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.
Data and analytics examples at PwC
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来自 PwC 的课程
Data-driven Decision Making
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从本节课中
Technology and types of data
This module is an introductory look at big data and big data analytics where you will learn the about different types of data. We’ll also introduce you to PwC's perspective on big data and explain the impact of big data on businesses. Finally we will name some of the different types of tools and technologies used to gather data.
与讲师见面
Alex MannellaAlumni / Former Principal
[MUSIC]
In this video, you're going to hear some of our PWC professionals have applied data
and analytics to address business challenges.
Including identifying growth opportunities, and
driving customer engagement to operate more efficiently.
While you watch this video consider how organizations can use data and analytics.
This can be helpful to you when you engage on the discussion boards with other
learners following this video.
>> Big data projects can be accomplished in two to three months, and
I'll give you an example.
We had a content provider client that wanted to know more about
the effectiveness of placement of their content within web storefronts and what
was driving customer behaviors to go view that content and purchase that content.
We're able to do that analysis using big data and
analytics to show what customer behavior was, what preferences there were,
and where that placement could drive more customers to make purchases.
In addition to that finding, the answer that we came back with beget
more questions, and that spawned a couple of other initiatives within the company.
We had a client in the communications industry that has the ability to
distinguish viewing patterns of their customers.
And what they wanted to do was track what's called pathing, and
following customers as they go from channel to channel to understand,
how do I optimize my advertising
by making sure that the right segments get hit with those messages.
The complexity of trying to figure out the behavior of all of my customers quantified
by all the channels and content that I offer becomes a little bit mind-boggling.
But if you plot that into some sort of visual representation on neural net
if you will, which shows the connection points, the highest probability connection
points between say news channel and lifestyle channels.
You start to understand and appreciate the demographics, where they go and
how do I target my advertising to reach those people more effectively.
>> There are a lot of new ways that companies are using the so
called big data technology and new sources of data.
Let me give an example, there's a very rich, very large beverage company, and
traditionally what they had done in terms of stocking up supplies with
the retailers was essentially looking at what was the past demand,
and based on the past demand, they would essentially stock up the shelf.
Of course, they'll adjust it for seasonal variations.
Some are winter and different days, so they would do that.
That's the way they were stocking out.
And the issue was they had a substantial stock problem.
In other words, when a person comes to the store they can't find the specific brand
of beverage that they wanted.
So they might have the overall brand but they may not have a light version or
they may not have the right flavor.
So they're always running into stock.
And they did an exercise to find out what the stock was and
it was a substantial number.
So what this particular organization did, and they came to us,
and they had a number of people with internally who were crunching through
a lot of algorithms to find the right optimal solution for stocking but
none of that had actually included interesting data from the external world.
In fact, real time data, real time data about other,
is there a football match happening in the vicinity of where these retailers are?
What is the temperature on a particular given day
when that football match was happening?
Taking into consideration all of those leads to
substantial variations in how we want to stack up various retailers.
And also they were able to do that in a way that was able to combine that
information but also disseminate the information in a way that a normal person,
a retailer, in a normal mom and pop shop could actually use that data.
So we help them bring in data literally on a smartphone,
and very simple ways of telling them how much they should
be stocking and how often they should be stocking.
And this is something which we call intelligence at the moment.
In other words, there is no point in having all of the fantastic analytics
happen in the head office where you're generating those insights.
They are not that useful if it doesn't make it's way on the shop floor,
as in this particular instance had the retailer.
And the retailers are not statistical PhDs, so
you need to tell in a way that they can understand and they can input the values.
So that's what we were able to do.
>> You just heard two examples of data and
analytics were applied to address real life business challenges.
Think about these examples and spend some time with your fellow learners
discussing data in action on the discussion board.
[MUSIC]
