplier duplicates in various source systems
• Identification of duplicates as part of the supplier data integration
• Semi-automatic iterative procedure whose logic is based on automatic and
manual processes
Example Inc.
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Matching of 100.000 suppliers in ~15 min
Up to 5 Terabyte of Data for Spend Analysis
Performance
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Supplier Harmonization: Overview
Spend Analysis
Match Run
Match Rules
Supplier Master Data
ProposedMatches
Automatic Matches
External Data Sources(Optional)
MatchExclusions
ConfirmedMatches
Manual Input
2
3
1
4
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Match Types (like sieving sand......)
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Reworking The Consolidation Status – self learning
Visualization of harmonization results:
• overview of the most important KPI and statistics
• automatic matches, match proposals, summary, etc.
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Commodity Harmonization
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Rules & Standards Based Process
Commodity Harmonization is based on a intelligent Rules & Standard algorithm:
• The mapping algorithm is unambiguously and reproducible “fuzzy” logic is not
controllable with millions of transaction data
• The development of the mapped spend per commodity must be understandable and logic
during the complete process
• Processing of all rules in fixed sequence which is defined at the beginning of the project
• Processing Duration for 5 million transactions and a Rules & Standard with 15.000 entries: 7
minutes!
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Commodity Harmonization – self-learning
Finally every single record will be assigned to the correct commodity according the Rules & Standard algorithm…
After the definition and creation of a common, unambiguously structure of corporate commodities every single record will be mapped and assigned to a dedicated commodity. Due to the iterative SynerTrade process the rules & standard will be refined and adjusted permanently to guarantee the highest quality as well as a very high degree of automation.
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Iterative Update Process for Rules & Standard
Assignment to CommodityRule based mapping
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Commodity Harmonization – Quality Reports
The Quality Reports are an essential part of the Harmonization Process…
Based on the Quality Reports, the progress of the iterative harmonization process could be monitored, still existing deficits could be made transparent and thus the rules & standard could be immediately adjusted to an optimum.
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Loading of Data Cube and Visualization of Data
1
Data Harmonization
2
Analysis and Reporting
3
Extraction,Processing
and Upload of Data into the Data Cube
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Multiple pre-parametered business reports available
A responsive design to display your reporting also on mobile devices
An advanced editor to create your own reports online !
A story-telling mode to create dynamic presentations
Some key features of the final Spend Analyses solution
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Glisser Question
Question: So what if I have bad data? All this means is duplicates, right?
No, it means.....?
Audience rank possible answers via Glisser:
• No bundling
• Higher prices
• More effort
• Missing opportunities
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Take away Case Study INSIGHT DATA QUALITY
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Rainer MachekExecutive Vice President
205E 42nd Street 20th FloorNew York, NY 10017 USA
+ 1 646 880 4496
Your contact at SynerTrade
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Want to be Ready for Big Data?
Make Sure to Control Small Data First!
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Rainer Machek
Executive Vice President
+ 1 646 880 4496