POWER OF INTELLIGENCE AGILE BUSINESS DECISION MAKING

Transcription

1 POWER OF INTELLIGENCE AGILE BUSINESS DECISION MAKING

2 Agenda Introducing Pieter Rambags Introducing Nippur Business Intelligence Traditional Active BI Big Data The extended enterprise revisited Conclusions

3 Pieter Rambags (1964)

4 Nippur s motto Nippur improves decision making capability of customers not incidentally but structurally

5 Services BI strategy services Business Intelligence services Data warehousing services Master data management services

6 Nippur Oirschot and Nippur Arnhem

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8 Business Intelligence more than only IT BI as process: BI is the continuous process allowing organizations to collect, register, analyze and apply information and knowledge in decision making to enhance the organization s performance BI as technology: BI is the collection of ICT-tools that supports and shapes BI as process in organizations BI as phenomenon or discipline: BI is the whole of concepts, processes, strategies, culture, structure, methods, standards and ICT-means that allow organizations to behave more intelligent and to develop themselves. Source: Daan van Beek, Business Intelligence: de intelligente organisatie, 2004

9 Business Intelligence from a business perspective

10 The BI information pyramid Strategic Dashboards and standard reports + Tactical Adhoc querying and analysis + Operational reporting Operational

11 Increase in (need of) Self Service BI Business models change overnight IT in many cases not capable to meet Business data and information demands Increase in data volume, speed and complexity Thus need for agile architectures providing data and information fast Ability of the business to analyse the data themselves Availability of Data Scientists Source: Marketing distillery

12 Business Intelligence from an architectural perspective Further aggregation & KPI calculation Data Warehouse Semantic integration

13 BI reference model DATA MODELS DBMS REPOSITORY ETL REPOSITORY KPI DEFINITIONS REPORT DEFINITIONS PROCESS MONITORING ACCESS AND USAGE METADATA SCHEDULING AND MONITORING PROCESS SCHEDULING AND MONITORING ACCESS & USAGE MONITORING SOURCES STAGING DATA WAREHOUSE DATAMARTS APPLICATIONS END USERS EXTRACT / LOAD EXTRACT / LOAD BUSINESS DATAVAULT BUSINESS RULES ETL AGGREGATION + FILTER PRESENTATION PORTAL SOURCE DATAVAULT Trends: - Architecture generation - Architecture virtualization

14 Business Intelligence trends toward active BI Level 2: Why did it happen? Standard reports as starting point for advanced analytics Focus on finding relations in data Level 1: What happened? Standard reports Focus on availability and reliability of data Level 4: What should I do? BI to support operational and tactical decisionmaking Focus on application of predictive business models Level 3: What will happen next? Analysis of cause and effect relationships Focus on building predictive business models Level 5: Automation BI supporting operation Focus on automatic signaling of relevant events

15 The future, Level 5: Automation Source: Stephen Brobst, Teradata

16 Big Data: Fact or fiction?

17 Big Data in perspective Creating Clarity 17

18 Big Data applications- technology

19 Big Data applications - people

20 Big Data applications - people

21 Big data own research sentiment analysis NPS measurement versus social media sentiment analysis Social media data collected from (!) web sources in Dutch matching of internal NPS data with social media data Conclusions: NPS shows higher satisfaction ratios Significant and relevant relationship between NPS measurement and data mining effort Data mining automation can replace NPS measurement at lower cost

22 Big data own research emotion analysis Crimewatch monitor of Algemeen Dagblad showing decrease in crime over the years However feeling of insecurity seems to increase Research Data mining of textual data on more advanced sentiments Feeling insecure Anger Frustration. Results expected september 2015

23 Big Brother!? Data privacy versus data usage: a paradigm shift

24 Integrated architecture NOSQL

25 The extended enterprise revisited

26 The extended enterprise revisited Business Communicati on model Model

27 The extended enterprise revisited

28 Information Hub: a business service to integrate and share information Information Services Master Data sharing People Organizations Products Customers Markets Territories Competitors Businesses Data Dictionary Operational process monitoring Business analytics Planning And budgetting Ad-hoc Data discovery Live Dashboards Governance, Processes, technology

29 Information Hub: supports DWH and MDM components Information Services Master Data sharing Internal Internal Internal transaction transaction transaction sources sources sources Data Integration Metadata Mgt. Data Dictionary Operational process monitoring Internal Internal External transaction transaction transaction sources sources sources Data Warehousing. Business analytics Internal Internal transaction transaction Documents sources sources Master Data Mgt. Data Quality Mgt. Planning And budgetting Ad-hoc Data discovery Live Dashboards MD Authoring and Data governance

30 Future Reference Architecture Information HUB

31 Conclusions Business Intelligence shifts from predicting the past to automating the future of decision making Architectures and technology support massive, high performance and near realtime data collection and decision making Solutions can be implemented at an ever faster pace There truly is a data explosion taking place Big Data analytics looks promising but requires out-of-the-box thinking Data privacy is an important topic to consider Sharing the power of knowledge could boost business productivity But are we ready and willing to share our knowledge and data?

32 Creating Clarity 32

33 NIPPUR Spoordonkseweg KB Oirschot E: info@nippur.nl W: T: I: NIPPUR BV All rights reserved. No part of this document may be reproduced without the written permission of Nippur.