the big picture · hyperion outlooksoft tm/1 essbase focus microstrategy ... some vs. all data ......
TRANSCRIPT
The BIg Picture
Dinsdag 17 september 2013
2
A short historical overview on BI
Current Issues
Current trends
Future architecture
First steps to this architecture
Agenda
3
Report
Database
Multi- dimensional
Analytics
Backend
Frontend
Data Sources
APL
Files (R)DBMS
Report Writer
Drill down
Mainframe TSO Unix
3270 DOS PC
Files Records
MIS/EIS
OLAP
DWH
Pivot Charts
Slice and Dice
Client Server
Windows PC
ERP
Data Warehouse
OLAP, MDX
Layered Struct. Arch.
Dashboards, Maps Traffic Lights
Exceptions Events
Database & Appl. server
Web
ERP+, Web 3rd Party
BI
Slice and Dice, OLAP ETL concept Information presentation
External data
Analytics Integration budget/LE
Dashboards
Many loading options
Budgeting Maps
Performance Big Data Flexibility
Too complex
Historical view
Easy to produce reports
Many reports
Not flexible Data
logistics Graphs
4
Device
Data Layer
Data Sources
End user tool layer
PC / Excel Web Mobile
Reporting Budgeting Dashboards
DWH In-Memory Real-time
virtual
ERP Other
e.g. Excel 3rd Party
Current Architecture
5
Proclarity
Tealeaf
Siebel
Analytics Adaytum
KXEN
Netezza
Qlikview
Hadoop Roambi
Tableau Spotfire
Google EveryAngle
Hyperion
Outlooksoft
TM/1
Essbase
Focus
MicroStrategy
SAS Cognos
Brio
BusinessObjects
Crystal
Reports
DataStage
Informatica
Sunopsis
Acta
Ascential SPSS
Excel
Lotus
Access
Informix
DB/2
Oracle
Ingres
SQL Server
Sybase IS-AS-RS
SAP BW
InfoSphere
Oracle
DWH
6
Current Issues BI
Too rigid, inflexible
Nice architecture, but takes too much time to change model
No room for project data
Time to report too slow
Not all data is suited for a Data Warehouse (e.g. Big Data)
Too complex to use
Training required to empower users
No BI Self Service, still dependent on IT
Too Slow
Users expect google like performance...
Still not very Intelligent
Past very well described, but how about the future?
What information can be drawn from data?
7
Trends
Consumerization
Intuitive
Mobile
Easy and advanced
Big Data
98% of today’s information is digital
Characterized by
Some vs. All Data
Accurate vs. Messy
Causation vs. Correlation
One road to the truth
Data/information logistics with 3rd parties
Uniform access to data
Data concentration for all departments
Google like performance
In memory databases
Map reduce & HDFS
8
Trends visualized
9
Data Sources
End user tool layer
Data Warehouse layer
ERP
EDW (In Memory) Sandbox Big Data DW
Analytical Applications
Visualization Dashboards Reports
Mobile Web Desktop
CRM SRM Web Mail Sensors Robots Machine
Data Mining & Predictive Models
Data Lake
The era of ‘13-’20
10
Report
Database
Multi- dimensional
Analytics
Backend
Frontend
Data Sources
OLAP, MDX
Layered Struct. Arch.
Dashboards, Maps Traffic Lights
Exceptions Events
Database & Appl. server
Web
ERP+, Web 3rd Party
BI
In Memory network
HANA, HIVE, HBASE
Interactive Hybrid reports
Data Mining, Predictive Fcst, MapReduce
Cloud HDFS
Tablet, Mobile
MES, social media transactions
BA
In-Memory, fast!
Data Mining, forecasting Sand boxes
Big Data
11
Use cases of Big Data
Tracking outbreaks of seasonal flu based upon Google search archives (Google paper published in Nature in 2009) by comparing historical influenza data with the 50 million most used search terms between 2003 and 2008.
First time right yield in manufacturing company: Joining large datasets from sensors and product data to understand current process capabilities for critical parameters and bring these parameters under control
UPS: using sensors in cars to identify certain heat and vibrational patterns that in the past have been associated with failures in those parts
Ad tracking: E-commerce sites record enormous amount of data which can, for instance, be used to track the effect of place, color, size, wording and other features of add’s on websites.
Mapping tweets to geographical maps and analyzing the emotions in those tweets to predict possible problem areas
CAT Scan comparison to facilitate the automatic diagnosis of medical issues
12
SAP filling in the gaps
13
Each company roadmap will be different
End user tool layer
Data Warehouse layer
Sources
Data Lake
What can enable
self service?
What would be Useful on mobile
What would be a case for predictive
What data would be handy In Memory?
What data would be handy in Real Time?
What data would be smart to
analyze but is to large?
Do I need all this data on premise?
14
Start taking small steps:
Create an one KPI dashboard on mobile using BI on demand
15
Start taking small steps:
Provide that self service BI insight with
Lumira on a Hana data set in the cloud
16
Start taking small steps:
Gather big data insight with your Google or Amazon big date
cloud environment
17
Start taking small steps:
Retrieve real time data into a HANA environment in the
cloud and start analyzing, monitoring and reporting
18
Finally:
It is about catching the right technology wave, mobile, cloud,
big data, self service BI, but that only works if you try…