Short Presentation TitleTimo Elliott September 2011
Holding Supercomputers in Your Hands
“The iPad 2 could have stayed on the list of the world’s fastest supercomputers through 1994”
5
Whatever You’re Trying to Do, Analytics is the Answer
Brand Performance Analysis
Inventory
Net Margin Analysis
Trade Promotion Effectiveness
Budgeting, Planning & Consolidation
Sales & Operations Planning
Marketing Mix Analysis
Customer Sentiment Analysis
On-Shelf Availability Analysis
Data is extremely important for competitive advantage
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%
9
2009 2010
10
2009 2010 2011
Gartner: worldwide BI, analytics and performance management software revenue
“BI spending has far surpassed IT budget growth overall for several years”
Dan Sommer, Gartner
New revenue generated from IT initiatives (enterprise innovation, context- aware computing, social networks, etc.) will become the primary factor determining CIOs compensation.
Information-smart businesses will increase recognized IT spending per head by 60%.
“Enterprise leaders and stakeholders must change their way of thinking that “lower is better” for IT spending per employee”
Gartner Predicts 2011 2011 2015
+60%
12
3.0%
3.7%
6.7%
17.8%
18.3%
19.5%
22.9%
13
Business Analytics Market (BI, EPM, Analytic Applications) Share of Market, 2010
Business Analytics Market Shares
14 14
Analytic Capabilities
Services and Best Practices
The Office of Tomorrow, in 1945: “Intricate calculations of quotas or sales by territories will be turned out at the touch of an assistant’s finger. Records will appear as if by magic from files.”
17
18
Business Analytics Has Struggled to Keep Up
“Where are you going? Ah -- If I were you, I wouldn’t start from here”
19
Reporting
Query Results Query
21
22
23
Today’s Disks Can’t Keep Up With Processing Power
24
BT Tower 152m
25
Cost of 1 Mb of memory today: ≈ ½ p
My daughter: 1.30m
Price/performance of in-memory has
26
Data Marts
Row-based Column-based
Row-Based Data
Wasted space, and a full scan to aggregate any particular field
29
30
31
Calculation Engine
33
34
52 weeks x 500 branches = 26000 values
26000 database writes 1 database write
36
Volume Driver Cycles Driver Forecast Driver Forecast Agents Grow Seasonal Complex Assortment Planning Cumulate Days Days Outstanding Discounted Cash Flow De-cumulate Delay Delay Debt
Delay Stock Annual Depreciation Annual Depreciation Diminishing Balance
Depreciation Sum of Year Depreciation Year To Date Statistical YOY/ YOY Difference Forecast Dual Driver Forecast Sensitivity Feed Feed Overflow Forecast Funds Future Value
Inflated Cash Flow Internal Rate of Return Moving Median Number of Periods Net Present Value Outlook Payment Present Value Lag Last Lease Lease Variable Linear Average Forecast Mix Moving Average/Sum
Proportion Rate Repeat Seasonal Simple Seasonal Simulation Stock Flow Stock Flow Reverse Stock Flow Batch Time Time Sum Max Value Minimum Value Transform Rounding
Up until now, there’s been a false separation between application logic and database functionality
37
38
Query
Up to 1,000x faster Push processing down to dedicated hardware, less traffic
Analytic Appliance
Calculation Engine
Data Business Applications
44
6.6
41.9
HANA
3.2
5.1
5.1
1050
1320
2660
46
Copy
Data
47
Columnar Databases
Hardware Acceleration
Calculation Engine
Columnar storage increases the amount of data that can be stored in limited memory (compared to disk)
Column databases enable easier parallelization of queries
In-memory processing gives more time for
relatively slow updates to column data
In-memory allows sophisticated calculations
Hardware acceleration makes sophisticated
calculations like allocations possible
Each technology works well on its own, but combining them all is the real opportunity — provides all of the upside benefits while mitigating the downsides
“By 2012, 70% of Global 1000 organizations will load detailed data into memory as the primary method to optimize BI application performance.”
- Gartner
50
Cloud computing Unstructured and personal data Mobile revolution Collaboration
“If things seem under control, you’re just not going fast enough.”
-Mario Andretti
In-Memory Computing is Like Digital Photography
A transformative technology that slowly but surely upturns the whole industry
Faster, Easier, More Convenient
54
It’s All About Flexibility and Evolution
“It's not the strongest that survive, nor the most intelligent, but the ones most responsive to change.”
Charles Darwin
Different information sources
Different project phases
56
57
15X
9000X
16X
58
59
COPA case (includes financial line item reporting)
CRM & ECC Content (Q2 2011)
Inventory Movement
Billing Management
Order to Cash analysis
20+ SAP Projects - H2 2011 for Ramp-up & GA Early 2012
60
Preconfigured software Includes financial, sales, purchasing, shipping, and master data reporting
Rapid and affordable Deliver in as little as 6 – 8 weeks, using SAP consulting Flexibly priced
Clear path to real-time operational analytics Ready for access by SAP BusinessObjects, Excel, mobile devices…
HANA RDS Operational Reporting
Fast, powerful operational reporting
Powerful insights Flexible reporting to maximize profitability at customer, product, and SKU level
Easy deployment Easily deployed with no disruption
SAP CO-PA Accelerator
Uses ALL data available Open receivables, open payables, open shipments, purchase orders, etc. From SAP and non-SAP systems
More accurate payment predictions Based on actual customer history, not just payment terms
Track performance By company, region, customer, etc.
Full cycle approach Model & simulate impact of non-payment, re-integrate into pricing and contracts
SAP Dynamic Cash Management
cash position
Data Qwality is a Real Problem
33% Lost a customer or new business deal as a result of missing data
30% Lost business due to the mishandling of data
30% Of operating revenue is wasted through process failure and information rework
4% Rate data quality as excellent
63% Have no idea how much data quality might be costing
17% Have no plans to start a data quality initiative
88% See data as a strategic asset
Sources: Vanson Bourne; Larry English; The State of Data Quality Today
BUT
66
Scorecard to measure DQ from a Data Steward’s perspective
Key Quality Dimensions (KPI for data)
Drill into scorecard details
Business-Lead Data Cleansing
Empowers data stewards/domain experts to develop custom data cleansing solutions for any data domain Cleansing Package Builder is available within Information Steward
Cleansing Package Builder
Powerful connectivity, mapping and loading designed for the business user
Easy and repeatable Easy to use, speedy and simple to deploy
Enhances productivity, makes data integration simple
Reliable, repeatable, consistent solution at lower cost
Quality, confidence and compliance Improve insight while lowering compliance costs
Deep understanding of performance data via drill-to-origin capability as well as drill-to-source in SAP ECC
Greater trust and lesser risk in collecting, mapping and loading data
Superior connectivity Integration with SAP and non-SAP data sources
Enables “closed-loop” strategy to execution performance management via EPM–to–EPM integration
Improve EPM processes, reduce cycle times, facilitate compliance
69
If everything’s incremental, when do we do data cleansing?
Levels of quality
Column stores are good at storing text data.
Can push the text analytics algorithms into the appliance, more flexibility
74
Medtronic
75
Experience.SAP.com
76
77
No longer query – wait – analyze – format …
Iterative feedback loop allows instant feedback and learning
79
80
81
85
86
89
Altron Allied Electronics
“The days of our users and execs being in the office have gone. They work from home or on the road.
We had to develop a solution that gets information out to where our people are. Everything we do is mobile first.
In addition, it’s less cumbersome and cheaper to buy and use a tablet than any other form.”
Debra-Lynn Marais Group Information Manager
Altron Allied Electronics
91
92
94 * Requires IT integration using built-in APIs and SAP StreamWork enterprise edition if advanced security is required
Decision-Making, Not Just Data Gathering
Try it now! sapstreamwork.com
Connecting Islands
“The big failure of social business is a lack of integration of social tools into the collaborative workflow.”
97
100
102
Expertise location — Relationship Mining — Social Network Analysis
103
105
The Dunning Kruger effect
© SAP 2008 / Page 116
Pe rc
en ta
ge o
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.
117
Collaborative
118
Conclusion
Analytics industry revolution Every company in the industry heading the same direction Don’t be the last one shooting on film
Time to rethink how you do analytics Back end: simplification, new real-time opportunities Front end: mobile first, collaboration
You can start today The technology is real
Thanks!
You Should Follow Me on Twitter: @timoelliott
Drowning in Information?
Whatever You’re Trying to Do, Analytics is the Answer
Business Analytics Provides Great Value
Surging Growth in Business Analytics
Analytics is an Ever-Increasing Share of IT Budget
IT Spending Per Head Rising Fast
Business Analytics Around the World
Business Analytics Market Shares
Slide Number 16
“Typical” Business Intelligence Today
What’s the Problem?
Today’s Disks Can’t Keep Up With Processing Power
In-Memory Computing Costs Have Plummeted
In-Memory Computing Costs have Plummeted
In-Memory Computing
In-Database Analytics
In-Database Analytics
Large Bank – 1 Month of Customer Information
Slide Number 45
Applications of the Future
Virtuous Circle of Technology
It’s All About Flexibility and Evolution
Reality Is, and Always Will be, Messy
Slide Number 56
What About Big Data / NoSQL / Hadoop?
HANA Applications Available in H2 2011/2012
HANA RDS Operational Reporting
Collaborative Information Governance
Business-Lead Data Cleansing
Information Governance
Slide Number 79
Slide Number 80

Altron Allied Electronics
Next Up: Brain Waves!
Connecting Islands
Social Intelligence Needs The New Architectures
SAP Social Intelligence
SAP Social Intelligence
SAP Social Intelligence
Did You Know?
The SAP difference