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Short Presentation Titleassets.timoelliott.com/docs/sapsa_track.pdf · 2011-09-13 · +13.4%....
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Everybody Has Questions At Every LevelO
PER
ATIO
NS
l H
R l
FIN
ANC
E |
IT
| S
ALES
l M
ARKE
TIN
G
MANUFACTURING l RETAIL l HEALTHCARE l BANKING l UTILITIES l TELCO | PUBLIC SECTOR
Tom Davenport International Institute for Analytics
How and whydid it happen?
What is the risk if it does/doesn’t happen?
How do you prevent / ensure it happens again?
Whathappened?
What is happening now?
What willhappen?
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Business Analytics Provides Great Value
Data is extremely important for competitiveadvantage
Data makes an important contribution to customer relations efforts
Business information has helped manage costs or improve operations
Executives believe companies can benefit greatly from using data, especially information generated within the company
Agree: 69% Agree: 77% Agree: 70%
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Surging Growth in Business Analytics
2009 2010
+3.8%
+13.4%
Gartner: worldwide BI, analytics and performance management software revenue
BI Growth more than tripled between 2009 and 2010!
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Analytics is an Ever-Increasing Share of IT Budget
2009 2010 2011
3.9%
+4.1%
+4.3%
Gartner: worldwide BI, analytics and performance management software revenue
“BI spending has far surpassed IT budget growth overall for several years”
Dan Sommer, Gartner
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Business Analytics Around the World
Business Analytics MarketGrowth 2010
3.0%
3.7%
6.7%
17.8%
18.3%
19.5%
22.9%
Eastern Europe
Japan
Western Europe
North America
Middle East and Africa
Latin America
Asia/Pacific
13.2%
11.6%
Gartner Market Share Analysis: Business Intelligence, Analytics and Performance Management Software, Worldwide, published March 2011
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Business Analytics Market (BI, EPM, Analytic Applications)Share of Market, 2010
Worldwide Leader in Business Analytics
SAP
Oracle
SAS Institute
IBM
15.6%
13.2%
11.6%
23%
Gartner Market Share Analysis: Business Intelligence, Analytics and Performance Management Software, Worldwide, published May 2011
The 4 “Megavendors” continued to increase their market share– smaller vendors took from each other
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“In-Memory Business Analytics” is Nothing New
The “What” doesn’t fundamentally change — but the “How” does
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Key Data Challenges Today
Poor organization of data is a challenge
Agree: 88% Agree: 81% Agree: 77%
Poor processes for sharing data between departments and employees is a hurdle
Technical issues—such as data silos or incompatible systems—represent at least some challenge
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SAP HANA™■ In-Memory software + hardware
(HP, IBM, Fujitsu, Cisco, Dell)■ Data Modeling and Data Management■ Real-time Data Replication via Sybase Replication
Server■ SAP BusinessObjects Data Services for ETL
capabilities from SAP Business Suite, SAP NetWeaver Business Warehouse (SAP NetWeaver BW), and 3rd Party Systems
Capabilities Enabled■ Analyze information in real-time at
unprecedented speeds on large volumes of non-aggregated data
■ Create flexible analytic models based on real-time and historic business data
■ Foundation for new category of applications (e.g., planning, simulation) to significantly outperform current applications in category
■ Minimizes data duplication
SAP In-Memory Appliance (SAP HANA™)Architecture
SQL MDXBICSSQL
SAP In-Memory Computing studio
Sybase Replication
Server
SAP Business Objects Data
Services
SAP HANA™
Other ApplicationsSAP BusinessObjects BI Solutions
SAP NetWeaverBW
SAP Business Suite 3rd Party
SAP In-Memory Database
Calculation and Planning Engine
Row & Column Storage
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Simplified Memory Hierarchy Main memory vs. Cache (Based on Intel Nehalem)
CPU
Core
1st Level Cache
2nd Level Cache
Shared 3rd Level Cache
Core
1st LevelCache
2nd Level Cache
Core
1st LevelCache
2nd Level Cache
Core
1st Level Cache
2nd Level Cache
Size Latency
64KB 1-2 cycles
256KB 6-20 cycles
8MB 30-60 cycles
SeveralGBs up to TBs
100-400cycles
Main Memory
Flash – 5000 cycles; Disk seek – 10,000,000 cycles
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Columnar data store benefitsü Optimizes load of data to CPUü High data compressionü Very fast data aggregationü Makes use of real-life fill of tables (few fields filled, few
updates)
Can be joined with row-based data
Making Use of Columnar Data Store
A 10 € B 35 $ C 2 € D 40 € E 12 $
A B C D E 10 35 2 40 12 € $ € € $
memory address
organize by row
organize by column
A 10 €
B 35 $
C 2 €
D 40 €
E 12 $
conceptual view
mapping to memory
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§ Regression§ Correlation and Covariance§ Analysis of Variance and Designed
Experiments§ Categorical and Discrete Data
Analysis§ Nonparametric Statistics§ Tests of Goodness of Fit§ Time Series and Forecasting§ Multivariate Analysis§ Survival and Reliability Analysis§ Probability Distribution Functions
and Inverses§ Random Number Generation§ Data Mining
§ Linear Systems § Eigensystem Analysis § Interpolation and Approximation § Quadrature§ Differential Equations § Transforms § Nonlinear Equations§ Optimization § Special Functions § Statistics and Random Number
Generation
In-Database Analytics
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A Database Designed for Business
Volume DriverCyclesDriverForecast DriverForecast AgentsGrowSeasonal ComplexAssortment PlanningCumulateDaysDays OutstandingDiscounted Cash FlowDe-cumulateDelayDelay Debt
Delay StockAnnual DepreciationAnnual DepreciationDiminishing Balance
DepreciationSum of Year DepreciationYear To Date StatisticalYOY/ YOY DifferenceForecast Dual DriverForecast SensitivityFeedFeed OverflowForecastFundsFuture Value
Inflated Cash FlowInternal Rate of ReturnMoving MedianNumber of PeriodsNet Present ValueOutlookPaymentPresent ValueLagLastLeaseLease VariableLinear AverageForecast MixMoving Average/Sum
ProportionRateRepeatSeasonal SimpleSeasonal SimulationStock FlowStock Flow ReverseStock Flow BatchTimeTime SumMax ValueMinimum ValueTransformRounding
Up until now, there’s been a false separation between application logic and database functionality
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Extending HANA
Business ApplicationsAnalytic Appliance
Business Intelligence
Cloud computingUnstructured and personal dataMobile revolutionCollaboration
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What about Hadoop?
Complementary technology
Very real value, but immature
Primarily used today for preprocessing unstructured data
Velocity
Volume Variety
New analytic
platforms
HADOOP
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HANA, Sybase IQ, And HADOOP
Event Driven Transactional Processing EDWOperational
Data Store
Multi-Dimensional
OLAP
Real-Time Real-Time Intraday+Intra-hour Intraday+
Small< 1GB
Small< 1GB
Large1 TB+
Medium100 GB+
Medium100 GB+
Events Parameterized ParameterizedParameterized Ad-HocPredictive
Analysis
Data Volume
Latency
Batch Processing
Intraday+
Very Large10 TB+
Ad-HocPredictive
Structured Data Analytics Un-StructuredSemi-Structured
Event Insight SAP ECC
Sybase IQ
HANA HADOOP
+
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Reality Is, and Always Will be, Messy
Different information sources
Different levels of expertise
Different access devices
Different time horizons
Different levels of analytic need
Differentproject phases
RiskPolitics
But new architectures mean simplification and new opportunities
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Data Quality
Data Integration
Master Data Mgt
Meta Data Mgt
Business IntelligenceTop-to-bottom
visibility required
Enterprise Information Management
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Real-Time Data Quality
If everything’s incremental, when do we do data cleansing?
Levels of quality
In-db cleansing
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Unstructured Data
Column stores are good at storing text data.
Can push the text analytics algorithms into the appliance, more flexibility
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Text Data Processing for Unstructured Data
http://experience.sap.com/twitterta/sapsummit.jsp
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Centralized Infrastructure, Full Autonomy
Data Warehouse
ApplicationData Department
Data
Personal Data
Ease of Use is The #1 Barrier to Deployment
Top Roadblocks to BI Success
Challenge Rank
Complexity of BI tools and interfaces 1Cost of BI software and per-user licenses 2
Difficulty accessing relevant, timely, or reliable data 3
Insufficient IT staffing or excessive software requirements for IT support 4
Difficulty identifying applications or decisions that can be supported by BI 5
Lack of appropriate BI technical expertise within IT 6
Lack of support from executives or business management 7
Poor planning or management of BI programs 8
Lack of BI technology standards and best practices 9
Lack of training for end users 10
1. Doug Henschen, InformationWeek, “BI Efforts Take Flight”, Oct 13, 2008
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Services Analysis PurchasingFinancials Sales
Sales Overview• Sales Order per Customer (List
reporting)• Fulfillment rate (static, per value and
per quantity)• Credit Memo List• Billing Document List• Sales Organization Analysis
Master Data• Material List• Customer List• Vendor List
Generic
Accounting Overview• Flexible customer open item reporting
(Debitor)• Flexible vendor open items reporting
(Creditor)• Flexible open item reporting (New
General ledger)• Customer open item analysis (Day
sales outstanding)• New General Ledger line item
reporting
Shipping
Purchasing Overview• Order History Overview• Purchase Orders• Goods Receipts / Service Entries• Return Delivery Rate• Vendor Invoices
Shipping Overview• Outbound Deliveryies• Outbound Deliveries for Picking• Outbound Delivery Items• Outbound Delivery Items for
Picking• Stock Overview
HANA Rapid Deployment Solution for SAPHANA 1.0 SAP Operational Content – Included with HANA
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Real-Time Financial Applications
Financial applications are the lowest-hanging fruit for the new architectures
Operational, but very analytic – push down budget algorithms, forecasting, ABC costing, etc. down into the underlying architecture
Strategy & RiskManagement
Business Planning& Consolidation
Execute withCompliance
PerformanceOptimization &Sustainability
AnalyticPlatform
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SAP HANA ERP CO-PA Accelerator
n Accelerated reporting and month-end closing processes
n Flexible CO-PA Reporting, not limited in data scope
n Super-fast processing of queries and drill downs against large data volumes
n Easy integration of legacy CO-PA data with flexible modeling in PCM
n Better commercial decision making - pricing, discounts, sales strategy etc.
n Optimized month end close
n Timely self-service analytics for business users
n Rapid deployment and low cost of ownership
Expected benefitsNeeds
Faster processes and deeper insight for competitive differentiation
SAP In-Memory Computing - potential business impact
§ Business performance is impacted by poor reporting and month-end closing runtimes
§ Limited profitability insight and root-cause understanding due to large or incomplete data sets
Typical Business ChallengesSAP In-Memory Computing
600 million records
Drill-down to detail in seconds
Analyze any SKU, product family, region, time period …
HANA CO-PA Analysis
0 200 400 600 800 1,000
Standard System
In-Memory System
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Structure of the CO-PA model in HANA
Keys:-Object no.-Customer-Product-Sales Org-...
Realigned keys:-Object no.-Customer-Product-Sales org-...
Persisted key figures:-Gross sales-Sales deductions-Variable cost-..
Line items CE1xxxx Objects CE4xxxx
Master data and texts
Master data and textse.g. for product
Analytic View for Actuals
Calculated key figures:-Contribution Margin-Net Sales-...
Keys:-Object no.-Customer-Product-Sales Org-...
Realigned keys:-Object no.-Customer-Product-Sales org-...
Persisted key figures:-Gross sales-Sales deductions-Variable cost-..
Line items CE2xxxx Objects CE4xxxx
Analytic View for Plan Data
Calculated key figures:-Contribution Margin-Net Sales-...
Calculation View for Plan & Actuals
HANA CO-PA AcceleratorERP CO-PA with HANA as secondary database
The solution
HANA
HANA 1.0
~30x compression100x faster
ERP
Traditional DB
CO-PAProfitability Analysis
Report Writer
BOBJ BI
BOBJ BI 4.0
Flexible reporting
Unabridged data
Archived data
Fasterallocations
Fasterreporting
Accelerated month-end closing ü Accelerated CO-PA Reportingü Accelerated ERP CO-PA Allocations
Improved Reportingü Business user driven data analysisü Instant response times
Eliminated data boundariesü No pre-defined data aggregation levelsü Complete life-time, line-item analysis
Deeper Insightü High-volume and ad-hoc data queriesü No limitations to reporting dimensions
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Line of Business: Finance SAP Dynamic Cash Management
§ Consolidated visibility and powerful calculations of massive volumes of transactional data (millions of documents) from heterogeneous source systems
§ Powerful cash forecasting based on critical drivers such as open shipments, open PO’s and customer/company payment behavior
§ Real-time visibility into a company’s cash position by customer, product, supplier, region, and broader time horizon
§ Easy-to-use interface based on Excel and SAP BI tools
n Maximize return on liquid/cash assets and optimize the company’s cash flow positions
n Improve forecast accuracy of cash flows to ensure critical business operations
Key BenefitsCapabilities
§ Ineffective management of cash, accounts receivables/payables leading to sub-optimal return on liquid assets
§ Limited visibility of cash due to challenges in consolidating cash flow data and restrictions in current systems (e.g., limited to 30-day window, lack of customer/document-level details)
§ High forecast error of cash due to insufficient visibility of drivers such as open shipments, PO’s, customer payment behavior
Business Challenges
Advanced by SAP In-Memory Computing
A new SAP application, advanced by SAP In-Memory Computing, that delivers powerful forecasting capabilities and real-time visibility for a company’s cash flow management processes
Solution Overview
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Line of Business: Finance SAP Dynamic Cash Management
§ Consolidated visibility and powerful calculations of massive volumes of transactional data (millions of documents) from heterogeneous source systems
§ Powerful cash forecasting based on critical drivers such as open shipments, open PO’s and customer/company payment behavior
§ Real-time visibility into a company’s cash position by customer, product, supplier, region, and broader time horizon
§ Easy-to-use interface based on Excel and SAP BI tools
n Maximize return on liquid/cash assets and optimize the company’s cash flow positions
n Improve forecast accuracy of cash flows to ensure critical business operations
Key BenefitsCapabilities
§ Ineffective management of cash, accounts receivables/payables leading to sub-optimal return on liquid assets
§ Limited visibility of cash due to challenges in consolidating cash flow data and restrictions in current systems (e.g., limited to 30-day window, lack of customer/document-level details)
§ High forecast error of cash due to insufficient visibility of drivers such as open shipments, PO’s, customer payment behavior
Business Challenges
Advanced by SAP In-Memory Computing
A new SAP application, advanced by SAP In-Memory Computing, that delivers powerful forecasting capabilities and real-time visibility for a company’s cash flow management processes
Solution Overview
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10k m
De NHM kijker
Eerste Romeinsenederzetting: “OppidumBatavorum”Jaartal: 12 voor Chr.Afstand: 300 meter
0.3
Augmented Reality
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Filter by: Maintenance History
Tower Pipe 3Last Maintenance: 2 Weeks
E 0.1km
Photo by Thomas Hawk, Flickr
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How Not To Stock Up For Promotions
SKU ProductAverage items sold prior 3 weeks
Items sold during special promotion % increase
120595 Kams Mint Toothpaste 8 oz 72 112 56%593300 Peepers Size 5 Diapers 32 pack 134 170 27%309454 Pata Negra Ham Sandwich 35 43 23%139913 Closers Breath Mints 40 112 180%149292 Bboy Barbecue Charcoal 2lbs 17 98 476%249200 Lindas Cookie Ice cream kids treats 26 65 150%202184 Giant Corn Chowder Soup 12 oz can 43 84 95%233120 Silly String Cheese, Lunch pack 12 55 358%210653 Green Label 6-pack beer 120 115 -4%
499 854 151%71%
Average of % Increase
Better: Ratio of total items sold provides different % increase
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The REAL Big Leap Forward
© SAP 2008 / Page 94
Breadth and Sophistication of Possible Analytical Tasks
Perc
enta
ge o
f Use
rs D
oing
or
Thin
king
abo
ut th
ese
task
s
Quantitative Thinking Gap
Huge opportunity to make business people more productive and efficient, increase their satisfaction, save money for the company, and drive more revenue.
Thanks!
Email:[email protected]
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