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The marketplace and emerging trends in big data analytics

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Data-driven Decision Making

4.6 (3,602 ratings) | 94K Students Enrolled

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

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.

Skills You'll Learn

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

Reviews

4.6 (3,602 ratings)

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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

From the lesson

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.

The marketplace and emerging trends in big data analytics10:31

Business impacts of technology advancements and data trends9:25

Taught By

Alex Mannella
Alumni / Former Principal

Transcript

[MUSIC]

Great to be back with you again.

In this video, Alex Mannela will share where he's seen data and

analytics in action.

Adding value to business practices, helping leaders make business

decisions and where it's having an impact from data modeling to social media.

>> Thanks Dan.

I'm Alex Mannella and analytics and data has played a critical role to me and

my career over the past 20 years.

Data and analytics continues to add value to

organizations by examining how decisions are made.

Next generation analytics can automate the way decisions are made

in areas like making inventory management, financial risk management, and

sensor data management.

We now have greater accessibility to data.

There is enhanced visibility of relevant information and

massive amounts of data are now easily accessible.

This results in improved reporting to clients and stakeholders.

We can use retail, social and clinical data to precisely customize offerings and

marketing campaigns to consumers.

We think about not just business to consumer, but also business to business.

Think about how the ability to use employee data to search for and discover

new needs can enable organizations to fine tune optimal performance and efficiency.

By using data and analytics to identify emerging trends,

organizations will be able to create new service offerings for their consumers.

And new business models who execute them.

Using data and analytics isn't limited to only high tech industries.

Whatever sector you operate in, be it finance, healthcare, retail, or just

about anything else, data and analytics can and will play a critical role.

Some examples include: discovering patterns in large numbers of service requests for

a large bank; discovering which brands are most similar based on social media

platforms and followers on each; gaining insight

into an annuity policyholder behavior based upon terabytes of consumer data.

Now, when one observes or collects data firsthand, this is called primary data.

Primary data is very valuable in getting consumer behavioral insights.

If asking a consumer what kind of coffee they like or what

kind they intend to purchase, we don't learn what they've actually purchased.

That's where secondary data comes in.

Secondary data is data that is collected by someone or something else.

In this case, one could collect the data from someone's credit card statement and

determine whether or not they actually bought the coffee.

Both primary and secondary data are used in building strategic decisions.

Big marketing decisions and M and A decisions and beyond.

Right now, rapidly evolving technology and

the availability of real time data is allowing organizations to use data

analytics as the true game changer and the table stakes of the future.

Organizations are collecting massive amounts of data about their customers, and

the customers are expecting that this data is used intelligently and precisely.

Our clients are coming to us because their customers are expecting that this data is

to be used to improve communications, improve products,

and ultimately the services that are being offered.

If an organization is going to be an industry leader or

an innovator, analytics is key to understanding correlation and

causation in turning strategy into reality to.

Organizations today are facing information overload as data increasingly comes at

them in new formats, and at faster speeds and higher volumes than ever before.

They have to use a combination of techniques, including simulation and

visualization to manage, manipulate and organize this torrent of data.

So they can address the progressively complex decisions they have to make

from operational control, to management control, to strategic planning.

Our clients are now trying to figure out how they build information insights.

The groups, the analytic centers of excellence, and

how to take these organizations to the next level to drive value.

What does the organizational structure need to look like?

What does it mean?

It's not just the data and analytics.

If you can't get the data through the organization efficiently,

it's just a statistical model or it's just an answer.

Our clients are challenging us on a daily basis to help them figure out

what an organization that fully uses the data and analytics must look like.

What do the models look like?

Another vital source of data is social media.

Generally, social media is defined as a set of internet technologies that allow

users to generate and share content, collaborate, exchange ideas and

form community with shared or opposing interests.

Social media can be good or bad.

So many clients think there is so much to find, but

there is a lot of noise in social media.

So how do you extract that signal from the noise?

It's important to look at data collected from social media platforms,

along with all other relevant datasets.

Social media is a powerful platform but it's one that many organizations

are chasing, and don't have a sound social media or overall integrated data strategy.

When you think about data and analytics,

you need to think about the social media strategy in the organization.

What can someone bring from a social perspective?

Being able to challenge how social media is being used, and

if it's going to be used from an analytics perspective.

Whatever social media data an organization is trying to use, it's very important that

you tie it to other relevant data sets to validate findings and

ensure the accuracy of what you are projecting.

We've seen our organizations misuse data from social media and

not tie it back to other data assets that they have into the analytics and

models that they are building.

When used properly, data and analytics can change how organizations operate,

allowing them to become more efficient and

have greater impact on their bottom line and most importantly, their customers.

Now let's look at how data and analytics can be used in the retail industry.

Companies can use consumer purchase data, social networks,

and geolocation information to optimize product mixes and

make decisions related to product development.

For example, a retailer can use data generated from consumer mobile devices for

location based sales and offerings.

Take a local drug store where a customer has their mobile

app installed on their phone.

The drugstore can push a notification if you are nearby

because it uses your geolocation information efficiently.

Organizations can also use data from mobile devices for payments and

purchase patterns.

The organization can go well beyond just pushing offers,

but using the data and analytics to optimize products and segments is

one of the core components that underlies social media and data analytics.

The operations department at the same retailer can evaluate store traffic

patterns to improve product placement and point of sales processes.

And finally, the service department can evaluate online and

real time interactions to improve and tailor in-store and

post-sale experiences.

Another example of data analytics in action is from the healthcare industry.

In terms of product development, analysis of disease trends in clinical studies

can direct, and optimize research and development efforts for drug discovery.

Data can help bring new drugs to market more quickly, and

more efficiently, using things like claims data to more efficiently

communicate with policyholders on how they can manage their health and

prevent specific disease or illnesses from happening, diabetes or the common cold.

Analysis of patient history and disease patterns allows sales and

marketing teams to target patients with care or lifestyle improvements.

By using claims data and patient demographics,

hospitals can improve operations by optimizing treatment selections.

And in terms of customer service, health care providers can improve treatments and

manage chronic illnesses through data collected from ongoing patient monitoring.

As the processing capacity of data analytics continues to increase, it opens

the door to a broad range of advanced algorithms and modeling techniques that

organizations can employ to draw a valuable insights from data and analytics.

There are different ways to model structured and unstructured data.

Of course there are the traditional methods, but

we also see many new modelling techniques emerging to handle all the different types

of data that is available.

For the analytics individual or for the advanced data scientist,

there are a portfolio of methods that can be used today, more so than ever,

given the advancements in processing power, mathematics, data storage and

most importantly visualization.

In summary, what has fundamentally changed are three critical things.

Having the right people, the right tools, and the right algorithms in place to take

advantage of the massive amounts of data that organizations have access to today.

>> Thanks Alex, for those terrific insights into data and analytics.

In this video, we've learned how data and analytics is helping add value to business

practices And becoming a critical game changer in making business decisions.

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