Building Data-Driven Organisationsaccess.i2ifacility.org/Community/Spatial_Data... · 2017-06-13 · Building Data-Driven Organisations with clear, focused analytics goals Megan Yates

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


2

≤ 3 > 3

Language

3

≤ 3 > 3

Node 4 (n = 94)

1350

0.20.40.60.81


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


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


21

≤ 22 > 22


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


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


1

≤ 12 > 12


2

≤ 3 > 3

Language

3

≤ 3 > 3

Node 4 (n = 94)

1 2 3 4 50

0.20.40.60.81


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


11

≤ 22 > 22

Node 12 (n = 122)

1 2 3 4 50

0.20.40.60.81


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

http://www.ibmbigdatahub.com/blog/maturity-model-big-data-and-analytics

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

18

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

Documents

Building Data-Driven Organisationsaccess.i2ifacility.org/Community/Spatial_Data... · 2017-06-13 · Building Data-Driven Organisations with clear, focused analytics goals Megan Yates