Why do you need a data and analytics framework?

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From the course by PwC

Data-driven Decision Making

2815 ratings

PwC

Data-driven Decision Making

2815 ratings

Course 1 of 5 in the Specialization Data Analysis and Presentation Skills: the PwC Approach

Welcome to Data-driven Decision Making. In this course you'll get an introduction to Data Analytics and its role in business decisions. You'll learn why data is important and how it has evolved. You'll be introduced to “Big Data” and how it is used. You'll also be introduced to a framework for conducting Data Analysis and what tools and techniques are commonly used. Finally, you'll have a chance to put your knowledge to work in a simulated business setting. This course was created by PricewaterhouseCoopers LLP with an address at 300 Madison Avenue, New York, New York, 10017.

From the lesson

Introduction to Data Analytics

In this module you'll learn the basics of data analytics and how businesses use to solve problems. You'll learn the value data analytics brings to business decision-making processes. We’ll introduce you to a framework for data analysis and tools used in data analytics. Finally, we’re going to talk about careers and roles in data analytics and data science.

Why do you need a data and analytics framework?3:34

The 4 aspects of the data and analytics framework3:41

Data and analytics framework: tools and techniques12:15

Meet the Instructors

Alex Mannella
Alumni / Former Principal

[MUSIC]

Welcome back.

I am Anand Rao and I lead a team of data scientists.

We're constantly looking for innovative techniques,

tools and technologies to apply to our client problems.

We work closely with academic institutions, start-up companies and

establish clients to bring innovative solutions.

In this video, we're going to look at the data and analytics framework.

I'm going to talk about the compliments of the framework and

explain why it's important to follow a framework when solving a business problem.

Let me give you an example.

A global insurance company came to us with the desire to incorporate

advanced data and analytics within their organization.

They're a number of business objectives that they wanted to achieve,

including improving retention and cross selling in one country and

increasing acquisition in another country.

They wanted to build an analytics organization,

while at the same time ensuring that they solve a business problem.

We went through our data and

analytics framework to first determine the outcomes in each country.

For example, as we said, greater cross-product holding in one country.

Then we listed all the actions or

decisions they can take to effect that outcome.

For example, who to target, when, why and through which channel.

That led us to investigate how they were making those decisions today and

how they could be improved with better insights.

For example, insights around next best product, channel preferences.

This led us to the techniques that are required to generate those insights and

the data required to generate those insights.

For example, characteristics of existing customers with one,

two or three products with the client.

So why do we need data and analytics framework?

The short answer is a framework allows you to move through

data analysis in an organized way.

It provides you with a process to follow as you walk with your teams and

clients to solve problems.

The framework allows us to focus on the business outcomes first and

the actions and decisions that enable the outcomes.

It focuses our attention on what generates value to our clients

first before examining all the data that we do have or

data that we don't have that needs to be procured.

In summary, the framework allows us to structure of a discretions with the client

and follow a path that leads to actionable insights and business outcomes.

If you're not using a framework, there's a good chance that different people will use

different approaches to solve the same problem.

If you don't use the approach we outlined, there is a tendency for

people to start with the data.

And then try to figure out all the cool analytics that one can do and then try and

figure out how they can be applied.

While this may be a fine exploratory process for newcomers in the field.

It is an approach that is fought with dangers of overanalysis and

a hammer looking for a nail to hit.

[MUSIC]

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