Oracle Analytics Introduction...Machine Learning in Oracle Analytics Cloud The most advanced piece...

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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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Story Telling

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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: Write Back

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Essbase: Szenario Simulation

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Summary

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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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Zum Nachlesen …

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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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