want to be ready for big data?

Make Sure to Control Small Data First!

Want to be Ready for Big

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

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

• 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:

Accelerate: Digital Solutions, used by 500+ customers like:

Reference: Fresenius Medical Care Project

SAP, SAP BW

Sage, Axapta, Infor

4 Regions (EUROPE, APAC, LATAM, NAFTA)

Source: internal Fresenius Medical Care Newspaper

All around the world, different taxonomies

Reference: Lufthansa Group Project

17 SAP, 2 SAP BW

1 Oracle Database

129 subsidiaries out of around 400

2,800,000 of invoices, 17 SAP Systems

Source: internal Lufthansa Newspaper

Extraction, Processing and Upload of data

1Data Harmonization

2 Analysis and Reporting

Three Steps to a Sustainable Spend Transparency

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

- Savings > Order Amount- Contract Number invalid- Incorrect Currency

Conversion- …

Data Completeness

Data Correctness /

Plausibility

Data Quality Matrix of Different Quality Dimensions

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?

TRANSACTIONS

Vendors

Quality Reports: Analysis of Missing Data

Supplier Harmonization

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.

Matching of 100.000 suppliers in ~15 min

Up to 5 Terabyte of Data for Spend Analysis

Performance

Supplier Harmonization: Overview

Spend Analysis

Match Run

Match Rules

Supplier Master Data

ProposedMatches

Automatic Matches

External Data Sources(Optional)

MatchExclusions

ConfirmedMatches

Manual Input

Match Types (like sieving sand......)

Reworking The Consolidation Status – self learning

Visualization of harmonization results:

• overview of the most important KPI and statistics

• automatic matches, match proposals, summary, etc.

Commodity Harmonization

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!

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

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.

Loading of Data Cube and Visualization of Data

Data Harmonization

Analysis and Reporting

Extraction,Processing

and Upload of Data into the Data Cube

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

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

Take away Case Study INSIGHT DATA QUALITY

Rainer MachekExecutive Vice President

205E 42nd Street 20th FloorNew York, NY 10017 USA

rainer.machek@synertrade.com

+ 1 646 880 4496

Your contact at SynerTrade

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