norgesgruppen as...norway’s largest grocery enterprise a better everyday life norgesgruppen in...
TRANSCRIPT
NorgesGruppen AS Norway’s largest grocery enterprise
A better everyday life A better everyday life
NorgesGruppen in brief
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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)
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NorgesGruppen’s business areas
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NorgesGruppen
Wholesale Retail
Logistics
Distribution
Wholly owned stores
Chain service centers for
grocery and convenience
trades
Warehousing
Marketing/sales
Other activities Property
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Dagligvare Servicehandel Servering/
Storhusholdning Faghandel
Eier
Deleier
Leveranser
Konseptene
Danmark
Sverige
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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)
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Source: Nielsen Dagligvarerapporten 2012
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Collaboration in supply chain: efficiency and operational
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SUPPLIERS
CUSTOMER
NORGESGRUPPEN
ENGROS
GROCERY NG owned
Partly owned
Storcash
CATERING
SERVICE TRADE
UNIL
(EMV) Wholesale-
distribution
Direct-
distribution
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Corporate function: Category
development/purchasing
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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
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Goal of Data Warehouse
Covering business requirements
Effectivily bring information to end users to help them make business decisions
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Journey
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Business requirements Product
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NorgesGruppen Data Warehouse
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Business needs, requirements and realities
(Behovsinnhenting og kravspesifikasjon)
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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
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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?
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Dialog Developer End user
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NorgesGruppen Data Warehouse
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Business needs, requirements and realities
(Behovsinnhenting og kravspesifikasjon)
Developing methodology and design
(Utviklingsrammeverk)
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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
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Developing methodology 2
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Architecture
Uniform modelling
Naming of objects
Required documentation
Load strategy
Best practice
Data modelling
Vendor tools
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Design 1
Data modelling
• Foundation for database
• Data re-use and sharing
• Clarifies spesifications
• Confirms business
requirements
• Decrease system
development and
cost
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Design 2
Data flow and load strategy
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Design 3
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Layout
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NorgesGruppen Data Warehouse
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Business needs, requirements and realities
(Behovsinnhenting og kravspesifikasjon)
Developing methodology and design
(Utviklingsrammeverk)
Stage
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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
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NorgesGruppen Data Warehouse
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Business needs, requirements and realities
(Behovsinnhenting og kravspesifikasjon)
Developing methodology and design
(Utviklingsrammeverk)
Stage
Data vault / Opreational data store
(Grunnlag)
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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
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NorgesGruppen Data Warehouse
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Business needs, requirements and realities
(Behovsinnhenting og kravspesifikasjon)
Developing methodology and design
(Utviklingsrammeverk)
Stage
Data vault / Operational Data Store
(Grunnlag)
Mart / Analyzing area
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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.
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NorgesGruppen Data Warehouse
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Business needs, requirements and realities
(Behovsinnhenting og kravspesifikasjon)
Developing methodology and design
(Utviklingsrammeverk)
Stage
Data vault / Operational Data Store
(Grunnlag)
Mart / Analyzing area
Reports Analysis
Dashboards
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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
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Business case: Salgsmappe
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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
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Design 1
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Design 2
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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
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Design 4
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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
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Stage, Data vault & Mart: Load 1
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Powercenter Designer mapping
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Stage, Data vault & Mart: Load 2
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Powercenter Workflow
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Load
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Powercenter workflow
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Report development 1
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Report Development 2
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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’
+
+
+
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Report: Salgsmappe
Published weekly from Business Objects (reporting tool) to consultants mailbox
Format:
• Ipad / Iphone
Salgsmappe
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BI – How to succeed
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Challenges and Success Criteria Measures
Complex tools
Poor response time
Ownership
Using other tools
Data Quality
BI Trends
03.10.2012 Page 41 /
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BI Trends 2012
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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
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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
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BI Trends 2012
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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.
* Source: Gartner and CIO.com
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BI Trends 2012
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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..
* Source: Gartner and CIO.com
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BI Trends 2012
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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.
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BI Trends 2012
03.10.2012 Page 47 /
Big Data
• MORE Velocity
•MORE Variety
•MORE Volume
* Source: Gartner and CIO.com
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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
• Standard Storage
(Local, SAN, NAS)
• Improved processors
(dual core, quad core)
• Parallel processing
• Processing in the
database
• Initial BI Appliances
• Standard Storage
(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)
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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.
* Source: Gartner and CIO.com
NGData NorgesGruppen’s IT-supplier
Side 50 /
3. okt. 2012
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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
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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”
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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
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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
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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
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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
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