Agile Analytics

January 2020

PwC

1. What is Agile Analytics 03

2. Model Development Lifecycle 05

3. Planning Analytics Projects 08

4. Organising for Agility 10

5. Summary 12

Agenda

2

January 2020Agile Analytics

PwC

Agile

adjective

1. able to move quickly and easily.

2. relating to or denoting a method of project management, used especially for software development,

that is characterized by the division of tasks into short phases of work and frequent reassessment and

adaptation of plans.

We are going to focus on Definition 1

Source Oxford English Dictionary

Agile is many things to many people

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January 2020Agile Analytics

PwC

So how do we make Analytics quick and easy

Quick

Traditional analytics has depended on a standard model development lifecycle. This is underpinned by:

• Data scientists developing models

• Focus on developing the “Best” model (highest goodness of fit)

• Each model is designed and built to highest standard, deployed and then retrained (usually after 12 –

18 months)

Easy

Usually modelling is a specialist role. Requiring many different complex skills

• Data & feature engineering

• Extensive coding experience

• Vast knowledge of different modelling techniques

Agile Analytics January 2020

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What if all this is wrong?

PwC

Model Development LifecycleExample – Crisp DM

Agile Analytics January 2020

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Source DataScienceCentral.com

Traditional Approach

Typically MDL takes at least 9-12 weeks

Most of the time is spent

• Data Engineering

• Feature Engineering

• Model coding

• Packing data prep and model code

• Testing code package

Build once – Deploy once – retrain in a year

PwC

Automated Modelling tools – Rapid Modelling

Automated Modelling Tools

Automated tools are not new – many have been around nearly a decade

They build models and engineer features with little/no user intervention

As computing power becomes cheaper their abilities have increased

Models are NOT as good as a trained data scientist

But they are quick and their output code is bug free (less test time)

Time to Decision is now more important than accuracy for many, but

not all applications

Used correctly Automated tools are:

• Faster

• Cheaper

• More accurate

• Support the Citizen Data Scientist

Agile Analytics January 2020

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Source: kdnuggets.com

PwC

Agile Analytics January 2020

7

Traditional Approach

Accuracy Effort

Jan 0.00% 20

Feb 100.00% 0

Mar 90.00% 0

Apr 81.00% 0

May 72.90% 0

Jun 65.61% 0

Jul 59.05% 0

Aug 53.14% 0

Sep 47.83% 0

Oct 43.05% 0

Nov 38.74% 0

Dec 34.87% 0

Rapid Approach

Accuracy Effort

80% 5

72% 0

65% 0

80% 5

72% 0

65% 0

80% 5

72% 0

65% 0

80% 5

72% 0

65% 0

Automated Modelling Tools Vs Traditional Modelling

Average 57.18% 72.27%

PwC

January 2020

8

How do we USE

Predictive AnalyticsDATA

ANALYSE

RESULTS DECISION

ACTION

EVALUATE

OUTCOMES

A more agile approach to modelling

PwC

January 2020

9

How do we PLAN

the use of Predictive

Analytics

DATA

ANALYSE

RESULTS DECISION

ACTION

EVALUATE

OUTCOMES

A more agile approach to modelling

PwC

September 2018

10

4 main Analytical Organisational Models

1. Fractional• In this model no central analytics function exists, each function

performs its own analysis

• Information is difficult to reconcile and often varies hugely

2. Centralised• One central unit responsible for all Analytics, requests are submitted from the

business and prioritised accordingly

3. Hybrid• A more mature version of the centralised model,

• Business users are brought in as part of the project team

• Projects are selected based on business case

4. Community• Most advanced of the models

• CoE supports departmental or SME led Analytics functions

• CoE acts as an internal consulting function, sets standards, manages vendors, sets strategy

• Ensures high standards are used throughout the organisation

If we are working differently do we need to organise differently?

PwC

September 2018

11

Fractional Centralised Hybrid Community

Structure: Fractured, each business unit has its own analytical capability, no co-ordination or standards.

Culture: Information is power, not shared, no collaboration, focus on me.

Standards: Generally only adherence to standards is due to regulatory requirements, and then only the most important ones

Information Processes: Not defined, no centralised control or repositories

People: Analytics is not a career, rather something that a few people do as part of other responsibilities

Infrastructure: Mainly PC based, high reliance on desktop tools, particularly excel. Any sharing of data or information is via email

Structure: One central function for analytics. Requests are submitted and results sent back.

Culture: Even with central function there is little collaboration, each department focuses on its area. CoE is a service centre

Standards: Adhere to all required regulations, but do not view standards as a positive

Information Processes: Sharepointpredominant, each function has its own site, no inventory is maintained

People: Analytics is a career, but only in the CoE. Specialist technical skills are hired for this specialist role, little requirement for business acumen

Infrastructure: Server based, but data is still relatively ad-hoc, little metadata or semantics

Structure: Central function, but with strong business relationships. Business SMEs join teams for projects and rotate back out

Culture: Collaboration is at the core of everything. CoE works closely with the business and understands business processes and constraints

Standards: Both regulatory and industry best practice standards used.

Information Processes: Information portals are common, some self-service reporting for simple analysis

People: Some lines of business consider data and analytical skills as part of the recruitment process, but it is still a “nice to have” skill

Infrastructure: Server and cloud based, usually a data warehouse in place, strong metadata management

Structure: CoE enables citizen data scientists in business units. CoE works as an internal consulting group

Culture: Innovation is everywhere, “fail fast” approach is typical. Information is an asset and is valued

Standards: Standards are regarded as good practice. CoE sets standards and best practice for the analytics in the organisation

Information Processes: Subject centric, searchable, commentary enabled information portals abound

People: Analytics is a career, with defined structures both in the CoE and the business. Managers are expected to have a good understanding of analytics.

Infrastructure: Mobile devices are typical. Data lake with strong, searchable semantics and lineage

Maturity LevelLow High

Analytical Maturity

PwC

Agile Analytics January 2020

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Summary

To achieve Agile Analytics requires more than just a project delivery methodology

1. Right tools – automated tools to reduce Time to decision.

2. Rapid development approach – less initial accuracy, better results by retaining more often

3. Plan the project with a focus on business actions

4. Organise to support Fast decisions

pwc.com

Thank youMartin.z.Duffy@PwC.com

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