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Building Data-Driven Organisations
with clear, focused analytics goals
Megan Yates Chief Scientist, Ixio Analytics
November 2016
“Startups in the Fintech space took on almost 30 per cent of the total funding
raised by African tech businesses in 2015"
“Telcos in Ghana have offered to assist FSPs to identify, manage and mitigate credit risk using
borrowers’ consumption pattern of telecom services"
5
How analytically mature is your organisation?
Becoming a truly data-led organisation with competitive data-driven activities is a 3-5 year journey
Analytically Impaired
Localized Analytics
Analytical Aspirations
Analytical Company
Analytical Competitor
Reactive operational and compliance reporting
Analyses of trends and benchmarks; customizable self service dashboards
Statistical analyses to solve business problems. Centralized staffing and integrated data
Predictive models i n t e g r a t e d w i t h business systems A g i l e ‘ Te s t a n d Learn’ campaigning
Data housed in c loud. Mul t ip le external data sets. Pervas ive da ta driven decision making and results
Most banks are here
6
“Without data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”
Geoffrey Moore
CustomerJoinDate_Years
1
≤ 12 > 12
CustomerJoinDate_Years
2
≤ 3 > 3
Language
3
≤ 3 > 3
Node 4 (n = 94)
1350
0.20.40.60.81
CustomerJoinDate_Years
5
≤ 2 > 2
Language
6
≤ 5 > 5
Node 7 (n = 49)
1350
0.20.40.60.81
Node 8 (n = 7)
1350
0.20.40.60.81
Product
9
≤ 2 > 2
Language
10
≤ 5 > 5
Node 11 (n = 8)
1350
0.20.40.60.81
Node 12 (n = 2)
1350
0.20.40.60.81
Node 13 (n = 39)
1350
0.20.40.60.81
Language
14
≤ 1 > 1
CustomerJoinDate_Years
15
≤ 5 > 5
callerpredictions
16
≤ 0.525 > 0.525
Node 17 (n = 13)
1350
0.20.40.60.81
Node 18 (n = 9)
1350
0.20.40.60.81
Node 19 (n = 58)
1350
0.20.40.60.81
Node 20 (n = 426)
1350
0.20.40.60.81
CustomerJoinDate_Years
21
≤ 22 > 22
CustomerJoinDate_Years
22
≤ 14 > 14
callerpredictions
23
≤ 0.347 > 0.347
Node 24 (n = 4)
1350
0.20.40.60.81
Node 25 (n = 29)
1350
0.20.40.60.81
Node 26 (n = 89)
1350
0.20.40.60.81
XIncome1K
27
≤ 18 > 18
Node 28 (n = 22)
1350
0.20.40.60.81
CustomerJoinDate_Years
29
≤ 25 > 25
callerpredictions
30
≤ 0.728 > 0.728
Node 31 (n = 13)
1350
0.20.40.60.81
Node 32 (n = 7)
1350
0.20.40.60.81
Node 33 (n = 31)
1350
0.20.40.60.81
94% accurate
Customer Join Date Call Prediction (likelihood to call in) Income Product Language
CustomerJoinDate_Years
1
≤ 12 > 12
CustomerJoinDate_Years
2
≤ 3 > 3
Language
3
≤ 3 > 3
Node 4 (n = 94)
1 2 3 4 50
0.20.40.60.81
CustomerJoinDate_Years
5
≤ 2 > 2
Language
6
≤ 5 > 5
Node 7 (n = 49)
1 2 3 4 50
0.20.40.60.81
Node 8 (n = 7)
1 2 3 4 50
0.20.40.60.81
Node 9 (n = 49)
1 2 3 4 50
0.20.40.60.81
Node 10 (n = 506)
1 2 3 4 50
0.20.40.60.81
CustomerJoinDate_Years
11
≤ 22 > 22
Node 12 (n = 122)
1 2 3 4 50
0.20.40.60.81
CustomerJoinDate_Years
13
≤ 25 > 25
Node 14 (n = 34)
1 2 3 4 50
0.20.40.60.81
Node 15 (n = 39)
1 2 3 4 50
0.20.40.60.81
Customer Join Date Language
87% accurate
9
Ghana’s Banking Industry
10
29Banks
912ATMs
1,173Branches
14Domestically
controlled
Interactive display of Bank performance
0 10 20 30 40
20142015
Mobile Money Transactions(GHc billion)
11.2
35.4
A highly competitive banking sector…
Automated teller machines (ATMs) per 100,000 adults
11
29Banks
912ATMs
1,173Branches
14Domestically
controlled
Interactive display of Bank performance
0 10 20 30 40
20142015
Mobile Money Transactions(GHc billion)
11.2
35.4
…with some signs of stress
Bank nonperforming loans (NPLs) to total gross loans
Ghana’s Banking Industry
A Challenging Outlook
12
Customers are much more demanding even as the economy slows and NPLs increase
0
1
3
4
5
March 2015March 2016
Non performing loans - NPLs(GHc billion)
11.2
35.4
-100
10203040
March 2015March 2016
% Growth in Income Before Tax
32%
-1%
Deteriorating credit quality
31.8Declining profitability
Customers want More
CHOICERelevance
Value
FasterCHEAPER
Mobile
Fidelity Bank Ghana’s maturity in terms of data and information, estimated based on current knowledge
* Graph source: http://www.ibmbigdatahub.com/blog/maturity-model-big-data-and-analytics
The Data Function would develop and leverage data & analytical expertise across all business units to increase ana l y t i c s ma tu r i t y and competitive advantage
Analytics MaturityMost organisations sense and react
Complexity increases within monolithic, vertically integrated mainframe systems leading to…Infrastructure
Journey
… increasing sprawl, higher costs and frequent outages
- Apps proliferate. APIs provide the primary access in and external to the bank
- All relevant data is stored. Machine learning and modelling drive decision making
Projects routinely require changes. Unfortunately many projects provide patches in an unplanned mannerApplication &
Data Journey
Project delivery takes precedence with patches overwhelming the integrity of core assets. Projects become far too slow and far too complex
- Project delivery focuses on fast, agile, continuous customer experience improvements
- Complexity and cost are reduced. Delivery is simplified
1970 - 2000 Post 2000: The Emerging State
For most banks, the journey looks like this…
… and complex infrastructure means reactions are typically slow
Beginning with the End in MindDefining goals…
Goal is for Fidelity Bank to be a proactive, data-driven organisation where:
Data is trusted and accessible
Data and data-derived insights facilitate better decisions
Excellent customer outcomes are achieved through smart, data-driven customer insights and recommendations across all touch points
The Journey Towards a Strong, Capable Data and Information Function
A High Level Viewprovides focus
The Basics The Focus The Opportunity
Scale - we cannot scale without technology
Keeping the books straight
Customer Service
Staying out of trouble• System availability • Cyber security • Regulatory reporting • Maintenance
Technology as a key enabler
Efficiency - Cost optimisation
Automation - workflow
Channel migration• Internet banking • Mobile app(s) • E-wallets
Incremental improvements
Value Creation
Customer Centricity
10x not 10%
Leverage the true power of data & technology
Organisation StructureData work is a crucial part of all units
Personal
Commercial
Wholesale
Financial Inclusion
Data Analytics
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Air Traffic Control
Who ?When? What?
Where?
Digital Migration
Maximise channel
productivity
Propensity Modelling
Predict Customer Behaviour
Lifetime Value
Follow targeted value migration
paths
Segment
How do customers really
behave?
Without a comprehensive analytics program, it is very difficult for banks to win in this market
Internal TalentFSPs have the talent
Individuals within
your organisation
know and
understand the
business and it’s
challenges
Personality Traits
Skills
Data Engineer Market Research Analyst
Data Analyst
Lt = alpha * (Yt - S
t-s) +
(1-alpha)*(Lt-1
+ bt-1)
Problem Solving
Curious
Statistics
M a t h e m a t i c sPredictive Modelling
Mathematics
Research Design Business Strategy
Statistics
Confident
Market Intelligence
Machine Learning
Enjoy Learning
Good communicators
PerseveranceInquisitivePassionate about data
Pragmatic
Passionate about dataPassionate about data
Data Warehouse Design, Set Up and Maintenance
ETL Integration
Data Wrangling
Data Wrangling
Data Wrangling
Theoretical thinkersProactiveAttention to detail
Analysis
Problem Solving
Problem Solving
Forward looking
Skeptical
Data Collation
Loves tech & coding
Methodical Practical
• Communicate Ongoing communication of Vision, Objectives, Progress and Successes
• Change management Ongoing support for business directors, heads and colleagues in setting up and running the Data Function
• Review work Review current data & reporting landscape - from business stakeholders as well as data resources
• Review skills Review skills of current resources involved in data-related work
• Formal training Intensive classroom training, followed by ongoing tutorials
• Skill development Continuous on-the job exposure to Technical Skills + Commercial Awareness + Empathetic Business Partnering
• Prove the case Deploy small teams to tackle and solve 1-5 specific use cases with significant business value
• Run bank Actively support and drive all analytics projects
• Data Function Team Resources formally transition into Data Function Team
• Data Architecture, Engineering & Governance
Physical and logical organisation of data, supported by tightly governed and responsive processes
A RoadmapPlanning the organisation’s evolution
Cost
Flexibility
User Friendliness
Integration
Future Value
Tech ConsiderationsBuilding for the future
A z u r e D a t a W a r e h o u s eR Python
Microsoft Power BI
Towards Analytics MaturityData-driven use cases
1- Customer Segmentation and Customer Behavioural Analytics - building a fuller picture of customer interactions with Fidelity. This includes bio details, products purchased, types of transactions effected, channels utilised.
2- Customer-led product propensity models to improve go-to market economics and optimise customer value/ share of wallet.
3- Predictive Analysis of Non-performing Loans to identify customers likely to default on loans and take preventative measures to avoid default
4- Customer Dormancy to understand potential triggers and patterns that lead to dormancy and predict accounts that are likely to go dormant
5- Product life cycle modelling to determine current and future expected performance of the major Fidelity banking products
6- Campaign analytics - setting up test and learn framework
Building for the Futureaggregating data from disparate sources enhances our understanding
Internal Bank Data - products - transactions - channels - behaviour
Campaign Response DataCall Data Records
Online Data - how, when, where - behaviour
A l e r t R e s p o n s e D a t a
Social Data
V i s i t s
Building for the Future
Social media monitoring acts as an
early warning system
aggregating data from disparate sources enhances our understanding
Spatial Data Journeyusing spatial data to serve customers better
Reporting by Regions - branches grouped by region
Mapping of branches
Mapping of ATMs
Mapping of Agents
Visits and Transactions
Sourcing of external spatial data sources (e.g. census, CDR)
Cur
rent
Next
Spatial Data Journeyusing spatial data to serve customers better
Goals
Insights
Prediction
1 assess customer access to physical banking facilities (branches, ATMs, agents) 2 assess customer types (in terms of product usage, transactional patterns, channels) by geography 3 use spatial data and assessment of customer types to ensure the bank is meeting customer needs 4 optimise banking footprint (in terms of operational efficiency and customer needs) 5 assess customer communication/campaign success by geography - what works where, for whom and why? 6 monitor penetration and service performance in space and time
7 predict success of potential new branch/agent locations by modelling GIS data and branch performance data
Be Curious
Always ask ‘Why?’
Challenge the status quo
Don’t accept “this is the way we’ve always done it” - ask “how can we do it better”
DATA CULTURE
Source: Euromonitor International
Nigeria
CIV
LBR
TGO
United States
FRA DEUGBR
CHN
ZAF
IND
Ghana
Wes
tern
Afri
ca
Northern AmericaWestern Europe Northern Europe
W. AsiaE. Asia
S. Afr.S. Eur. S. Asia
E. Afr.
Arrivals and departures data, 2014Ghana and all travel contributors.Ixio Analytics
31
“The price of light is less than the cost of darkness”
Arthur C. Nielsen