Revisiting Problem Formulation

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Business Analytics and Digital Media

138 classificações

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Indian School of Business

Business Analytics and Digital Media

138 classificações

Curso 2 de 6 em Specialization Business Technology Management

The explosion in digital media - web, social and now mobile - represents a departure from how things were like in the last century. This proliferation of digital media is both a threat and an opportunity for many businesses. Business Analytics can be leveraged to process data, sentiment, buzz, contacts, context and other aspects of business interest in real time, for business performance and impact. The course picks and uses use-cases from a variety of industries and geographies, to showcase the potential and impact that business analytics done properly (or not) can have on business performance.

Na lição

Introduction to Business Analytics

An overview of the what and the why Learning outcomes by the end of week 1 should normatively be that (a) Students grasp what is business analytics from the perspective of a business manager, (b) identify areas of interest, overlap, co-ordination and conflict with other business functions and processes in the firm, (c) and, develop an appreciation for the value of data, of analyses and of the components of analytics.

Introductory Business Analytics and Digital Media6:45

Conceptual Preliminaries13:39

Framework for Problem Formulation7:21

Transformative Problem Formulation6:58

Data Types and Psychometric Scaling7:17

Exploratory Vs Confirmatory Dichotomy7:13

Revisiting Problem Formulation4:27

Conheça os instrutores

Sudhir Voleti
Assistant Professor and ISB Research Fellow
Marketing

[MUSIC]

Well, hello everybody, welcome back.

I'm going to wrap up the session now with a few closing remarks.

And a very interesting reading.

Now, this is a slightly oldish reading.

But before I get there, let me revisit problem formulation a bit.

So recall the business problem we had done, about the decision maker and XYZ.

I will actually claim that, that was easy to solve.

And the reason for that?

There are the symptoms actually showed up.

I mean, we could see that sales were falling, and

then something had to be done.

It gets tricky.

So in some sense, there is a problem, we have to cure it.

It gets tricky, I guess, when the symptoms are invisible, and

a problem is actually yet to manifest.

But it will manifest in the future.

But there are no symptoms also, there is no biting urgency to do something.

Is there something in this realm,

something called preventive or predictive analytics?

Can I predict failure in the future and act now?

Even though there are no symptoms at present that say that something is

going wrong?

And that is where a lot of analytics actually is moving.

How best to understand this than is through a reading.

This reading, as of 2010 vintage.

But, it is actually a very nice reading because it brings together a lot of

history in some sense, and it reports it forward that way.

So let's read this, and have a short discussion on the implications.

Welcome back.

I hope you've seen the reading, right?

So which analytic type is going to be more challenging, the curator one or

the predicted one?

Clearly, it is going to be the predictive one.

My question is, why?

For the simple reason that the symptoms are not visible so

we don't even have dark to go with.

The big learning from the reading is a traditional product-category,

industry and sectoral boundaries are starting to blur.

No longer can I claim to know my competition,

as we use to be able to say in the traditional.

Today's competition could actually come from across industry barriers.

It could happen just because, it could come out of the blue in some sense.

MobilePay could actually compete with Banks, when taken some sense could disrupt

the state banking sector, well regulated banking sector as well.

Which brings up the question, are there any silver bullets?

If you were Sony or Cannon in 2003, and the digital photography

revolution was just taking off, how would you know what was in store?

How would you know just how bad the impact would be?

What could you have done, in some sense, to forestall it?

Take a minute, think about it.

Are there any silver bullets?

If you were some other company in some other sector, is there a way,

an approach, a method for me to know that I'm going to be disrupted tomorrow?

To me, there is only one, in some sense, and ones that are a little bit out there.

And that is to focus on customer need.

It doesn't have to be,

the customer need could be satisfied by an offering from another industry.

It doesn't have to be within my own industry.

So if that is changing, that is basically what, in some sense, I want to look at.

And then with these problems arise opportunities, latent needs are one such.

Now, latent basically means something that has been at the surface,

that is not actually visible.

And latent needs are those needs of customers

that customers don't know they have.

And offering a product could appeal to a latent need, and

the market could switch overnight.

In some sense, latent means present a problem and an opportunity.

You, as a firm, could actually discover a latent need, and

perhaps capitalize on that, based on firm capabilities as of now.

Conversely, someone else could discover your existing customer basis latent need

and try to, in some sense, attack that.

All of this would call for a reliable data analytics operation,

predictive analytics, curative analytics, too, for that matter.

Would require a very nice data problem formulation,

data collection, data analytics operation backing it up.

With that, we are completed with the first session of this course.

In the other three sessions,

we will explore data collection plus analytics in a lot more detail.

And digital media of course, also playing a role in data collection.

[MUSIC]

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