building a data enabled business the actual application · building a data enabled business the...

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Building a Data Enabled Business

The Actual Application

Strictly Private and Confidential

6 December 2017

PwC

Content overview


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December 2017

2 How to set outcomes and objectives, and how can you measure the impact of using analytics?

1 What are the common challenges of implementing analytics and how to overcome them?

90% 4 Examples of how companies are using analytics

3 Getting the right talents, internally and externally, to manage and analyse your data

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Section 1

What are the common challenges of implementing analytics and how to overcome them?

The Actual Application December 2017

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• Lack of stakeholder alignment or support

• Lack of clear roadmap

• Lack of adequate funding

• Inability to articulate value at stake

Data and Analytics implementations fail for three primary reasons


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• Inability to integrate analytics solutions into work flows

• Inadequate investment in driving frontline adoption

• Lack of change management strategy and business buy-in

• Lack of enterprise data governance

• Missing or incomplete data

• Data quality or accuracy issues

• Data fragmentation

Lack of alignment with strategic goals and direction

Poor integration with ‘business as usual’ and limited frontline adoption

Poor data quality and accessibility

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Majority of organisations lack technology to support analytics


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17%

50%

17% 16%

0%

20%

40%

60%

80%

100%

Rudimentary, with only spreadsheets

and basic reporting tools

Basic reporting tools with limited predictive

analytics tools

Reporting and predictive tools widely

available

Reporting and predictive tools, plus

tools for analyzing unstructured data, with prescriptive

triggers/alerts

% o

f o

rg

an

isa

tio

ns

Maturity of Analytics Infrastructure

Source: PwC Analysis

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Dearth of advanced analytics skillset and talent exacerbates the problem


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Human capital has proven to be one of the biggest barriers standing in the way of realising the full potential of data and analytics

01 Data

Scientists

02 Data

Architects

03 Data

Engineers

04 Business

Translators

Analyse data with increasingly sophisticated techniques to develop insights

Design data systems and related processes

Scale data solutions and build products

Turn analytical insights into profit and loss impact

There are four broad types of roles to consider:

Source: US Bureau of Labor Statistics; Burning Glass

Expected number of trained data scientists would not be sufficient to meet demand in the US Numbers in thousands

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Analytics Maturity Curve

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DESCRIPTIVE What happened?

DIAGNOSTIC Why did it happen?

PREDICTIVE What will happen?

PRESCRIPTIVE What should be done?

Traditional static reports, often built up over time and under utilised.

Limited visualisation.

Limited insight for action.

Multiple, disparate datasets discoverable in one place.

Mix of standard dashboards and self service discovery.

High visualisation.

Insights to drive actions.

Getting the data into shape to allow good Diagnostic analytics opens the pathway for more advanced analytics as required.

1

2

3

4

BUSINESS INTELLIGENCE PROCESS INTELLIGENCE

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Becoming an insight enabled organisation requires a strong command of how to apply analytics

1 2 3 4 5

Spend time where it matters

Discovery Insights Actions Outcomes

Shorten the distance between

trusted insight and value

Anticipate and act Drive analytics adoption


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Becoming an insight enabled organisation requires a strong command of how to apply analytics, first in Discovery of relevant problems to solve, second in creation of Insights, third to enabling Actions to generate results, and fourth in measurement of Outcomes

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Step 1: Discovery




Drive analytics adoption


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Spending time where it matters – effective hypothesis generation for problems worth solving balanced with maturity to deliver

Discovery

Find value in internal data environments

1 (a) Hypotheses Generation

• Identify drivers of business value and business opportunities; build Hypotheses

• Sources of information: stakeholder workshops and interviews, data (policyholder, advisor, financial, attitudinal, customer experience), customer surveys, social media data, and past models

(b) Data & Analytics Maturity

• Conduct early assessment of data assets, and analytics maturity

• Tools and approaches: PwC’s Analytics Maturity Diagnostic will accelerate and benchmark your organisation’s data and analytics environment (e.g. data architecture, data governance, and determine readiness)

People

Process Technology

Culture

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Step 2: Insights






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Shortening the distance between trusted insight and value; focused prioritisation based on business value and analytical processes

Insights

Combine data science experts for tailored, value-creating insights

2 (a) Value Driven Prioritisation

• Identify highest impact areas (segments, products, advisors) for targeting balanced with complexity and ability to execute

• Tools and approaches: PwC’s decision-enabling frameworks will accelerate your organisation’s prioritisation of highest impact areas

(b) Data & Analytics Model Building

• Run analytics on customer or operational data to develop insights or actions for front-line

• Approaches: Build relevant models iteratively (Propensity, Survival, Lapse, Conversion, Revival, Agent-Allocation, Triage, etc.)

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Step 3: Actions






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Anticipate and Act – enable actions through the right technical solution at the point of action for leaders and employees

Actions

Make decisions, deliver analytics & data quick wins and operational

capabilities

3 (a) Enable Technical

Deployment

• Ensure data management and architecture is aligned to move insights from Analytics to the decision makers or front-line employees to take action to create a measurable result

• Tools and approaches: PwC’s enterprise and analytics architecture expertise and frameworks

(b) Develop Operating Model

• Recommend relevant operating model to maximize effectiveness of analytics and ensure alignment between analytics and business consumers

• Approaches: PwC’s analytics operating model frameworks will guide the design considerations in assessing the right model for scaling analytics

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Step 4: Outcomes






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Drive Analytics Adoption - create traction through piloting (lean, agile methods) with well-defined measurement of success factors

Outcomes

Unlock value, build talent and improve financial, market and risk

metrics

4 (a) Implementation of Pilots

• Implement a pilot framework based on lean and agile principles to innovate and move fast

• Tools and approaches: PwC’s Lean Lab and Agile expertise provides a blueprint for executing sprints and a “see one, do one, run one” transition of knowledge to enable your organisation to own the process sustainably

(b) Measurement of Impact

• Measurement of the benefit impact of investment, and creating a feedback loop to refine models and insights

• Tools: Monitor and deliver outcomes in effective ways to each stakeholder group – measurement KPIs defined and instrumented to collect data to show benefits

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PwC Enterprise Insights Technology


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PwC’s Enterprise Insights Technology offers executives insight into their business processes and systems activities by analyzing transactional and master data, security, and system configurations

https://www.youtube.com/watch?v=pqLP_LX7zRY



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Data Analytics for Internal Audit


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Profiling: Analyze transactional activity and develop a baseline understanding which can be used to identify outliers.

“Intelligent” sample selection: Identify specific anomalous, unusual, or higher-risk transactions for follow-up and investigation.

Exception testing: Identify transactions that specifically violate company policies or represent internal control breakdowns.

An

aly

tic

s

Profiling Sampling Testing

Monitoring: Operationalizing analytics created in prior phases and test risk areas in a sustainable and repeatable way.

Monitoring

Understand data completeness and quality Set standard benchmark to evaluate totals.

Policy changes requiring exceptions to speaking or consulting fee caps; Engagements with starts dates earlier than contract signature dates.

Test 100% of all transactions that exceed Company's policy limits or activity thresholds.

Create continuous monitoring solution around a specific use case providing access to Audit and Compliance as well as business stakeholders.

Ou

tco

me

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Major vendors within the Audit Analytics space


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Core Technological Components and Key Vendors

Analytics

Data Management Monitoring

Visualisation

Data Preparation

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Section 2

How to set outcomes and objectives, and how can you measure the impact of using analytics?


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One-step-at-a-time approach to analytics helps demonstrate credibility and tangible value


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Slow and steady wins the analytics race

Smart analytics leaders are overcoming skepticism and gaining executive advocates by:

1 Tackling small projects that yield impressive results

Showing tangible, incremental improvements financially or operationally

Demonstrating how analytics improves competitive positioning

2

3

Developing a culture of data-driven decision making at all levels 4

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Build analytics results into performance management for a successful implementation


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Operational measures, such as the number of outbound calls or enrollment rates, can track the operational impact of changes during use case implementation

Operational measures

Predictive measures, such as GINI index, can reveal a model’s accuracy and power Predictive measures

Feedback, such as voice of the customer measures and input from business stakeholders, can include qualitative and quantitative metrics to show use case implementation progress Feedback

Financial metrics, especially cost management and revenue growth, are important, but lagging indicators should not be the only measures of success

Financial Metrics

Leaders must embed analytics into their organizations’ DNA by making it an enterprise priority and managing to it. The most effective performance management systems cascade to all levels of the organization, with metrics tied directly to business value and indirectly to frontline adoption and other goals.

Many successful companies include at least four categories of metrics in their performance management systems:

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Most valued impact of analytics is on income production or cost reduction


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December 2017

“ We are really not spending money on data analytics. We are using it to find better alternatives for making money.

Database marketing executive for regional bank

“ Once you start spending money, the data becomes even more important.

The culture we’re growing is data hungry, and the best idea driven by

the best data wins.

Head of analytics for an insurance company

1 Increasing Sales

Identifying innovation opportunities

Forecasting financial performers

2

3

Understanding financial drivers 4

Analytics delivers by:

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Analytics is becoming an irreplaceable strategic weapon in the corporate arsenal


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December 2017

1%

6%

9%

9%

10%

15%

50%

Identification and creation of new product and service revenue streams

Better capability to respond to buying trends in the marketplace

Better financial performance of the organization

Better relationships with customers and business partners

Better sense of our risk and better ability to react to changes in the economic environment

Better enablement of key strategic initiatives

Better decision-making based on data

While the reasons for employing analytics capabilities are as varied as the companies and industries using them, respondents agree overwhelmingly on one key point - analytics will continue to grow in importance over the next three years.

Source: PwC Analysis

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Section 3

Getting the right talents, internally and externally, to manage and analyse your data


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Your people: The key to delivering analytics advantage


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December 2017

“ Compared to traditional reporting and

dashboarding, analytics causes a paradigm shift within organizations

that will require new behaviors. People will need to collaborate more,

new processes will need to be developed, and managers and

executives will need to trust the decision support that analytics will

provide them.

Schrage, 2014; Satell, 2014

Insights

• Organizations that try to win at analytics by hiring predominantly ‘red’ talent (data scientists, quants, technology architects) are doomed to failure

• It has now become eminently clear that ‘blue’ people (change managers, political navigators, subject matter experts) are required to promote a culture that embraces analytics insight to actively drive decision-making

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Successful analytics implementation requires a healthy blend of ‘red’ and ‘blue’ skillsets


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Red Team Blue Team

Technical and Analytical

• Testing and validation

• SQL Querying

• Data Modelling

• Data Analysis

• Reporting Software

Business and Communication

• Technology Alignment

• Macro-Perspective

• Business Knowledge

• Business Commentary

• Soft Skills

Design Thinker Information Designer

Subject Matter Expert

Change Manager Software Developer

Data Architect Technology Architect

Data Scientist

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Section 4

Examples of how companies are using analytics


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SunMoon Food CEO has data from over 11,000 POS at his fingertips


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December 2017

SunMoon, a global distributor of fruit and food products, has deployed NetSuite OneWorld to support its global growth, enabling dramatically increased overall productivity and efficiency.

In just five months, NetSuite OneWorld facilitated 900 transactions, having saved SunMoon 150 hours and an estimated S$20,000.

These global financial capabilities give SunMoon real-time organisation-wide visibility and new insights for its three subsidiaries via supplier, customer and other transaction data.

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Starbucks processes over 90 million transactions a week spread around 25,000 stores globally


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December 2017

Personalizing orders and experiences During last year’s annual shareholder meeting, CTO Gerri Martin-Flickinger emphasized the goal of personalizing the customer experience: “with our 90 million transactions a week we know a lot about what people are buying, where they’re buying, how they’re buying, and if we combine this information with other data, like weather, promotions, inventory, insights into local events, we can actually deliver better personalized service to other customers.”

New store locations Patrick O’Hagan, director of market planning, talked about how data drives decisions on where to open new stores: “Through a system called Atlas, Starbucks links to as many external and internal APIs as possible, connecting the data with R to build cannibalization models that can determine impact to existing stores if a new store enters the area.”

Targeted marketing Another application for Starbucks’s abundant data is targeted marketing. A recent mobile app update started targeting customers with discounts and rewards on certain items based on their purchase history. Additionally, Starbucks sends out emails to re-engage dormant customers. The content of those emails is targeted towards each customer, based on their purchase history as well.

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PayPal is a leader at leveraging data from its unique, closed-loop network of consumers/merchants


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By combining advanced data processing with human oversight, PayPal is able to help keep its customers’ money moving to the right people and out of the hands of the wrong ones. Its system gets smarter with every transaction they process, enabling PayPal to accurately establish patterns.

Helping prevent fraud

PayPal continually analyzes petabytes of data from its website and mobile app interactions to understand how its customers use products and identify where they can remove friction to enhance the experience for every financial transaction.

Providing a frictionless customer experience

By applying advanced analytics to big data, PayPal is able to present relevant offers from merchants to consumers – such as discounts when using PayPal as payment.

Delivering relevant offers and opportunities

PayPal uses proprietary data and insights to expand access to capital that small businesses need to grow and hire. Through the innovative use of data, PayPal Working Capital has disbursed a total of $1 billion to more than 60,000 small businesses around the world.

Offering access to credit

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How do I learn more?


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How PwC’s Enterprise Insights Platform can work for you?


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December 2017

PwC’s Enterprise Insights is a cross system analytics solution that helps companies assess risks within their enterprise system data.

With a reporting portal and the ability to workflow results, this content rich monitoring platform provides one solution to support a data-driven approach to manage business process risks and controls.

Enterprise Insights helps enable regulatory compliance with actionable responses to analytics results—giving executives precise insights into business risks.

Link to Enterprise Insights:

https://www.pwc.com/us/en/risk-assurance/enterprise-systems-risk-and-controls/enterprise-insights-technology.html
















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Coursera Specialisation: Data Analysis and Presentation Skills - The PwC Approach


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December 2017

This Specialisation will help you get practical with data analysis, turning business intelligence into real-world outcomes. We'll explore how a combination of better understanding, filtering, and application of data can help you solve problems faster - leading to smarter and more effective decision-making.

Link to the course: https://www.coursera.org/specializations/pwc-analytics

https://www.coursera.org/specializations/pwc-analytics



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This document is provided on the basis that PricewaterhouseCoopers Advisory Services Pte. Ltd. or PricewaterhouseCoopers Risk Services Pte. Ltd. accepts no liability—whether in contract, tort (including negligence), or otherwise—to ISCA or to any other person in respect of the document “The Actual Application”.

This document must not be made available or copied in whole or in part to any other person without our express written permission.

© 2017 PwC. All rights reserved. PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details.

December 2017 The Actual Application

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Contacts James Larmer Data & Analytics Leader, South East Asia +65 6236 3005 [email protected]

Mark Jansen Data & Analytics Leader, Singapore +65 6236 7388 [email protected]

http://www.pwc.com/structure

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