A Day in the Life of an Analyst

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Agenda

Introduction• Myself• Data Science vs. Business Analytics• The typical analytics problems

Analytics project

Lifecycle

• Business Problem Framing• Analytics Problem Framing• Data• Methodology Selection & Model Building• Deployment & Model Life Cycle Management

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MyselfShubham Nath

Senior Specialist – AnalyticsShared Business ServicesMSD International GmBH

(Singapore Branch)

• EngineeringB.Tech. – Electronics & CommunicationIndraprastha University – Delhi

• ManagementMBA – Industrial & Management EngineeringIndian Institute of Technology - Kanpur

7+ years in Banking, IT & Pharma Travelling & Cooking…

The world is a book, and those who do not travel read only one page - Anonymous

Ordinary folk prefer familiar taste – they’d eat the same things all the time – but a gourmet would sample a fried park bench just to know how it tastes - Walter Moers

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Data Sciences vs. Business Analytics

Am I a data scientist?

Data Science:The journey from inquiry to insights

Decision Science:The journey from insights to impact

Business Analytics

Source: BADIR- structured approach from “data to decisions”

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There is a wide variety of purposes for which analytics can be applied

Signalling Profiling Clustering

Segmentation Prediction Optimization

No matter what may be the purpose of analytics, a typical analytics project is an amalgamation of several steps.

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Business Problem Framing

Analytics Problem Framing

Data Methodology Selection

Model Building Deployment

Model Life Cycle

Management

Series1 15% 18% 22% 15% 15% 10% 5%

Time Allocation

More than half the time is spent in framing the problem and acquiring the data…

Iterative Process

Source: INFORMS Candidate Handbook

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Business Problem FramingThe ability to understand a business problem and determine whether the problem is amenable to an analytics solution…

• Obtain or receive problem statement and usability requirements

• Refine the problem statement and delineate constraints

• Obtain stakeholder agreement on the problem statement

• Determine whether the problem is amenable to an analytics solution

• Define an initial set of business benefits

The client may tell a long story without touching upon his pain-points.

It may take data to unearth the problem statement.

The scope of projects evolve as the data starts unfolding trends.

Business often confuses Analytics for IT/ reporting/ automation

Analytics may be an “over-kill”

The apparent business benefit may not always be quantifiable

Perceived value when the project begins often changes by the end of it.

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Analytics Problem FramingThe ability to reformulate a business problem into an analytics problem with a potential analytics solution…

• Reformulate problem statement as an analytics problem

• State the set of assumptions related to the problem

• Develop a proposed set of drivers and relationships to outputs

• Define key metrics of success

Assumptions are a very important ingredient of your analysis, its hard to have any analytics solution without one

• Obtain stakeholder’s agreement

Drivers of the solution are often difficult to recognize without knowing about what data is available.

The metrics of success may have a dependency upon quality of data points

Stakeholder’s are often busy business leaders, who treat analytics as a “nice to have” service.

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DataThe ability to work effectively with data to help identify potential relationships that will lead to refinement of the business and analytics problem…

Data often sits in various pockets across the organization, and may be governed by processes.

Data is “power” – giving it away is often perceived as losing control.

• Identify & Prioritize data needs and sources

• Acquire data

• Harmonize, rescale, clean and share data

• Identify relationships in the data

Most pain-staking step. More effort at this stage,

helps build confidence in the end result

Playing with the data should give analyst a view of the possibilities - “moment of truth”

• Document and Report findings – insights, results, business performance etc.

• Refine the business and analytics problem statement

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Methodology (Approach) Selection & Model BuildingThe ability to identify and select potential approaches for solving the

business problem and to build effective model structures to help solve the business problem…

• Identify available problem solving approaches

• Select Software Tools• Test & Select approaches

Simple tools & simple approaches are often the client favourites.

You also have to sell your approach/model to the end client – keep your methodology transparent.

Data Triangulation

• Identify model structures.• Run, evaluate & integrate

the models• Perform business validation

of the model

• Document & Communicate findings – including assumptions, limitations & constraints

What should the first slide of your analysis presentation be?

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Deployment & Model Life Cycle ManagementThe ability to deploy the selected model to help solve the business problem

and to manage the model life cycle to evaluate business benefit of the model over time…

• Deliver report with findings• Create & Deliver model, usability,

and system requirements for production

• Support deployment

Doing the analysis is not an analyst’s only job…

Driving adoption can be a key driver of success.

• Document initial structure• Track model quality• Support training activities• Evaluate the business benefit of

the model over time

Analytics & Analysis evolves…

Delivering value is the bottom line..

The bottom line is “what value did analytics deliver”?