Challenges of Analytics

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1 Challenges of Analytics Setting-up a Data Science Team BA4ALL Eindhoven November 2015 Laurent FAYET 1

2 Agenda 1 About ARTYCS 2 Definitions 3 Data Value Creation 4 An Approach to Implementation 5 Challenges of Implementation 6 How it Works 2

3 1. About ARTYCS 3

4 About Us We are experts in advanced analytics. Because we know the potential of DATA, we help you extracting the full value of this key asset. We focus on client needs and deliver, in partnership with them, pragmatic and concrete answers to their questions. We transform RAW DATA into ACTIONABLE INSIGHTS 4

5 2. Definitions 5

6 Definition of Data Science Data Science Data Science is an interdisciplinary field about processes and systems to extract knowledge or insights from large volumes of data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining and predictive analytics. (Source: Wikipedia) 6

7 Definition of Big Data Analytics Big Data Analytics Big data analytics is the process of examining large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits. (Source: WhatIS) 7

8 Big Data Analytics 8

9 3. Data Value Creation 9

10 Data Value Creation DATA is an ASSET Do you know the value of this asset? 10

11 BDA for What Purpose Enhance Customer Experience Generate New Revenues Increase Operational Efficiency Improve Risk Management Data Value Creation 11

12 Enhance customer experience Increase service level Identify patterns among clients 360 view of clients Anticipate clients moves with predictive analytics Improve customer understanding through behavioral analytics Improve client retention 12

13 Generate new revenues 13

14 Minimize unproductive Back Office activities Improve operational efficiency Manage data to speed up processes Integrate business knowledge from the data to automate analysis Automate manual processes Automate paper based workflow to maximize efficiency and quality Apply Machine Learning to automate labour intensive processes Supply Chain Optimisation Include clients and suppliers data into supply chain workflow Anticipate failures using predictive analytics on production data 14

15 Control risks Rational Decision Making Assess consequences and impact of decisions through simulation models Identify and predict fraudulent activities and network Decide based on statistical models outcomes Detect Fraud RISKS Be Compliant Identify Threats Actively monitor your data Assess data veracity Align risk management with regulatory constraints Predict system malfunctioning or breakdown Identify security breaches and anticipate on future attacks 15

16 4. An Approach to Implementation 16

17 Gradual Approach 17

18 Sequencing of Implementation Pilot Experiment Implement Industrialise Senior Sponsorship Low Complexity Limited Investment Pure Cashout Learning phase Increasing complexity Variety of Projects Low to High ROI Competence centre Tactical IT Infrastructure Prioritisation process Focus on High ROI Embedded Processes Selection of Tools Strategic IT Infrastructure Production Mode Allow for Failure Change Management Data Driven Organisation Analytics Maturity Copyright ARTYCS All rights reserved

19 Analytics Maturity Model MATURITY ORGANISATIONAL Distinct role in the organisation Full- fletched technology solution Embedded in decision making process Data governance for analytics Collaborative analytics culture INDUSTRIAL Analytics processes in place Analytics life- cycle standardized Scalable sandbox technology Production infrastructure BENEFICIAL Pipeline of business use cases Analytics life- cycle in place Scalable sandbox technology Usability of analytics output EXPERIMENTAL Limited business use cases Trial and error Sandbox technology CONCEPTUAL Limited awareness Questioning on applicability 19 TIME

20 5. Challenges of Implementation 20

21 Key Learnings EXPERIMENTATION ANALYTICS MATURITY DATA MANAGEMENT COMMUNICATION AGILE ANALYTICS Copyright ARTYCS All rights reserved

22 6. How Data Science Works 22

23 Example of Organization ENGAGEMENT MANAGER: MIX OF PROJECT LEAD, MARKETEER, PUBLIC RELATION, PEOPLE MANAGER, STRATEGY DATA SCIENTISTS: MIX OF STATISTICS, BI, BUSINESS ANALYSIS, COMMUNICATION, CURIOSITY, VISUALISATION, COMMON SENSE ANALYTICS ARCHITECT: MIX OF BUSINESS ANALYSIS, PROJECT LEAD, COMMUNICATION, COMMON SENSE, ANALYTICS DATA ENGINEER: MIX OF DATA EXPERTISE, DATAWAREHOUSING, DATA QUALITY, DATA CLEANSING, DATA EXTRACTION, HADOOP, NOSQL GRAPHIC DESIGNER: MIX OF DESIGN, VISUAL ANALYTICS, INFOGRAPHICS, MARKETING, CREATIVITY Copyright ARTYCS All rights reserved

24 Data Scientist Skills Statistician BI Expertise Business Analyst Visualization Communication Curiosity Creativity Common Sense Copyright ARTYCS All rights reserved

25 Key Messages Start Small, Think Big Analytics maturity will determine the speed of implementation Communicate, teach, explain Experiment and innovate Importance of the right resource mix Agile development cycles Copyright ARTYCS All rights reserved

26 26

27 ThE ARt of AnALyTiCs 27

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