Oracle Analytics Introduction...Machine Learning in Oracle Analytics Cloud The most advanced piece...
Transcript of Oracle Analytics Introduction...Machine Learning in Oracle Analytics Cloud The most advanced piece...
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Self-Service AnalyticsEndlich kann der Fachanwender selbständig analysieren
Oliver RönigerBusiness Analytics & Big Data Sales Manager Germany
DOAG, Nürnberg, 22. November 2018
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Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
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Agenda
Self-Service Analytics: Einordnung ins Analytics Spektrum
Typischer Arbeitsablauf
Erweitertes Funktionsspektrum
Summary
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Self-Service Analytics
• Das Ziel ist es, den fachlichen Anwender zu befähigen, selbständig mit den Daten zu arbeiten - ohne Datenmodell- und Programmier-kenntnisse.
• Die IT wird entlastet, weil sie nicht bei jedem neuen Informations-bedarf involviert werden muss.
• Aus Sicht der Governance besteht Gefahr! Bereinigte, abgestimmte Zentraldaten müssen als Basis bleiben und dürfen nicht verwässert werden.
• Heute wird oft Excel genutzt, um zeitnah Ergebnisse zu bekommen.
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BI Trends aus dem weltweit größten Survey der BARC
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Quelle: BARC Trendmonitor 2019, S. 13
Top 3 (wie schon im Vorjahr)
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Enterprise Analytics Architektur der Erste Bank
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Quelle: Reiling, R. (2018): Big Data, Data Science, DWH & BI, does all that have a future in a Bank, Vortrag auf der DOAG Oracle Data Analytics Konferenz, Brühl, 19.3.2018, https://analytics.doag.org/de/data-analytics-2018/
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Business Intelligence: Nutzergruppen (2008)
Entscheider
Analysten
Konsumenten95%
4%
1%
▪ Freie Recherchen und Analysen▪ Erstellung Dashboards und Standardberichte
▪ Standardberichte
▪ Management Dashboard▪ Standardberichte1-2%
3-5%
95%
IT-Administratoren und Entwickler
Typische Verteilung
▪ Datenmodellierung▪ Metadatenverwaltung▪ Berichtsstandards und -templates
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Typische Oracle Analytics Architektur (2008)
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Business Analytics: Nutzergruppen (2018)
Entscheider
Analysten
Konsumenten
▪ Freie Recherchen und Analysen▪ Erstellung Dashboards und Standardberichte
▪ Standardberichte
▪ Management Dashboard▪ Standardberichte
IT-Administratoren und Entwickler▪ Datenmodellierung▪ Metadatenverwaltung▪ Berichtsstandards und -templates
+ Business Power User
= SELF-SERVICE ANALYTICS
+ Data Scientists
= Machine Learning(alle Daten, auch Big Data)
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Oracle Analytics (2018): Cloud oder Onpremises
Interactive Dashboards
Published Reporting
MobileConsumption
Information Delivery
Common Enterprise Information Model
EnterpriseData
Ad-hoc Reporting
Physical Logical Presentation
Connections Dimensions
Mappings Calculations Security
Categorization
Scheduling & Alerting
Catalog & Search
Personal / DepartmentalData
Data Mashup
KeywordQueries
MobileAuthoring
VisualExploration
Data Visualization
Visualizations Maps
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Advantages: Business Freedom and Data Governance
• Centralized enterprise metrics repository
• Provide the business with a consistent view of the truth
• Balance governance and user autonomy
• Combine all relevant information from any data source, managed and self-service
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Territory
Sales
Revenue
Inventory
TimeProduct
ENTERPRISE MODELED
MY SUBJECT AREATerritoryQuarterProductTarget…
PERSONAL
NOT MODELEDOracle
Analytics
PERFECT COMBINATION
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Complete Analytics Platform for (all) Data Sources
HCM Cloud
Marketing Cloud
Planning
Sales Cloud
Local files
Oracle Analytics Cloud
Enterprise Data
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Visualisierung wählen,
erste Analysen
Datenquellen anbinden, eigene
Datenanpassungen(Data Wrangling)
Spielen mit den Daten, vertiefende
Analyseideen
Story Telling: Präsentieren der
neuen Erkenntnisse in einer Geschichte
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Verbinden der Datensets,
definieren von Data Flows
Datenquellen hinzufügen
(Data Mashup)
Typischer Arbeitsablauf mit Oracle Data Visualization
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Visualisierung und erste Analysen
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Visualisierungen (Auswahl)
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Mashup: Verbinden von zwei Datenquellen
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Data Preparation und Enrichment (“ETL light”)
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Freie Suche, auch via Sprache / Mobile
BI Ask
Day by Day
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Erweitertes Funktionsspektrum
• Automated Data Diagnostics „Explain“
• Natural Language Generation (NLG): Roadmap
• Avanced Analytics und Machine Learning
• Essbase: Write Back / Simulation & What if
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Automated Data Diagnostics – “Explain”
Basic Facts
•What are the values and how do they relate to each other?
Key Drivers
•What elements in this data best explain the values of an attribute?
Anomalies
•What groups in the data exhibit unexpected results for an attribute? Discover statistical anomalies in the dataset that merit further investigation.
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Segments
•What hidden groups in the data can predict outcomesfor an attribute? Learn which segments and clusters of data have highest predictive significance.
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Natural Language Generation (Roadmap)
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Advanced Analytics
Visualize Predictive Factors
Drag & Drop for functions:
• Clustering
•Outlier detection
• Singe-click trending and forecasting
• Enable easy incorporation of template-driven statistical, predictive and textual (feature extraction) models which can then be used for scoring with data sets
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Machine Learning in Oracle Analytics Cloud The most advanced piece of our ML is the use of self-learning algorithms in the discovery phase. As new analysts join a new function or as new data is incorporated into a system, there is tremendous learning that must happen to familiarize people with the insights hidden in the data.
Machine Learning
• Train sophisticated machine learning models directly within any data flow
• Numeric Prediction
• Multi-Classifier
• Clustering
• Binary Classifier
• Custom Model
• Easily Apply trained models on production data in data flows
• Leverage best practice model configuration parameters
• Easily visualize quality metrics for each trained model
• Apply trained models directly on DV data in the context of canvas
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Essbase
Scenario calculation and Simulation
• Allow users to “have a conversation with their data”
• Find answers for “what happened” and “what if”
• Personal to enterprise business modeling
•Wide-range of business modeling and management reporting applications
•Users can produce compelling models visually with little to no training
• Test data without impacting anyone else
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Aufbau Essbase Cube aus Dataset (easy)
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Essbase: Szenario Simulation
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Take away messages
• Fachanwender fordern Self-Service Analytics Funktionalität. Die IT kann es nicht verhindern, sondern sollte diese unterstützen.
• Es dürfen keine neuen Inseln entstehen. Self-Service Analytics muss „Enterprise ready“ umgesetzt werden, unter Einbindung der bestehenden Konzepte (DWH, Benutzerrechte, Metadaten, etc).
• Self-Service Analytics geht mit dem „erweiteren Funktionsspektrum“ weit über triviale Analyseaussagen und Visualisierung hinaus. Es liefert neue Erkenntnisse von hohem fachlichen Wert.
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Zusätzliche Informationen (1)
Für den Einstieg eignet sich die folgende youtube Playlist:
• Oracle DV Workshops
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Zusätzliche Informationen (2)
• Wir haben bei youtube den „Oracle Analytics youtube Channel“ eingerichtet.
• Sie finden dort eine Fülle an Videos rund um die verschiedenen Komponenten der Analytics Cloud und Data Visualization und Data Visualization Desktop, u.a.:
• Oracle DV: Highlighted New Features in Oracle Analytics Cloud 18.3.3
• Oracle DV: Introduction to Attribute Explain
• Oracle DV: Training a Numeric Prediction Model : Overview
• Oracle Mobile Analytics: A demonstration of Day by Day
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Zusätzliche Informationen (3)
Data Visualization Desktop• Sie haben die Möglichkeit, die Funktionalitäten mit der Desktop Variante
von Data Visualization auszuprobieren. Sie können für Testzwecke die Desktop Version herunterladen und für 30 Tage kostenfrei nutzen.
• Offizielle Dokumentation
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