Tech Layoffs, Visualized

The last few months have been difficult for technology workers. It seems like every week, we hear about a blue-chip tech company laying off thousands of employees. Crunchbase has been tracking US-based technology layoffs here. But an ever-growing table like the one below doesn’t exactly tell the story or reveal trends.

Crunchbase data on Tech Layoffs, 2022/2023

There’s obviously a lot of value hidden in this data, so we pointed Juicebox at it to discover (and share) some of those hidden insights. The interactive report we built is embedded below, but here are some things we captured during our exploration:

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Here’s the embedded report so you can explore the data for yourself. Start scrolling:

Take a Visualization MasterClass with 'We Feel Fine'

My first stop for data visualization inspiration is Jonathan Harris’ We Feel Fine. It is a MasterClass in combining artistic passion, compassion for the subject matter, and technical craftmanship in user experience design.

Released in 2005, it continues to stand out as a monumental achievement in the breadth of its ambition and in the tiny details that make it a delight to experience. This is why I come back again and again for design concepts and engaging data visualization techniques that reach any audience.

We Feel Fine set out to harvest the feelings of the internet…

It searches the world's newly posted blog entries for occurrences of the phrases "I feel" and "I am feeling". When it finds such a phrase, it records the full sentence, up to the period, and identifies the "feeling" expressed in that sentence (e.g. sad, happy, depressed, etc.).

This data is gathered into a database of “several million human feelings” and presented through a series of searchable and sortable visualizations.

I love the realization of this vision. And in that spirit, I want to share a few of the brilliant design choices that have inspired me for over a decade.

Bringing physicality to data

Harris seems to ask the question: What if you could play with data in the way you play in a sandbox? His data points dart around the screen, come together in blobs as if by magnetic attraction, and reshape themselves to tell their story. I’ve always been drawn to the challenge of bridging the gap between the abstract nature of data and the reality that it represents. We Feel Fine delivers on this notion.

Animated fun

We Feel Fine is exploding with energy, just like the emotions it represents. The “Mounds” visualization collects the volume of different feelings and shows them in wiggling Jello-like piles. While it is effectively a bar chart, the result is far more engaging for the viewer.

Careful use of color

Harris uses color to bring energy to his visualizations. With the dark backgrounds, the vibrant colors provide a powerful but not overwhelming contrast. And even when he is using multiple colors, he is able to sequence them and limit the variety to ensure it isn’t distracting.

Visual elements to enhance the data

Good data presentation combines data visualization with other visual elements that communicate the message. In We Feel Fine, the use of typography and icons helps support the data content. He even uses touches of skeuomorphism (i.e. interface objects that mimic their real-world counterparts) to express the data in creative ways.

Word Play

Data visualizations and data stories need to guide the audience through the content with text. Harris brings a levity to the language he uses. Most notably, the various “Movements” (i.e modules of his project) all start with the letter M. If you are having fun as author, your readers will too.

Specificity

If I’ve said it once, I’ve said it a thousand times: Specificity is the soul of (data) narrative. In We Feel Fine each data point is a person expressing a feeling, and each point can be expanded to see the feeling-statement and associated image. These feelings are the atomic building blocks of the database and the visual presentations — Harris wants to make sure you never lose sight of that.

Parts into the whole

The “Mobs” visualization in We Feel Fine stacks up individual feelings into a bar chart. This is a technique that I love and we have borrowed for our own distribution charts. For the viewer, it reinforces the fact that each bar is composed of unique elements. As a result, the data values becomes less of an abstract concept.

Selections that are fun and intuitive

Throughout We Feel Fine, the reader can filter to narrow the range of feels shown. The interface for this filtering is among the best examples I’ve seen. It is clear, intuitive, and easy to navigate. As with everything else in this project, it is carefully crafted with the user in mind.

A Guide to Building Better Data Products

At Juice, we’ve helped our clients launch dozens of data products that generate new revenue streams, differentiate their solutions in the market and build stronger customer relationships. Along the way, we’ve learned a lot about what works and doesn’t. In this series I’ll take you through what you need to know to design, build, launch, sell and support a data product.

Part 1: Getting Started

The first step in building a great data product is to pinpoint a customer need and determine how your unique capabilities will solve for that need.

A successful data product lies at the intersection of the three circles in the following Venn diagram:

1. Your customer’s pain point, an urgent problem they want to solve;

2. The characteristics of your data which can be brought together to solve that problem;

3. The capabilities you have to enhance the value of the data to make it as useful as possible.

Take the Academic Insights data product we designed and built for US News and World Report as an example of finding this intersection. (1) Their customers, university administrators, needed to understand how they compare to peer institutions and where they could best invest to improve their performance and stoke student demand. (2) US News was sitting on decades of detailed survey data and rankings to compare universities of all types. This data was unique in its breadth and historical coverage. However, the data was essentially stored in old copies of the paper magazine, not a format that was conducive to delivering insights to their target audience. (3) That’s where our data visualization and user experience capabilities helped them turn this data into a web-based analytical tool that focused users on the metrics and peer groups they cared about.

Let’s dive a little deeper into those three elements:

1. Pain Points

We’ve noticed a temptation with data products to forget the cardinal rule of any product: it needs to solve a specific problem. Without this focus, a data product comes in the form of a massive 100-page PowerPoint deck or a collection of raw data tables. There may be value in the data, but it is clear the product manager hasn’t thought deeply about their customers and what the data can do to solve their problems. I spoke to a credit card executive recently who mentioned how his bank spent huge sums of money on benchmarking reports. Despite his deep experience, he was unable to make sense of the reports he was sent. These are lost opportunities to deliver powerful data products.

“Your users are your guidepost. And the way you stay on the right path in the early stages of a startup is to build stuff and talk to users. And nothing else.” -- Jessica Livingston, co-founder of Y Combinator

With data products the core question of your user is: What information or insights will let you make better decisions and perform better in your job? 

Look for those unique situations where indecision, ignorance, or lack of information are blocking smart actions. Rather than solving your user’s pain, you need to enable them to solve their own pain. Physician, heal thyself.

2. What’s unique about your data?

The foundation of your product should be data that is somehow unique, differentiating, and valuable. In our experience, the right raw materials can come in a few different forms:

Breadth: Do you have visibility across an entire industry? Or population segment? Breadth allows you to provide benchmarks and comparisons that aren’t otherwise visible to your customers. One of our clients has data on the learning activities of more than 60% of all healthcare workers.

Depth: Can you explore deeply the behaviors of individual people, companies, or processes? By drilling into these activities, you may have the power to predict future behaviors or find correlations that aren’t visible to others. Fitbit tracks massive amounts of personal activity data from each individual user.

Multiple data perspectives: Are you in a position to combine data sources across industries or connect disparate data sources? By bringing together different perspectives on your subject, you may be able to answer new types of questions or explain behaviors through a multi-faceted perspective.

Naturally, having breadth, depth and multiple perspectives is best of all. Companies like Google, Apple and Amazon have profound data assets because they can both see human behaviors across a large audience and they know a lot about each individual.

3. Your value-added data package

It is seldom enough to create a data product that is simply a pile of data. That isn’t to say we haven’t seen many companies that believe that a massive data extract represents a useful solution to their customers.

People don’t want data, they want solutions.

How are you going to turn that data into a solution? There are many paths to consider:

• Visual representations that reveal patterns in the data and make it more human readable.

• Predictive models to take descriptive data and attempt to tell the future.

• Industry expertise to bring understanding of best practices, presentation of the best metrics, analysis of the data, and thoughtful recommendations. Bake your knowledge of the problem and the data into a problem-solving application.

• Enhancing the data through segmentation, pattern recognition, and other data science tools. For example, comments on a survey can be enhanced with semantic pattern recognition to identify important themes.

• Enabling users with features and capabilities to make them better in their job. The user's ability to analyze, present and communicate insights can be a value-add to the raw data.

If you can determine the right recipe of customer need, data and value add, then you've gone a long way toward defining the foundation of your data product. But before getting down to designing the data product, you'll want to get the right people in place.

4. The right product manager

We’ve helped launch data products in many industries including healthcare, education, insurance, advertising and market research. The most important factor in turning a concept into a business is a quality product manager. The best product managers have a vision for the product, understand the target customers, communicate well, are definitive in their decisions and recognize the reality of technical trade-offs. For a more complete list of general product manager skills, check out this Quora answer.

For data products, we’d emphasize a few more skills. The product manager needs to understand the data, what it represents and the business rules behind it. It helps if she is a subject matter expert, but if not, she should know when to bring in more expertise. Finally, she needs to understand the technical challenges involved with building a data product and be able to weight the impact of changes (which are often necessary as you learn more) against the benefits of launching sooner and gathering customer feedback.

5. Get stakeholder buy-in early

Kevin Smith of NextWave Business Intelligence (a consultancy focused on data products) warns: “Get the critical stakeholders involved and in agreement early or you’ll end up reciting the history of the project and why key decisions were made many times for many people.”

Launching data products is a journey that doesn’t end at the product launch. It also can push your organization into new and uncomfortable ground. These realities highlight the need to build broad support early in your process. Ask yourself:

• Is IT on board to provide development support, data access and data security resources and sign-off?

• Is the COO ready to provide resources after launch to support and maintain the product?

• Is your legal team confident that the data you’ve been collecting and incorporating into your data product is available for this new purpose?

• Is the marketing team ready to support a product launch that includes all the resources, collateral and creativity required of any new product?

• Is the sales team in place to understand the product, the target audience and establish the sales framework for pushing the product?

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“The secret to getting ahead is getting started.” 
― Mark Twain

For data products, this means finding your sweet spot at the intersection of customer needs, your data, and data product value add. And then getting the right people lined up to make your product a success.

Part 2: Development

If “Data is the Bacon of Business” (TM), then customer reporting is the Wendy’s Baconator. Sure it contains bacon, but nobody is particularly happy with themselves after eating it.

In a recent blog post, we described the differences between customer reporting and data products. Those differences result in some very different functional requirements. In particular, data products require more C.L.I.C. D.R.A.G.

• Context — Benchmarks, comparisons, trends, and/or goals that encourage decision-making.

• Learn — Help and support features to train users to get value from the information.

• Integration — Connections with other software systems to integrate with data and enable operational actions.

• Collaboration — The ability to save insights and communicate them with other people. Decisions aren’t made on an island.

• Documentation — Because data products live on and touch many people within your organization.

• Reporting — To track usage of the data product.

• Administration — Features to manage users and control permissions.

• Guidance — To point users to the most effective ways to explore and understand the data.

This collection of capabilities gives some indication of the gap between your standard customer-facing reporting and a complete data product. To accomplish all of these, you’ll need more than a talented BI report writer and access to your database. In our experience, the recipe for building a successful data product is dependent on a number of specialized roles.

Product Manager

The Product Manager sets the vision of the product. He gathers the necessary resources to make the team successful and builds organizational support for the product.

UI/UX Designer

The UI/UX Designer understands the user’s workflow and how to best guide the user to decisions. She crafts the interface and interactions to make the data intuitive. She's also in charge of design application styling and all visual elements.

Business Analyst

The Business Analyst translates application design into technical and data requirements. She's responsible for documenting business logic as product decisions are made.

Front-end Application Developer

The Front-end Application Developer's role is all about building interface elements, interactions, and data visualizations.

Back-end Application Developer

The Back-end Application Developer does everything the Front-end Developer does, only backward. Just kidding! But he does build the application server environment and define data queries to support UI interactions.

Data Guru

In addition to having the coolest title, he provides access to raw data sources. He understands and communicates the meaning of data fields and calculations to the development team.

Data Scientist

The Data Scientist defines the questions that will help end-users make better decisions. She enhances data through predictive modeling and other advanced data analytics techniques.

Technical Architect

He's the general technical architecture of the product, responsible for figuring out how the application connects to data sources and integrates into other systems.

Quality Assurance Engineer

The Quality Assurance Engineer evaluates whether the data product meets the need and requirements set out in the design process. He also tests data accuracy and product functionality.

It's a big load. That’s why you might want some help before going at it alone. At Juice, we've built a technology solution and an expert team that fills out many of these requirements. We have a platform, Juicebox, that ensures your application is a first-class user experience. Combined with our experienced design and implementation teams, we’ve got many of the resources covered. Our clients bring the product vision; we make it happen.

Our goal at Juice is to streamline the data product launch process so you can launch innovative data products in weeks, not months. Want to know more? Give Juicebox a try.

3 Basic Lessons on Data Visualization

These are three of the most important principles to bring to your data visualization work.

#1. Start with your audience

Before you make your first chart, think about who you are looking to serve with this data and what they do in their role.

An executive will have very different needs from an analyst. What data you choose to present and how you present it should start with empathy for your audience.

#2. Pick the right chart

Every chart type has its strengths and weaknesses. Line charts are great for showing change over time. Bar charts compare performance on one measure for a set of things.

You’ll need to understand what you want to emphasize in your data, then select the chart type that highlights that part of your data.

#3. Keep it simple

Remember that audience in #1? They are busy and have little attention. You need to keep your visualization as direct and straightforward as possible.

“Keeping it simple” means highlighting the important insights explicitly, labeling your chart clearly, and removing detail or extra data that distracts from your message. This is the hard and important work of editing.

#4 (Bonus): Looks matter

While your data visualization is primarily about the insights from your data, making it attractive can be the difference between your audience reading it or not.

Quality data visualization includes thoughtful use of color and contrast and graphical elements to bring the data to life or make the information more relevant.

We’ve been creating a data visualization solution designed with these principles in mind. It’s called Juicebox. Give it a try.

How to Make Data Actionable

Our office Wheel of Destiny is the most actionable of data. The results tell you where to go to lunch. No questions asked.

Actionable. Is there an adjective that is more fun to put in front of the word data? Perhaps big.

I’m as guilty as anyone. After all, the third step in our data storytelling framework is Action. But what types of actions are we talking about?

One way to think about actionable data is in terms of the direct or indirect actions that can be taken.

Direct Actions

Direct actions are the things we can do immediately with the insights or data. For example:

• An alert can notify someone that they need to react to a change.

• Data can feed into an operational workflow and result in the system reacting, like a loan approval or an automated customer discount.

• A list of customers to be contacted via email with a survey.

• An analysis of marketing data could kick off a targeted digital campaign.

Operationalizing direct actions from your data requires confidence and experience to know what’s important in the data and how you should react.

Indirect Actions

Indirect actions happen when the data moves people toward better decisions -- without necessarily making the decision on the spot. Indirect actions often involve communication and collaboration between people. A few examples include:

• Sharing an insight with executive leadership.

• Capturing an insight that will ultimately act as input to a strategic plan.

• Creating an action item for your team based on the results of an analysis.

• Sending a snapshot of a visualization to a colleague to initiate a discussion.

These types of indirect actions may not be viewed as progress. But they are the essential work require to make an organization smarter. Think of a sales team gathering to discuss a pipeline analysis to see what is working or not. This is an essential step forward and contributing to the decision-making process.

For certain operational activities, the goal should be to drive direct action based on data with scoring or optimization models. On the other hand, your data can also impact decisions that involve more human-involvement and careful consideration — indirect actions.

Consider what kind of action you want to facilitate as the starting point for your analysis or data presentation. At Juice, we design data stories beginning with the question: What actions can our audience take on this data? We want to imagine them getting to an ‘ah-ha’ moment...and then doing something about it.

Better Know a Visualization: Understanding Parallel Coordinates Charts

(With enough visualization methods to warrant a periodic table, it can be confusing to know what to use and when—and which visualizations are even worth considering at all. This series of posts is intended to introduce you to the visualization approaches that we find most useful, practical, and audience-friendly.)

What is a parallel coordinates chart?

Parallel coordinates is a visualization technique used to plot individual data elements across many performance measures. Each of the measures corresponds to a vertical axis and each data element is displayed as a series of connected points along the measure/axes.

Jon Peltier’s chart of baseball players below offers a simple example in Excel.

An example of vehicle performance across multiple measures from the Data Viz Catalogue.

Each line corresponds to a player with performance plotted across four characteristics. Two players have been highlighted to compared values.

Parallel coordinates was invented by Alfred Inselberg in the 1970s as a way to visualize high-dimensional data. These charts are more often found in academic and scientific communities than in business and consumer data visualizations. This isn’t too surprising as parallel coordinate charts can become very dense and difficult to comprehend. Stephen Few has a typical reaction (PDF):

The first time that I saw a parallel coordinates visualization, I almost laughed out loud. My initial impression was "How absurd!" I couldn’t imagine how anyone could make sense of the dense clutter caused by hundreds of overlapping lines. This certainly isn’t a chart that you would present to the board of directors or place on your Web site for the general public. In fact, the strength of parallel coordinates isn’t in their ability to communicate some truth in the data to others, but rather in their ability to bring meaningful multivariate patterns and comparisons to light when used interactively for analysis.

Mr. Few’s final point is right on: with the application of interactive highlighting, filtering, and roll-over detail, parallel coordinate charts can reveal interesting stories in your data.

What problem does this solve?

For most standard charts, there are only so many measures you can effectively show. A typical progression of charts by measures goes like this:

2 measures: Scatterplot

3 measures: Bubble chart

4 measures: Bubble chart with colors

5 measures: Bubble chart with colors and animation

And now you’ve pretty much made an indecipherable graphic. That’s where parallel coordinates can help in showing many measures, limited only by horizontal space.

Like all good visualizations, parallel coordinates can also show both the forest and the tree. The big picture can be seen in the patterns of lines; individual lines can be highlighted to see detailed performance of specific data elements.

What alternatives are there to parallel coordinates?

The most direct alternative to a parallel coordinates chart is a “leaderboard.” Leaderboards also show the performance of many individual items across multiple performance measures. However, leaderboards simply rank the items rather than plotting them precisely on each axis. Here’s an example:

At Juice, we’ve become big fans of the Leaderboard because it offers a couple benefits beyond parallel coordinates:

• Direct labeling of the individual items (and their values) makes it easier to read

• While ranking is less precise, seeing the top 10 performers for each measure can be a good fit for the audience’s needs.

To learn more about Leaderboards, check out this interactive example. Or make your own:

What to watch out for when using parallel coordinates?

With its power to visualize data across multiple measures, why aren’t parallel coordinate chart more popular? Here are a few of the issues:

• Large data sets create a lot of visual clutter. More from S. Few: "Most of us who have used parallel coordinates to explore and analyze multivariate data would agree that meaningful patterns can be obscured in a clutter of lines, especially with large data sets."

• The order of the axes impacts how the reader understands the data. Relationships between adjacent measures are easier to perceive than between non-adjacent measures.

• As the axes get closer to each other it becomes more difficult to perceive structure or clusters.

• Depending on the data, each axis can have a different scale, which is difficult to display and for the reader to absorb.

• Lines may be mistaken for trends or change in values even thought they are only used to show the connected relationship of points.

Parallel coordinates in practice

Protovis: In this example, hundreds of cars can be quickly compared by filtering along any dimension. Click and drag along the red rule for a given dimension to update the filter.

Junk Charts revised a New York Times graphic to come up with this take on a parallel coordinates chart:

Do it yourself in Excel

• Jon Peltier comes through again with a fine Excel tutorial.

Do it yourself with other tools

• Macrofocus uses parallel coordinate visualizations extensively in their products (InfoScope, SurveyVisualizer)

• "GGobi is an open source visualization program for exploring high-dimensional data"

• "FluxViz is a simple cross-platform tool that uses parallel coordinates for the visualization of high-dimensional spaces"

More resources

• Data Viz Catalogue: Parallel Coordinates

• Parallel Coordinates on Wikipedia

• The Parallel Coordinates Approach to Visualisation

• InfoViz.net, Parallel Coordinates article

• Multivariate Analysis using Parallel Coordinates (PDF)

How to Create a Successful Real-time Dashboard

https://parall.ax/blog/view/3045/tutorial-realtime-tv-monitoring-with-raspberry-pi

Real-time dashboards provide a single view to the most important performance metrics for an organization. Data exploration takes a back-seat to a focus on monitoring trends and progress to goals. Real-time dashboards show up on big screens in call centers, monitors in marketing departments, or the desk of a fictitious Private Equity titan on the TV show ‘Billions’.

CREDIT: JEFF NEUMANN/SHOWTIME

The job-to-be-done for real-time dashboards is to monitor status and support immediate decision-making.

The information must be easy to interpret, alert users to problems, and make the next action obvious. In addition to key success metrics, real-time dashboards may show detailed data about the action “on the ground.” Here are eight characteristics that make a real-time dashboard effective:

1. Summary status

The summary status that indicates how things stand overall. Users need to be able to tell at a glance whether they should worry or not. Some dashboards can summarize in a single measure, for example, a “threat level”, that lets everyone know at a glance whether further attention is required.

New Orleans under 'Code Red' level

2. Business drivers

Your dashboard should express a well-understood structure of the business. By the time you design a real-time dashboard, you should have an understanding for how the pieces of the business fit together (i.e. the relationships between key measures, drivers, and available actions). For example, in the call center business, there are clearly defined success measures (e.g. wait time), a mathematical relationship between these measures and their underlying drivers (e.g. call volume), and known levers to address problems (e.g. staffing levels). Your real-time dashboard isn’t for exploratory analysis to find what matters; it is for presenting and emphasizing what matters.

3. Rapid diagnosis of problems

The data presentation should point directly to the likely source of the problem. Real-time dashboards aren’t the place for deep analysis or introspection into the drivers of the business. Here’s a great example of a server management dashboard that makes it immediately obvious where things are going well or poorly.

Dashboard from Motadata

4. Simple data presentation.

Real-time dashboard’s aren’t the place for complex or advanced data visualizations. Imagine you were Napoleon and you had to use a half-completed version of this chart to make a battlefield decision in the next 5 minutes.

Charles Joseph Minard’s famous infographic is better as a retrospective than a real-time dashboard

5. Granular view of the “unit of action.”

Real-time dashboards are about tracking current activity. It may be useful to show the raw data around these events in the form of a ticker, scroll or detailed table. Google Analytics offers a real-time view that lets you view the activity of an individual, random visitor.

Google Analytics

6. Appropriate time window

Getting time right on a real-time operational dashboard is critical. If the measures and trends represent too long a time period, users may not react to changes quickly enough. On the other hand, very small time windows encourage frantic reactions to changes that may not represent real trends. Ideally, the dashboard should offer the ability to configure this time range and “freeze” a moment in time.

7. Prominent but balanced alerts

Naturally, alerting users to problems is a central mission for real-time dashboards. The challenge (as always with alerts) is to balance between “the sky is falling” hysteria and “don’t worry, be happy” apathy. I’ve written before about alerts, but one item to emphasize is the need to show a sense of relative importance. Not all problems have the same impact on the business, and finding a way to communicate this relative importance is valuable.

8. Point to specific action

If real-time dashboards are about identifying and responding to issues, the tool should point users to what they can do about a problem. This can be as simple as displaying the phone number of the right person to call.

If done poorly, your real-time dashboards can create mayhem, but a well-designed dashboard will bring an organization together to focus on the right metrics and ensure rapid reactions to changes.

Better Know a Visualization: Scatterplot

In 2010, I wrote:

With enough visualization methods to warrant a periodic table, it can be confusing to know what to use and when—and which visualizations are even worth considering at all. This series of posts is intended to introduce you to the visualization approaches that we find most useful, practical, and audience-friendly.

Sadly, only two data visualization profiles emerged from that effort: Small Multiples & Parallel Coordinates. Not wanting to leave unfinished business unfinished for much more than a decade, I’m reviving “Better Know a Visualization Series” with an interactive twist:

Why use a scatterplot chart?

A scatterplot chart shows how a bunch of items (e.g. people, places, user segments) compare to each other when plotted by two measures. The items are positioned (or scattered) on a two-dimensional plot to reveal patterns such as:

• Outliers with unusual combinations of values;

• Clusters of items that share similar combinations of values;

• Overall relationships between the two measures (e.g. height and weight are correlated).

Jump into our live Juicebox teaching app to explore a Scatterplot in action…

and see common mistakes people often make.

5 Rules for Successful Success Metrics

Here’s an analytics truism: everyone wants a dashboard (a.k.a. key performance indicators (a.k.a KPIs), success metrics, scorecards). Managers want a barometer of performance, a hammer to use on their subordinates, and a straightforward quantification of their business. Below are a few of the guidelines we use when we take on this task:

1. Actionable metrics

Ask yourself: what would I do if the metric is out of line? Do I have the levers that can impact it? Measures that track final outcomes like revenue or total customers don’t give you much time to react or guidance about what to do next.

2. Less than five.

When I first started at AOL, a friend of mine pointed to the dozens of reports flying around the organization and remarked (I paraphrase): "This many ’important’ metrics just indicates that nobody really understands this business." If you struggle to boil down, you should spend more time defining success and understanding the factors that drive performance.

Sprint Advertising Campaign

3. Simplicity over comprehensiveness

We don’t agree with Thomas Davenport’s call for more proprietary metrics:

You know you compete on analytics when...You not only are expert at number crunching but also invent proprietary metrics for use in key business processes.

In our experience, you’re better off if you choose metrics that can be understood outside your corner of the world. One common trap we’ve seen is a desire to create a single comprehensive metric; this metric is often an index that combines a number of factors into an overall measure of performance. The result: numbers that are meaningless without a lot of context and difficulty in interpreting deltas.

NFL Passer Rating Formula

4. Presentation matters

Your dashboard should be easy to understand and provide enough data to give your audience context. I’ve seen many dashboards that stubbornly show only the current state of a metric and the change from the previous week. Why so stingy with historical data? At Juice, we always show trending and try to give users a means to "cut" the data - by business line, customer type, month, etc. 

Juicebox dashboard

5. Evolve to goals.

Metrics without goals can be a waste. Unfortunately, getting people to agree to specific targets can be painful. After all, goals start us down a slippery slope toward clear accountability. Here’s what I’ve found works: start by focusing your energy on getting people to buy-in to the success metrics. Get clarity on definitions, show trending, and incorporate them into the organization’s vernacular. Be patient: one day someone will raise their hand in a meeting and ask if there are targets for the metrics. Pretend to act surprised by the cleverness of this suggestion.

SMART goal setting

6 Differences Between Data Exploration and Data Presentation

Let’s start by defining our terms:

• Data exploration means the deep-dive analysis of data in search of new insights.

• Data presentation means the delivery of data insights to an audience in a form that makes clear the implications.

Your toolbox for data exploration tools is flush with technology solutions such as Tableau, PowerBI, Looker, and Qlik. "Visual analytics" tools give analysts a super-powered version of Excel for dicing data to facilitate the search for valuable insights. Flexibility and breadth of features is critical; the user needs to handle lots of data sources and doesn’t know in which direction she will go with the analysis.

Data presentation is a different class of problem with distinct use cases, goals, and audience needs. Think about the incredible data stories delivered by the The Upshot, Fivethirtyeight, and Bloomberg. These data journalists often demonstrate data presentation at its finest, complete with guided storytelling, compelling visuals, and thoughtful text descriptions. When compared to these examples, it becomes obvious that the best efforts by a data exploration tool cannot deliver high-quality data presentation.

Data exploration tools generally try to cram all the information on a single page; data presentation needs better flow and explanation to tell the story properly.

You need a specialized solution if you really want to communicate data in ways that engage your audience. To understand the differences between data exploration and data presentation tools, let me offer six key ways that the activities are fundamentally different.

1. Audience — Who is the data for?

For data exploration, the primary audience is the data analyst herself. She is the person who is both manipulating the data and seeing the results. She needs to work with tight feedback cycles of defining hypotheses, analyzing data, and visualizing results.

For data presentation, the audience is a separate group of end-users, not the author of the analysis. These end-users are often non-analytical, they are on the front-lines of business decision-making, and may difficulty connecting the dots between an analysis and the implications for their job.

The needs and interests of a non-analytical manager will be wildly different from the analyst who speaks the language of data.

2. Message — What do you want to say?

Data exploration is about the journey to find a message in your data. The analyst is trying to put together the pieces of a puzzle.

Data presentation is about sharing the solved puzzle with people who can take action on the insights. Authors of data presentations need to guide an audience through the content with a purpose and point of view.

Data exploration is a journey to find truth; data presentation should guide your audience to focus on the most important data and insights.

3. Explanation — What does the data mean?

For the analysts using data exploration tools, the meaning of their analysis can be self-evident. A 1% jump in your conversion metric may represent a big change that changes your marketing tactics. The important challenge for the analysts is to answer why is this happening.

Data presentations carry a heavier burden in explaining the results of analysis. When the audience isn’t as familiar with the data, the data presentation author needs to start with more basic descriptions and context. How do we measure the conversion metric? Is a 1% change a big deal or not? What is the business impact of this change?

Fivethiryeight provides explanation surrounding their visualization to ensure readers understand what they are looking at.

4. Visualizations — How do I show the data?

The visualizations for data exploration need to be easy to create and may often show multiple dimensions to unearth complex patterns.

For data presentation, it is important that visualizations be simple and intuitive. The audience doesn’t have the patience to decipher the meaning of a chart. I used to love presenting data in treemaps but found that as a visualization it could seldom stand-alone without a two-minute tutorial to teach new users how to read the content.

My love for treemaps has been replaced by visualizations (like the leaderboard) that are more immediately intuitive to users.

5. Goal — What should I do about the insights?

The goal of data exploration is often to ask a better question. The process of finding better questions gets to new insights and a better understanding of how your business works.

Data presentations are about guiding decision-makers to make smarter choices. Much of the learning (through data exploration) should be done, leaving the equally difficult task of communicating the insights and the actions that should result.

In all these ways, data exploration and data presentation are different beasts. This is why we’ve chosen to focus on building the best possible data presentation tool, Juicebox.

6. Interactions — How are data insights created and shared?

Data exploration can be a lonely endeavor: Analysts work on their own to gather data, connect data across silos, and dig into the data to find insights. Data exploration is often a solitary activity that only connects with other people when insights are found and need to be shared. That is, when…

Data presentation is a collaborative, social activity. The value emerges when insights found in data are shared with people who understand the context of the business. The dialogue that emerges is the point, not a failure of the analysis.

Finding the Middle Ground: Data Storytelling

There is something between the extreme ends of data exploration and data presentation. We believe data storytelling lies in this intersection. Data stories aren’t entirely about “telling”, nor are they in the wilderness of “finding”. It is the opportunity to explain the data in a guided, narrative way where message meets exploration.

While there are tools for exploration (e.g. Tableau) and tools for presentation (e.g. PowerPoint), it is only recently that you’ve had the change to bring both together in one solution.