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Murder Mystery: a
data project
Save 42% off The Art of Data
Usability with code slbjorgvinsson
at manning.com.
Murder mystery or data project?
Maybe you’re like us – a big fan of murder mysteries.
The fun part of a cozy mystery is to...
Murder mystery or data project?
All murder mysteries follow a similar structure – with
minor variations:
 Characters asse...
Murder mystery or data project?
A cozy murder mystery not only follows a similar
structure as other cozy murder mysteries,...
Murder mystery or data project?
Typical data project structure:
 A situation and context trigger a project.
 The foundat...
Designing for different situations.
The first stage of a project, the trigger (or event), is
usually not something we cont...
Designing for different situations.
Your role in this first stage is not to create a situation,
just make the necessary pr...
Setting up the infrastructure.
After designing the project, you usually just don’t go
ahead and start typing in formulas i...
Setting up the infrastructure.
You need to decide how you’re going to manage the
data:
 Are you really going to use sprea...
Setting up the infrastructure.
This stage (of the project) is about how you manage
the data in a particular situation.
It ...
Collecting and processing data.
After setting up how you’ll manage the data, you can
actually start in on the ‘work’ part ...
Collecting and processing data.
This is the meat and potatoes of the project, and also
the part that most people associate...
Collecting and processing data.
The approach of exhaustively collecting all relevant
and error-free data might not always ...
Collecting and processing data.
Sometimes we have to focus on attributes other than
precision and accuracy—even at the cos...
Announcing the results.
After producing results your work isn’t done. You have
to tell someone about it.
When it’s time to...
Announcing the results.
There are various ways to disseminate the results.
Probably the most-used form of dissemination is...
Announcing the results.
You might think that you’ve done the lion’s share of
the work at this point, but don’t relax yet.
...
Wrapping up the project.
When our project finishes we don’t just turn out the
lights and leave. There are things you have ...
Wrapping up the project.
So, we’re going to wrap up this presentation by saying
that this was just a taste of what awaits ...
Data is only valuable if it’s useful, so
maximize your data’s usability.
Save 42% off The Art of Data
Usability with code ...
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The Art of Data Usability: data is only valuable if it's useful

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Data is only valuable if it's useful. If you're responsible for making meaningful data available to business stakeholders, researchers, or even the general public, you need a predictable process for discerning the users' needs and delivering the right data in the right way.
The Art of Data Usability teaches you to think about data quality in context, presenting a methodology to maximize the usefulness of data for its intended consumers.

Save 42% off The Art of Data Usability with code slbjorgvinsson at: https://www.manning.com/books/the-art-of-data-usability

Veröffentlicht in: Software
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The Art of Data Usability: data is only valuable if it's useful

  1. 1. Murder Mystery: a data project Save 42% off The Art of Data Usability with code slbjorgvinsson at manning.com.
  2. 2. Murder mystery or data project? Maybe you’re like us – a big fan of murder mysteries. The fun part of a cozy mystery is to have the same data as the quirky detective but not the same knowledge. The clues are all there, they’re all hidden in plain sight. But you, as the reader, are unable to figure out how they relate or how important they are until the very last pages.
  3. 3. Murder mystery or data project? All murder mysteries follow a similar structure – with minor variations:  Characters assemble and interact in a specific situation… then murder!  Quirky detective is called in (or butts in) to work on the case.  Quirky detective gathers clues, then analyzes them.  Quirky detective get everyone into a room and announces the culprit, then takes his or her leave.
  4. 4. Murder mystery or data project? A cozy murder mystery not only follows a similar structure as other cozy murder mysteries, it also follows a similar structure as most data projects. When you stop to think about it, a murder mystery is in fact just a data project, perhaps with more suspense and prose. The structure of a data project is essentially just the same as a cozy murder mystery!
  5. 5. Murder mystery or data project? Typical data project structure:  A situation and context trigger a project.  The foundations of the project are established.  Different data points are collected, and then processed.  Results of processing are disseminated.  The project is then closed. Sound familiar?
  6. 6. Designing for different situations. The first stage of a project, the trigger (or event), is usually not something we control. It’s just something that happens and we respond to it, so you don’t really do anything in a data project in this stage except prepare for possible situations. You already have the world in which a data project takes place, and you have to design the data project around that world.
  7. 7. Designing for different situations. Your role in this first stage is not to create a situation, just make the necessary preparations:  Identify the different situations that could come up.  Identify who will be involved as data providers and consumers.  Identify what the people involved want to get out of the project.
  8. 8. Setting up the infrastructure. After designing the project, you usually just don’t go ahead and start typing in formulas into a spreadsheet, willy-nilly. We have entered the next stage – the data management stage, which is about properly managing the data. Therefore, we need to ask ourselves some questions.
  9. 9. Setting up the infrastructure. You need to decide how you’re going to manage the data:  Are you really going to use spreadsheets?  Will you use databases?  How are you going to ensure access to the data?  What if your computer breaks down?  Etc.
  10. 10. Setting up the infrastructure. This stage (of the project) is about how you manage the data in a particular situation. It is equivalent to the part of the murder mystery in which the clues are handled, and how the characters relate to them. At this point, you’re thinking about the quality of the data handling, i.e. monitoring how well the project’s requirements are being fulfilled, and about data usability, i.e. making your data useful.
  11. 11. Collecting and processing data. After setting up how you’ll manage the data, you can actually start in on the ‘work’ part of the project: collecting and processing the data. These two processes are not linear, and you will often run into a situation where you realize you require more data, after collecting and processing a batch. Imagine our detective mulling over the clues and searching for connections. Sometimes he/she finds a new lead!
  12. 12. Collecting and processing data. This is the meat and potatoes of the project, and also the part that most people associate with data usability. However, usability is not limited to the output of data processing. A common notion is that we need to collect all of the data, and that data processing must be error-free, in order to get the most accurate data at the end. This doesn’t necessarily have to be the case!
  13. 13. Collecting and processing data. The approach of exhaustively collecting all relevant and error-free data might not always be the best. Consider the following:  Collecting all of the data may be too time- consuming or even impossible.  Sometimes accurate data isn’t needed, rather a rough idea that can be obtained quickly.  Data collection and processing are important, but they’re not everything.
  14. 14. Collecting and processing data. Sometimes we have to focus on attributes other than precision and accuracy—even at the cost of producing absolutely correct data! This may sound a bit unorthodox (even blasphemous), but sometimes pinpoint accuracy isn’t the most desirable attribute for a given project’s output data— some other attribute is: timeliness, for example.
  15. 15. Announcing the results. After producing results your work isn’t done. You have to tell someone about it. When it’s time to sit your stakeholders down and tell them the outcome—like the detective telling the gathered characters who did it, and how—you need to consider how you want to disseminate what you have learned.
  16. 16. Announcing the results. There are various ways to disseminate the results. Probably the most-used form of dissemination is writing reports and submitting them into some bureaucratic black hole—never to be seen again. Luckily, there are other acceptable methods, such as blog posts, videos, infographics, dashboards, or even a soap box in the park, just whatever works best to get the results across to the intended audience.
  17. 17. Announcing the results. You might think that you’ve done the lion’s share of the work at this point, but don’t relax yet. Even though the collection and processing of the data is done, the dissemination stage is just as important. Data usability makes another appearance in so far as the quality of how one disseminates his or her data, i.e. the way you pass on your new knowledge is a big part of seeing that it gets put to good use.
  18. 18. Wrapping up the project. When our project finishes we don’t just turn out the lights and leave. There are things you have to consider and take care of before you can properly close off the project. Like where to go with your results, and whether they can be built upon in a subsequent data project—to name a few possibilities.
  19. 19. Wrapping up the project. So, we’re going to wrap up this presentation by saying that this was just a taste of what awaits you inside The Art of Data Usability, and that if you really want to dig deep into data quality, data usability, and getting the most out of data projects, then you really ought to get this book! Check out the first chapter for free here.
  20. 20. Data is only valuable if it’s useful, so maximize your data’s usability. Save 42% off The Art of Data Usability with code slbjorgvinsson at manning.com. Also see:

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