Preparing Your Dashboard for Decision-makers

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Do curso por Duke University

Increasing Real Estate Management Profits: Harnessing Data Analytics

136 classificações

Duke University

Increasing Real Estate Management Profits: Harnessing Data Analytics

136 classificações

Curso 5 de 5 em Specialization Excel para MySQL: Técnicas Analíticas para Negócios

In this final course you will complete a Capstone Project using data analysis to recommend a method for improving profits for your company, Watershed Property Management, Inc. Watershed is responsible for managing thousands of residential rental properties throughout the United States. Your job is to persuade Watershed’s management team to pursue a new strategy for managing its properties that will increase their profits. To do this, you will: (1) Elicit information about important variables relevant to your analysis; (2) Draw upon your new MySQL database skills to extract relevant data from a real estate database; (3) Implement data analysis in Excel to identify the best opportunities for Watershed to increase revenue and maximize profits, while managing any new risks; (4) Create a Tableau dashboard to show Watershed executives the results of a sensitivity analysis; and (5) Articulate a significant and innovative business process change for Watershed based on your data analysis, that you will recommend to company executives. Airbnb, our Capstone’s official Sponsor, provided input on the project design. The top 10 Capstone completers each year will have the opportunity to present their work directly to senior data scientists at Airbnb live for feedback and discussion. "Note: Only learners who have passed the four previous courses in the specialization are eligible to take the Capstone."

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Dashboard for Decision-makers

This week complete your dashboard and add design elements so the dashboard is ready for stakeholders (Watershed executives, for example) to use it to test your model's assumptions.

Preparing Your Dashboard for Decision-makers4:35

Jittered Maps in Tableau10:26

Conheça os instrutores

Daniel Egger
Executive in Residence and Director, Center for Quantitative Modeling
Pratt School of Engineering, Duke University
Jana Schaich Borg
Assistant Research Professor
Social Science Research Institute

Congratulations on doing your first sensitivity analysis and tableau.

By now, you should replicated the results of your Excel watershed model

in your tableau dashboard using parameters and calculations.

In doing so, you might notice that it certainty would have been possible

to develop the entire predictive and financial model in tableau, and

skipped using Excel altogether.

When you take on new projects in your own companies, you should decide for

yourself when developing models in Excel or tableau would be most useful to you.

On that note, I wanted to make sure you knew that,

although tableau doesn't have the equivalent of Excel's solver function

built into its own software, tableau can achieve the same capability through its

integration with the statistical software R.

Tableau is set up so that, anything you can do in R, you can visualize in tableau.

So you could run your own custom state of the art statistical models for

optimization functions in R and

past parts of your equations into tableau to make a dashboard

that would help you visualize the effects of changing each part of those equations.

We aren't going to go over our integration with tableau in this specialization.

But they've included some length in the course free sources that will help you

explore possibilities on your own.

For now, it's just useful to know that do to its R integration, tableau actually is

much more sophisticated prediction and data science capabilities than Excel.

The analyses are just a little bit more of a pain to set up the first time.

This week we want you to finish using your dashboard to assess the reliability of

your model.

And then, spend the rest of the week focusing on designing and

formatting your dashboard so it's ready to share with Watershed executives.

You will start off by making one more visualization.

The gittered map I demonstrated for you last week.

Then, if you haven't done so already, make a first draft of the dashboard,

to allow you to investigate the effects of changing your assumptions on

the output of your model.

Change your assumptions systematically and

try to figure out, which assumptions change the results of your model the most?

How much do your model results change as you modify your assumptions?

And do they change in a way that would influence your recommendation?

Use your dashboard to figure out your final recommendation for

Watershed executives.

Should they enter the short-term rental market or not?

And if so, with what properties?

Once you know what your recommendation is, spend the rest of the week designing and

formatting a dashboard that will show Watershed executives how your model

supports your recommendation, and

how changes in your assumptions would influence your recommendation.

I warn you, going through the trouble to format a dashboard for

the external audience will give you first hand experience with two

limitations in the current data visualization field.

The first limitation is that despite their popularity,

dashboards are often very poor media for communicating the meaning of data.

They tend to pack in too much information in a very small space, so

they're difficult to design in a way that will guide your audience's eyes

in a clear path.

Until we find more creative ways to show large amounts of detailed information

efficiently, you often have to compromise storytelling ability in order

to accommodate the amount of information that needs to be included in dashboards.

That's not a big deal when you're making the dashboard for

yourself, since you know what to pay attention to.

But it has more significant consequences when you are trying to use dashboards

to convey information to an audience.

That's part of the reason tableau came up with the idea of story points, by the way.

Tableau stories are an effort to overcome the limitations of crowded individual

dashboards.

The second limitation you will appreciate this week is that although tableau is

exceptional at allowing you to create graphs quickly and

efficiently, it still sometimes struggles with allowing you to

format those graphs in exactly the way you want.

There are a handful of functionalities that you don't discover missing from

the software until you try to make a dashboard with limited space.

These functionalities include things like being able to resize titles or

column headers the way you want to.

Although there are often workarounds available for these issues that you can

find in the Tableau community website, the workarounds can be cumbersome,

which is hard not to notice when the rest of Tableau is so intuitive.

So you will see a lot of areas Tableau has room to grow this week.

When you find a missing capability that you really wish that Tableau would

implement, I suggest you post it in the ideas section of the Tableau community

website, so that the developers are aware of the need for that capability.

Of course, even if you find some aspects of making dashboards to be not ideal,

by the end of this week, you will also appreciate how useful dashboards can be

for helping both analysts and businesses make decisions.

Nothing beats being able to see all the important results of your analysis

in one place.

The ability to make a dashboard is a great skill to have an analyst no matter what

field you're in.

So enjoy this last phase of your dashboard making, and

remember to check out the videos and

links we've included with the course materials to help you out along the way.

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