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Identifying, organizing and processing data

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Data-driven Decision Making

4.6 (3,614 件の評価) | 94K在籍する学生

Data Analysis and Presentation Skills: the PwC Approach 専門講座における 5 の 1 コース

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.

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学習するスキル

Data-Informed Decision-Making, Big Data, Data Analysis, Data Visualization (DataViz)

レビュー

4.6 (3,614 件の評価)

5 stars
2,492 ratings
4 stars
824 ratings
3 stars
206 ratings
2 stars
47 ratings
1 star
45 ratings

AF

Aug 16, 2018

This is a great course in data and analytics for the every day professional. It gives great insight into how data and analytics can support decision making. The mode of delivery of the course is e

JJ

Nov 14, 2018

What an excellent laid out course curriculum. Bite size learning, various mediums, understandable concepts applied to real life business cases made for a 100% take away and applied knowledge. We

レッスンから

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.

Identifying, organizing and processing data7:32

Week 2 recap with Amity and Mike1:31

講師

Alex Mannella
Alumni / Former Principal

字幕

[MUSIC]

In this video we're going to discuss how organizations capture data,

how they store and process it, and ultimately how they visualize it.

One piece of data won't provide much value but when you look everything holistically,

the result will be better decision making and insights.

With the very different types of data that are available today, there are traditional

approaches and emerging approaches to what organizations are doing with their data.

Let's start by talking about the traditional approach where you need to

extract data from a relational database in order to develop

a traditional segmentation model from marketing program.

To share this with your client, you can use tools like Excel, and Access and

PowerPoint to tell the story and communicate the results.

When you think about new types of data that are available

you need to take an innovative approach.

An example of this would be when you have multiple types of data coming from

traditional databases as well as something like social media.

You have all these different types of data that you need to bring together

in order to build insights.

Using these different types of data, you can tell the story of

what it means using advanced visualization tools.

All of this data combined can be used in a way that

allows organizations to make decisions in real time.

You'll hear more about the different types of data later this week but

data available to organizations is coming from a wide variety of sources.

Mobile, web, email, social media, sensors,

operational data, logs, texts, the lists goes on and on.

With all of this data we need to look at four things to figure out

how we will get the insights from this data.

One, how do we get the data?

Two, how do we store it?

Three, how we process the data?

And four, how are we going to visualize the data?

Here is an example of how we can capture different types of data

through one interaction and

use it make operational changes to improve the customer experience.

Let's say your client is a national pharmacy chain.

A customer uses her mobile device to chat with customer service when she has issues

with her prescription not being filled in a reasonable amount of time.

From this single chat,

the organization is able to collect three primary pieces of data.

The device the customer is using to communicate.

Texts from the logs that are being captured during the chat.

An indicator of the customer's preferences on

the channel that they choose to communicate.

We can use this data and visualize it to unveil segments in the customer population

that have an affinity for one channel or the other.

I just talked about gathering data from a mobile device but how else do we get data?

Manual input, point of sale systems, web forms,

sensors, you get where I'm going and I bet you can probably think of many more.

Let's say, you have a client that is looking to increase multimedia sales.

We start by looking at purchase patterns in

multimedia, and use this to develop better marketing strategies.

What platforms are your customers using to purchase multimedia, such as music,

movies, or television shows?

When are they purchasing them?

How much are they purchasing?

Not just understanding the segments, but also the price points, the timing, and

the preferences for one piece of multimedia over another.

We can get all of these data from traditional data stores

as well as from other sources like third party data suppliers, social media, and

user device data.

How do we store the data?

In the digital age, there's so much data but

warehousing every bit and byte isn't always the answer.

It has to be high quality data.

It has to be where organizational leaders can access it.

And it has to be the data they need to help assess their risk and

pursue strategic opportunities.

You need to think about the type of data being collected.

The granularity of the data.

The time the data is captured.

And the completeness of the data set.

It's not up to an analytics person to store the data efficiently.

We work with our technology people and think about the business prob and

the data we're trying to collect.

Understanding that the platforms will continue to evolve,

there will be traditional and emerging platforms and it's

up to the data scientist to help to find the analytics path and the data they need.

It is necessary to have a base understanding of the different platforms

so that we're asking intelligent questions to store the data that is out there.

In many organizations, a data and

analytics person would work with the technology department to figure out

what the right platform is to collect, store and analyze the data.

How do we process this data?

Once organizations have the data and

it's stored, the goal is to transform it into

insights that help address complex, strategic and operational challenges.

And in many cases gain that competitive advantage.

Our clients often think it's an either-or, but traditional and

emerging data both serve a purpose.

Structured data, available in databases and spreadsheets fit into the traditional

model, and social media and wearable data fits into the emerging platforms.

It's up to the people doing the analytics in an effective technology team

to figure out the best platform.

How do you process these data sets?

We use software packages like SAS, R, Excel and Access.

But it's on us as the analytics team to work with our clients to identify

the tools that will solve the issues for us today but we also need think

about what implication does that have for building the organization in the future.

We work with our clients to understand the issues, come up with hypotheses and

then determine if we need business intelligent tools.

And finally, what tools help us visualize the data and

tells us a story of what the data means.

Let's talk about the key tools and

technologies available to harness all of the power of the data that's out there.

There are static desktop graphics like Excel or PowerPoint.

We can use BI reporting dashboards that are regularly refreshed to show important

operational data.

An example is Microsoft Power BI which is a collection of online services and

features that enable users to find and

visualized data, share discoveries and collaborate in intuitive ways.

There is Google's platform, dynamic web or there's G predicts.

All our tools that our analytics teams work on with our clients.

There are also dynamic web visualization tools that allow for

interactive visualization, like QlikView, Tableau and Spotfire.

Next week, you'll learn about some of the uses and the benefits of these tools.

In this video, we talked about how organizations capture data,

how they store it, how they process it.

And finally, how they visualize it and use to tell a story or answer a key question.

[MUSIC]

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