The BIg Picture

Dinsdag 17 september 2013

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A short historical overview on BI

Current Issues

Current trends

Future architecture

First steps to this architecture

Agenda

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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

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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

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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

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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?

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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

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Trends visualized

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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

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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

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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

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SAP filling in the gaps

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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?

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Start taking small steps:

Create an one KPI dashboard on mobile using BI on demand

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Start taking small steps:

Provide that self service BI insight with

Lumira on a Hana data set in the cloud

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Start taking small steps:

Gather big data insight with your Google or Amazon big date

cloud environment

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Start taking small steps:

Retrieve real time data into a HANA environment in the

cloud and start analyzing, monitoring and reporting

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Finally:

It is about catching the right technology wave, mobile, cloud,

big data, self service BI, but that only works if you try…