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Visualizations

12 Avoidances in the initial dashboard design phase

There are many milestones through the lifecycle of a dashboards. The initial stages are the most important, in preventing headaches or failure. This list will hopefully give some guidance to start the ball rolling on a correct path to success.

1)      Avoid waiting for technology waiting for traditional BI projects to begin could take months or even years with delays. Find what technology you have available and start working on some implementations. Once you have started some ground work, the design can easily be applied to other BI technologies with little adjustments needed.

2)      Avoid underestimating time and resources – Though a dashboard is designed to be typically compact, it does not mean it will be quick and easy to create and maintain. Expect it to be an on-going, frequently changing process.

3)      Avoid static/stale data Having data automatically and frequently updateable is very important to the user and designer.  Static or stale data will lead to a false sense of confidence.

4)      Avoid limited access Making dashboards easily accessible is ideal. Making it easy for the user to access the dashboard will allow for more frequent uses and feedback; helping keep the dashboard current and useful. For example, distributing dashboards on the web can help keep data current and still adhere to IT protocols and security standards.

5)      Avoid choosing the wrong metrics Find the metrics that will end up answering the underlying questions.

  1. How does each metric contribute to the objectives?
  2. Is there data that can shed light on the objective?
  3. Is there a meaningful metric that measures those contributions?
  4. Are the metrics, chosen, necessary to contribute to the objective?
  5. Is the metric a continuous organized measurement?

6)      Avoid not knowing the goals and objectives of the dashboard. Some examples of the most common goals found in companies can be generalize as:

  1. Make better-informed business decisions.
  2. Improve customer interaction and satisfaction.
  3. Gain an overall competitive advantage.

The goal(s) usually can be seen having these characteristics at its core:

  1. Objective-focused
  2. Visual
  3. Relevant
  4. Current
  5. Accessible to its audience

7)      Avoid meaningless variety When starting, keep it simple and aim at 3-5 metrics. Never go above 10 metrics. Too many metrics will likely cause added charts and graphs that add no value to the dashboard.

8)      Avoid ignoring the target audience give the audience what they need (e.g. timely summaries, links to supporting details, what actions are applicable, etc..) The usual types of audiences can basically be broken down into 3 categories.

  1. Strategic (Executives) these users want a summary of the overall health, long term progress and goals, historical data.
  2. Operational (Department Heads) these users need a snapshot of the current operation, clear meaning, real time data, interaction and link to information.
  3. Analytic (Engineers, Programmers, Researchers, Lawyers, Paralegals, etc¦) these users need ways to do comparisons, review historical data, evaluate performance, drill into causes, needs feedback, established goals to course-correct.

9)      Avoid using metrics no one understands it is wise to avoid any unnecessary teaching of some new metric. Stick to the common and familiar metrics that the audience will know of and understand.

10)   Avoid using compound metrics Compound metrics are usually developed by using a formula comprised of a variety of measurements to come up with a single value. (Example: FICO, Klout, etc¦) The problem is that compound metrics masks insight and understanding making it hard to compare and contrast the real underlying numbers.

11)   Avoid blindly following easy measurements Easy measurements are great, but can cause users to lose sight of the bigger, more important picture. For example, SAT scores may be a great measurement of what a student’s potential will be at a college, it can also cause a blind spot on the whole life of the student.

12)   Avoid complexity Keep it simple. Focus on trends rather than point-in-time comparisons. Keep focus on the handful of metrics and design principles throughout the dashboard(s). Reduce, minimize, or eliminate non-data elements. Try reducing emphasis by changing the size, color, and borders; meanwhile, removing data that does not add insight. Draw attention and maximize the important data element.

Some helpful resources:

 

By |January 28th, 2014|Visualizations|

Breaking Complex Data into Visualizations that Tell a Story

How much do you enjoy reading pages and pages of detailed report data? A large amount of information presented in a typical Excel spreadsheet can also be difficult to digest. Presenting complex data in a way that everyone can easily understand can be a challenge for companies. Data visualizations are a simple way to summarize data down into easy-to-understand terms. Various studies by companies like 3M, show that people respond better to data visualization rather than raw data.

Data Grid for visualizaiton

Data Grid for visualizaiton

When your company has complex or a large amount of information to present, instead of just putting a bunch of numbers into a report, data visualization allows you to tell a story that would otherwise be virtually impossible to tell due to the shear amount of data organizations have today. Busy executives can more easily see patterns at a glance.

Using charts, graphs and other visuals can help the company’s management:

  • Easily spot areas that need improved
  • See patterns in sales volume
  • Understand customer behavior and what impacts it
  • Better understand demographics of the customer base and how to anticipate future demand of products
  • Make decisions on where to reduce expenses
Bar Chart for Data Visualization

Bar Chart for Data Visualization

Choosing the Best Visuals to Support Your Data

One of the biggest challenges in breaking down data into visuals is deciding which type of graphic would work best to present that information. There are many factors to weigh, including how much data needs to be presented, the overall goal of the visual and which items should be included and which shouldn’t. Working with someone experienced in visualizing data will help you better understand what types of visual data work best for different purposes and for your individual company.

Keep in mind that more than one type of graphic may be necessary to fully explore the data as a whole. One example would be sales figures for the year. A bar chart with top sales months highlighted might help your company understand which months are the busiest for sales and which months need more attention, while a pie chart of top-selling items could help you see if there are products that need replaced or updated.

Figuring out the Data’s Key Point

Another thing to consider is what the key point of the data actually is. Keep in mind that the visualization is meant to help the lay person more easily understand a collection of statistics and figures. First, everything needs to be labeled clearly, so a quick glance tells the viewer what the graphic is about. What information is it getting across?

Let’s say the graphic is a module on a dashboard that shows how many customers are using the company’s website and how they are using it. What is the most important information from those statistics? For the CEO, the most important information may be items like:

  • What percentage of customers are visiting the website versus contacting the company over the telephone?
  • How are they using the website? For information? To order? To interact?
  • How does this compare to competitor websites?

Driving Action

The right visual drives action by presenting data, calling attention to key points and engaging the reader. Modern data visualizations also include interactive elements in some cases that allow you to drill down into the detailed data that the visual relies upon, but fancy visuals shouldn’t be added just for the sake of adding these items.

Like human DNA no two visualizations are exactly alike. Each company has a different story to tell and each set of data is another chapter in that story. Understanding which data visualization tells the story the best takes time and the ability to break down complex data.

How do you tell your data story?

By |January 9th, 2014|Visualizations|