NorgesGruppen AS...Norway’s largest grocery enterprise A better everyday life NorgesGruppen in brief Page 2 / 03.10.2012 Norway’s largest food retailer • NOK 58,6 billion in

NorgesGruppen AS Norway’s largest grocery enterprise

A better everyday life A better everyday life

NorgesGruppen in brief

03.10.2012 Page 2 /

Norway’s largest food retailer

• NOK 58,6 billion in operating revenues in 2011

• 7th largest company in Norway

Serves the following market segments

• Grocery trade

• Kiosk/convenience trade

• Catering (HoReCa)

Wholly-owned and dealer-owned retail grocery and kiosk

operations nationwide

• 2 350 sales outlets and approx. 27 000 employees affiliated to NG

• 37,4 % of the Norwegian grocery market*

• 45 % of the Norwegian catering market*

Norway’s largest grocery wholesaler

• 13 regional units

Property, product development and branded products

* Source: Nielsen Dagligvarerapporten 2012, Servicehandelsrapporten 2011 and Andøy Data (SHH)


NorgesGruppen’s business areas

03.10.2012 Page 3 /

NorgesGruppen

Wholesale Retail

Logistics

Distribution

Wholly owned stores

Chain service centers for

grocery and convenience

trades

Warehousing

Marketing/sales

Other activities Property

A better everyday life

Dagligvare Servicehandel Servering/

Storhusholdning Faghandel

Eier

Deleier

Leveranser

Konseptene

Danmark

Sverige

Side 4 / 03.10.2012 Side 4 /

http://www.jafs.no/cparticle236181-36100a.html

http://www.statoil.no/FrontServlet?s=sdh&state=index&showMenu=

http://www.rica.no/


Norwegian grocery market 2011: NOK 143,7 billion

Total market growth: 3.8 % from 2010 to 2011

NorgesGruppen 37.4 % (+0,5)

COOP Norge 23.4 % (-0,3)

Rema 1000 21.3 (+ 0,4)

ICA Norge 14.1 % (-0,8)

Bunnpris 3.8 % (+ 0,1)

03.10.2012 Page 5 /

Source: Nielsen Dagligvarerapporten 2012


Collaboration in supply chain: efficiency and operational

03.10.2012 Side 6 /

SUPPLIERS

CUSTOMER

NORGESGRUPPEN

ENGROS

GROCERY NG owned

Partly owned

Storcash

CATERING

SERVICE TRADE

UNIL

(EMV) Wholesale-

distribution

Direct-

distribution


Corporate function: Category

development/purchasing

03.10.2012 Page 7 /

Group’s central purchasing unit, with responsibilities for:

• NorgesGruppen’s private labels

• Development of product range and categories for

chains and individual customers in the grocery and convenience trades

professional catering customers

• Quality and food safety

• Negotiation of terms, agreeing contracts with suppliers

Expertise focused on three segments

• Grocery trade

• Kiosk/convenience trade

• Catering

Knowledge of trends and consumption patterns

• Important tool for developing and tailoring product ranges

Comprehensive set of specifications for selecting suppliers

• Including price, delivery terms, range, quality, safety, environmental

and ethical standards, etc.

Development, management and marketing of private

labels


Goal of Data Warehouse

Covering business requirements

Effectivily bring information to end users to help them make business decisions

03.10.2012 Page 8 /


Journey

03.10.2012 Page 9 /

Business requirements Product


NorgesGruppen Data Warehouse

03.10.2012 Page 10 /

Business needs, requirements and realities

(Behovsinnhenting og kravspesifikasjon)


Business needs

Should:

• Support business processes

• Support business’ KPI’s

• Decide necessity of bringing the decision support into data warehous if data is not to be

collocated with other data

What information does the end user need to make informed business decisions?

• Interwiev the end user

Does the data warehouse already contain the required information?

• Yes

• No: Identify the different new datasources to be introduced into the data warehouse

03.10.2012 Page 11 /


Requirements and realities

Is the end users exceptations realistic?

• Complexities

• Limitations

Are there additional useful information in the source which can help end users making

informed decisions?

03.10.2012 Page 12 /

Dialog Developer End user



03.10.2012 Page 13 /



Developing methodology and design

(Utviklingsrammeverk)


Developing methodology 1

Common approaches

• Kimball: BOTTOM-UP-APPROACH

Let everybody build what they want and when they want. Normalization in Operational data

store (Data Vault/Grunnlag). Denormalized in reporting layer.

• Inmon: TOP-DOWN-APPROACH

Dont do anything until design is done. All data should be normalized.

• Hybrid: mix of Kimball and Inmon methodologies

• And many more

03.10.2012 Page 14 /


Developing methodology 2

03.10.2012 Page 15 /

Architecture

Uniform modelling

Naming of objects

Required documentation

Load strategy

Best practice

Data modelling

Vendor tools


Design 1

Data modelling

• Foundation for database

• Data re-use and sharing

• Clarifies spesifications

• Confirms business

requirements

• Decrease system

development and

cost

03.10.2012 Page 16 /


Design 2

Data flow and load strategy

03.10.2012 Page 17 /


Design 3

03.10.2012 Page 18 /

Layout



03.10.2012 Page 19 /





Stage


Stage

Temporary storage of data

Tables quite simiilar to source

If source is database: Source system provides interface views or interface tables

• ”We are here and we use this data - dont change our source without telling!”

Examples of other types of sources:

• Flat files

• Xl files

• Xml files

• Input from end user from interface

• Many more

No high cost joins in source

No reporting on staging area

Normally no historical data – tables are truncated before loading

If historical data is needed : Two sets of tables

03.10.2012 Page 20 /



03.10.2012 Page 21 /





Stage

Data vault / Opreational data store

(Grunnlag)


Data vault

Foundation of Data Warehouse

Dimensional tables

Historical fact tabels

• Normalized

Source for fact tables is stage

Normally no reporting/ analyzing on these tables

03.10.2012 Page 22 /



03.10.2012 Page 23 /





Stage

Data vault / Operational Data Store

(Grunnlag)

Mart / Analyzing area


Mart

Aggregated information in tables

• Example: Sales per week, sales pr month, sales pr quarter …

Several data marts

• Diveded into areas / needs

Analyzing / reporting layer

Business rules implemented

Most of these tables are loaded at night, thus loading will not affect reporting.

03.10.2012 Page 24 /



03.10.2012 Page 25 /





Stage

Data vault / Operational Data Store

(Grunnlag)

Mart / Analyzing area

Reports Analysis

Dashboards


Reports, analysis, Dashboards

Tables in marts are foundation for end users reports/dashboards and analysis.

Reports are updated when agreed

Well designed reports provide:

• Necessery tool for end user

• Supported business decisions

03.10.2012 Page 26 /

Business case: Salgsmappe

03.10.2012 Page 27 /


Salgsmappe: Needs, requirements and realities

Joh Johanson Kaffe have consultants who visit their customers (shops) frequently

• Need of a tool to help them follow up customers

Folow ups:

• Stock

• Sales

• Placement in shop

Details in report (requirements):

• Sales per group of coffee

• Coffe sales vs. total sales in shop

• Joh Kaffe sales vs. competitors sales

• Stock

• Campaigns

Additional needs:

• Report should be PDF - printable

• Future: Report access by smartphone and/or tablet

• Consultant should only see own customers

03.10.2012 Page 28 /


Design 1

03.10.2012 Page 29 /


Design 2

03.10.2012 Page 30 /


Design 3

Sources

• Existing

Sales fact

Coffe groups

• New

Campaign information

– Note! Order database view from source

Relation: consultant – customer

– Not available in databases

» Solution: XL-source updated and owned by business

03.10.2012 Page 31 /


Design 4

03.10.2012 Page 32 /

Campaign

2 new db-

sources

Consultant

& customer

1 new

source (xl)

WM811T_

DKAFFEG

RUPPE

WB100T_SSAL

GBUTVAREUK

E

Mart tables WF162T_

DKUNDE

WF700T_DP

ERIODE

R

E

P

O

R

T WM810T_

FOMSBU

DSJKAFF

W


Stage, Data vault & Mart: Load 1

03.10.2012 Page 33 /

Powercenter Designer mapping


Stage, Data vault & Mart: Load 2

03.10.2012 Page 34 /

Powercenter Workflow


Load

03.10.2012 Page 35 /

Powercenter workflow


Report development 1

03.10.2012 Page 36 /


Report Development 2

03.10.2012 Page 37 /

SELECT

SCMDIM01.WF162T_DKUNDE.PROFILKJEDE_NV,

Table__84.KUNDE_NV,

Table__84.BESOKADRESSE || case when length(Table__84.BESOKADRESSE) > 0 then ', ' else '' end

|| Table__84.BESOKPOSTNUMMER || ' ' || Table__84.BESOKPOSTSTED || ' Kontaktperson: ' || Table__84.KONTAKTPERSON

|| ', Tlf: ' || Table__84.TELEFON_NR || ', email: ' || Table__84.EMAILADRESSE_TX,

SCMDIM01.WM811T_DKAFFEGRUPPE.KAFFEGRUPPE_NV,

ALLE_Kaffekonsulenter.NAVN,

Sum(SCKAFFE.WKA008T_SSALGBUTKAFFEVAREUKE.SALGSANTVEKT * SCMDIM01.WF023T_DVARE.NETTOVEKT),

SCMDIM01.WF162T_DKUNDE.KUNDE_NR,

to_char(SCMDIM01.WF700T_DPERIODE.UKE)

FROM

SCMDIM01.WF023T_DVARE,

SCMDIM01.WF162T_DKUNDE,

SCMDIM01.WF700T_DPERIODE,

SCMDIM01.WM811T_DKAFFEGRUPPE,

(

select kunde_nr, KUNDE_NV, BESOKADRESSE, TELEFON_NR, EMAILADRESSE_TX, KONTAKTPERSON, BESOKPOSTNUMMER,

BESOKPOSTSTED

from (

select distinct

k.KUNDE_NR,

k.KUNDE_NV,

k.BESOKADRESSE,

k.TELEFON_NR,

k.EMAILADRESSE_TX,

k.KONTAKTPERSON,

K.BESOKPOSTNUMMER,

K.BESOKPOSTSTED,

dense_rank() over (partition by K.KUNDE_NR order by K.GYLDIGTIL_DT desc, K.KUNDE_NR, K.KUNDE_LNR desc) rk

from SCMDIM01.WF162T_DKUNDE k

where K.NIVA_KD = 'K' and K.HOVEDSELSKAP_JN = 'J’

+

+

+


Report: Salgsmappe

Published weekly from Business Objects (reporting tool) to consultants mailbox

Format:

• Ipad / Iphone

• Print

Salgsmappe

03.10.2012 Page 38 /

inf5100-norgesgruppen-salgsmappe-2012.pdf

BI – How to succeed

03.10.2012 Page 39 /


Challenges and Success Criteria Measures

Complex tools

Poor response time

Ownership

Using other tools

Data Quality

BI Trends

03.10.2012 Page 41 /


BI Trends 2012

03.10.2012 Page 42 /

BI in the Cloud

Challenge:

• Moving data into the Cloud initially

• Security, network and bandwidth

• Quality of the data to transfer

• Usable interface

Experience:

• NorgesGruppen Data Warehouse has not tried out the concept of

cloud computing


BI Trends 2012

03.10.2012 Page 43 /

Mobile BI

Challenge:

• Security

• New tools(competence and skills)

• New guidelines and metohods for development

Benefits:

• Access from everywhere

• Easier to adopt

• Cost-effective

Experience:

• Norgesgruppen Data has defined a strategy for use of mobile interface in general. DW group

has just started to look at the possibilities from a technical perspective, to prepare for the needs

and requirements from the business.

* Source: Gartner and CIO.com


BI Trends 2012

03.10.2012 Page 44 /

Analytics

Challenge:

• Require different skills and competence(Statistics)

• Different tools

Benefits:

• Run the business more efficiently, make the most their customers and increase profitability

• Actionable intelligence

• Optimised decision management

• Visual presentation of increasingle complex data; new data analytics such as social media and

location information

Experience:

• NorgesGruppen has done advanced analytics for a decade, but this has been a very manual

process. We see that this can be done more efficiently in the future, so we are looking for tools

that can fit our business needs.



BI Trends 2012

03.10.2012 Page 45 /

In-memory analytics

RAM technology. No substitute for quality data. Should be used in conjunction with a

structured quality data warehouse solution.

Challenge:

• New technology

• New type of data modeling

Benefits:

• Faster development because of less use of aggregation

• Faster analytics

Experience:

• NorgesGruppen has looked at some tools, but technology is yet not mature enough for business

needs in NorgesGruppen. Also it will require a lot of effort to start with new tools, new

competence etc..



BI Trends 2012

03.10.2012 Page 46 /

The Agile approach to BI

Planning is done upfront

Faster result

Possible cut in project cost

Experience:

• Java teams in NorgesGruppen uses agile approach such as Scrum. The DW group is not there

yet, but we will have an long term approach implementing a more agile way to work.


BI Trends 2012

03.10.2012 Page 47 /

Big Data

• MORE Velocity

•MORE Variety

•MORE Volume



How does the vendor meet this six Trends - Appliance

03.10.2012 Page 48 /

* Source: http://www.sfdama.org/Presentations/2010/Saama_DAMA_BI_Appliance.pdf

1st Generation 2nd Generation 3rd Generation 4th Generation

Fe

atu

res

• Standard database

servers

• Standard Storage

(Local, SAN, NAS)

• Standard processors

(single core)

• Serial processing

• Processing in the

Database

• Powerful database

servers


(Local, SAN, NAS)

• Improved processors

(dual core, quad core)

• Parallel processing

• Processing in the

database

• Initial BI Appliances


(Local, SAN, NAS)

• Improved processors

(multi quad core)

• Massively Parallel

processing

• Processing at Storage

level and at database

level

• Advanced BI

Appliances

• Virtualization

• Storage in the Cloud

(Private/Public)

• Infrastructure as a

Service (IaaS) model

• Hadoop/MapReduce

Ad

va

nta

ge

s /

Dis

ad

va

nta

ge

s • Slow processing

• Index dependent

• High maintenance

• Multiple Vendors

• Faster processors

• Index dependent

• Limitations on I/O

• High maintenance

• Multiple vendors

• Black Box – less

maintenance

• Fast processors

• Index independent

• No Limitations on I/O

• Infrastructure Heavy

• Sufficient for several

terabytes

• Cloud – no

maintenance

• Pay-as-you-go

• Fast processing

• Caters to Petabytes

of

data

Ve

nd

ors

• Oracle, Sybase, DB2 • Oracle, Sybase, DB2 • Teradata, Netezza,

GreenPlum, Exadata

• GreenPlum (Private

Cloud), Vertica (Public

Cloud)

http://www.sfdama.org/Presentations/2010/Saama_DAMA_BI_Appliance.pdf


BI Trends 2012

03.10.2012 Page 49 /

Summary

• Gartner believe these six areas will converge and grow over the next few years.

• Organisations will embrace the Agile approach, utilising new tools and technologies to decrease

delivery times and demonstrate substantial business value.

• As we put more data into the Cloud, big data will become standard, which will in turn drive more

sophisticated analytics back out of the Cloud.

• Data itself will be delivered to satisfy the desires of users, so access from mobile devices will

lead over desk-based consumption. This entire process is cyclical; as users become more

demanding of their mobile interfaces, the process will start again, prompting more agile

development, more data into the Cloud and more analysed data out of the Cloud.

• The businesses that embrace these new business

intelligence trends, and take steps to change and adapt

the way data is hosted, analysed, utilised and

delivered, will be the ones that grow and prosper in

2012 and well beyond. They are the ones to watch.


NGData NorgesGruppen’s IT-supplier

Side 50 /

3. okt. 2012


NorgesGruppen’s organisation

*) I NorgesGruppen Detalj inngår bl.a. eiendom og servicehandel.

Side 51 / 03.10.2012 Side 51 /

Konsernsjef

Økonomi / Finans

Kommunikasjon og

samfunnskontakt

Kategori/

Innkjøp

IT

ASKO Kjøpmannshuset NG Detalj* KIWI Meny–Ultra

HR Miljø


NorgesGruppen Data

200 employees

4 basic values;

Fremtidsrettet Profesjonell Arbeidsglede Brukerorientert

Our vision is to make: (solutions that secure winners)

03.10.2012 Side 52 /

”Løsninger som sikrer vinnere”


Datawarehouse and the team - history

1998 - the team was created – 1 person

• Informatica Powercenter (ETL-tool) and SAP Business Objects was aquired

2003 - the team had increased to 4 people and the first major delivery of a

datawarehouse solution had been made

2005 – The wholesale part of NG initiates a large DW project. The goal: Replace the

mainframe reporting service they had had since 1985.

2009 – Mainframe reporting server is shutdown and the DW has over a thousand users.

2011 – New database platform. From traditional Oracle to Oracle Exadata – Exadata

Hybrid Columnar Compression (EHCC) db.

• Increase in performance

• Decrease in use of disk space

2012 – 7 employees, 4 temporary consultants

• Large and increasing need for solutions that streamline the work processes in the business and

provide appropriate aid in making the correct business decision quickly

03.10.2012 Side 53 /

A better everyday life A better everyday life 03.10.2012 Side 54 /

0

200

400

600

800

1000

1200

1400

1600

0

2

4

6

8

10

12

14

16

18

1998 2003 2005 2009 2011 2012 2013

Storage in GB

Number of users

Data warehouse disk use and increase in number of users

in NorgesGruppen during the past 15 years


DW team

Composition of backgrounds and competence

• Engineers

• Masters and bachelors from the university

• Economists

• And other backgrounds …..

A wide range of tasks to be peformed on regular basis

• Design and solutions, both functional and technical

• Estimates

• Source analysis

• Build ETL jobs

• Data quality checks

• Patching, bugfix, hotfix

• Testing

• Build reports, dashboards

• Documentation

• Help users, reporting functionality and contents

03.10.2012 Side 55 /


Opportunities in NGData

Master thesis collaboration

Career

• We are always on the lookout for good candidates

• Full-time positions

• Training in relevant tools will be given

• You will get a diverse and broad experience in one the largest datawarehouse enviroments in

Norway

We are happy to receive open applications

Contactpoints in NGData

• E-mail applications to

Group leader Tove Aulie, [email protected]

Kristian Bjerke: [email protected]

Lise Henriksen: [email protected]

• www.norgesgruppen.no

Ledige stillinger i NorgesGruppen

03.10.2012 Side 56 /

mailto:[email protected]



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NorgesGruppen AS...Norway’s largest grocery enterprise A better everyday life NorgesGruppen in brief Page 2 / 03.10.2012 Norway’s largest food retailer • NOK 58,6 billion in