Data Discussion Etiquette from Brad Pitt

Before Matt Damon impersonates an investigator in Ocean’s Eleven, Brad Pitt’s character delivers a little pep talk. 

Watch the 40 sec clip:

Rusty Ryan (Brad Pitt) explains the rules of undercover conversation to Linus (Matt Damon). From: Ocean's Eleven (2001)

Now watch it again, but this time imagine yourself giving a pep talk to the next email, powerpoint slide, or dashboard finding that you are about to send out. 

Presumably your data is not meant to distort, yet we can mine the advice here for a few practical communication tips to improve data-informed discussions.  

Let’s break down the key moments.

Be natural.

[Damon takes an unnatural, stiff stance] “No good. Don’t touch your tie. Look at me.”

 

 

His first posture is fidgety and self-conscious with an overly professional stance. 

First impressions are holistic and endure when it comes to perceived levels of interest and credibility. Most of us have an uncanny ability to sniff out a fake, and how data enters discussion is no exception. We’re not computers, so we don’t enjoy an overwhelming data dump of facts, findings, and insights. Two paragraphs and 15 slides in everyone wonders, “Where is this going? What’s the point?” Messages must be clear and focused, but should aim to jettison the unnatural, mechanical chart headings and the unnecessarily encrypted statistical speak. 

Be honest.

“I ask you a question. You have to think of the answer. Where do you look? No good. You look down; they know you’re lying. And up; they know you don’t know the truth.”

 

Be honest with what you do and do not know and what data you do and do not have. Your audience expects to have certain questions answered in order to take your information seriously. Your audience wants to both hear and understand answers to questions like these:

  • How do I know I can trust this data? How was it collected and who was involved?
  • How exactly is this metric calculated?
  • I see the number is X, but how do I know whether that is good or bad? 
  • What’s the history of this number and the frequency of its collection? How quickly does this number usually change? How long does it typically take to influence it in the future? 
  • How does this compare to other locations with similar attributes?
  • Why is this useful for me to know? How will it change what I care about?

These questions aren’t novel. They follow the 5W’s basics. Yet they are often either left out or overcomplicated in most data discussions. The goal here is to acknowledge these needs in the simplest, most useful way.

Start with a (very) short story.

“Don’t use 7 words when 4 will do.”

 

 

 

With data, as with words, precision is as much an art as a science. Still, helpful tools exist. Ann Gibson wrote a relevant post and I highly recommend reading the article for all the details, but here’s the magical excerpt:

Once upon a time, there was a [main character] living in [this situation] who [had this problem]. [Some person] knows of this need and sends the [main character] out to [complete these steps]. They [do things] but it’s really hard because [insert challenges]. They overcome [list of challenges], and everyone lives happily ever after.

The beauty of this frame narrative is that it provides a structure for those who are too long-winded to focus on the essence of their own message, and it helps others whose ideas tend to dart all over the place to preserve a sequential flow.

Each of these [placeholders] are candidates for data context that help satisfy the previous "Be Honest" section. I mocked up a quick scenario that demonstrates a short story with useful data context:

Set your mark.

“Don’t shift your weight. Look always at your mark but don’t stare.”

 

 

You’ve likely heard of S.M.A.R.T. goals before, but are your charts smart? Something as simple as a target value by a specific date on a chart can work wonders at moving towards something tangible. People crave purpose, so set and communicate your goals. But don’t be that presenter who stares incessantly at your metrics and goals. 

Be enjoyably useful.

“Be specific, but not memorable. Be funny, but don’t make him laugh. He’s got to like you; then forget you the moment he’s left your sight.”

 

 

Jazz it up,” “Make it shine,” and “Make it pretty” are all phrases you’ve either heard or used yourself. Few situations are more disappointing then when a company tries to overcompensate with their insufficient, irrelevant data by lathering on the “wow factor.” Don’t succumb to making your data memorable for the wrong reasons. For business the goal isn’t memorable chart-junk, but that does not mean your data should be lifeless and shallow.

Don’t leave people hanging.

And for God’s sake whatever you do, don’t, under any circumstances…”

 

 

The worst move you can make is to omit the call to action. End with clear next steps, key questions posed, or an action button that allows your audience to engage with immediacy, while your solid ideas are fresh and ripe for action.

Thirsty for more? Check out these related blog posts:

Data is the Bacon of Business: Lessons on Launching Data Products

Last week was the 4th annual Nashville Analytics Summit. The event has grown from 150 participants three years ago to 470 this year. I took the opportunity within this friendly analytics community to share our latest thinking at Juice. Last year I spoke about "Beyond Data Visualization: What's Next in Communicating with Data”. This year my talk was entitled “Launching Data Products for Fun & Profit”. I started with a simple premise: Data is the bacon of business. I’ll let Jim Gaffigan explain:

His logic works for data, too.

We've had a front-row seat as our clients have transformed their data assets into revenue-generating data businesses. But launching successful data products isn't simple. And it is a far cry from your typical reporting or self-serve BI solutions — the insight-free data delivery vehicles of the past. I’ve posted the slides from my talk here:

Here are a few highlights:

  • Data products are happening now. Big technology companies are making massive investments in pursuit of better data sources for their products. IBM spent billions for The Weather Channel to enhance Watson Analytics. Google bought Waze for crowd-sourced traffic data. Microsoft wanted LinkedIn’s “economic graph” so badly they spent $26 billion.
  • The best data product stories start with a visionary leader. Our clients aren’t just thinking about fancier visualizations. They want to transform their businesses by making their customers smarter and more successful through data.
  • My friend Oli Hayward of Hall & Partners provided some valuable lessons from launching a world-class market research analysis portal. He explained the need to start by selling to internal audiences and targeting only the most innovative clients (we’re in the same boat there).
  • Data is an imperfect reflection of reality. When you present data to customers, prepare to discover exactly how imperfect it is. Which led me to this joke...

If you’d like to hear more about our lessons learned from dozens of data product launches, send us a note at info@juiceanalytics.com.

The Jury's In: Findings from User Research

We made it our goal this summer to hear back from prospective users of our research application about how they would use the app to address various hypothetical issues in their day-to-day workflow. After asking a couple thousand departmental leaders to put themselves in situations that would lead them to use our app to address a need, we presented them with three different scenarios, ranging from grant proposal preparation to tenure decisions. We got some very interesting responses that we believe are applicable to how people use all different types of data products and reporting solutions. Here are our findings.

Benchmarks and Discussions - Specific to the research app, we found that when department heads go to write a grant proposal, they prefer to communicate with peers and use their peers' previously successful grant proposals as a benchmark of the quality that a particular sponsor expects from a proposal. 

Similarly, users of our Healthcare app also connect with their coworkers about training assessment and work performance. They too use their peers' experiences and expertise as a barometer for their own performance in training and in their work. Our chat feature that's built into Juicebox applications does a great job of facilitating discussions right in the app, so you can highlight metrics, share them, and start a conversation

Our chat feature in action

Our chat feature in action

Performance Measurement - Specific to the research app, we found that department heads take their faculty's research activity very seriously. In fact, they consider a faculty member's research activity to have a greater influence on their promotion and tenure decision than teaching evaluations, service, and the opinions of other faculty members in their department.

At Juice, we are no stranger to performance metrics. Managers in all types of industries use our apps to measure the performance of their employees for promotion decisions and general review purposes. We take measuring performance to the next level by giving our users seemingly unlimited ways to filter the data.

An example of research performance measurement

An example of research performance measurement

By listening to the needs and preferences of our users, we've created our apps to enable users to analyze peer performance within their institution and communicate with each other seamlessly. This takes the guesswork out of with whom to consult and what to seek from those data-enabled conversations. To get a taste of how you can get rich insights out of Juicebox, check out a quick demonstration of our research application or schedule a demo.  

Office of Research Application Preview

Imagine you're a researcher at a top university. In addition to conducting innovative projects, it's your job to work with research administrators to create proposals and receive funding. But how do you go about finding sponsors?

Our Juicebox Office of Research Applications removes the guesswork and makes it easy for researchers and administrators to communicate and successfully find sponsors and create grant proposals. Watch the video below for a quick taste of exactly how it works - from quickly sorting through information and making selections, to communicating with co-workers within the app.

Thirsty for more information? Send us your questions at info@juiceanalytics.com or for a more in-depth look schedule a personalized demonstration.

A look at our latest visualization

At Juice, we recognize the importance of design and visualization in making you successful with your data. In fact, it's the design and functionality of visualizations that bring your data to life so we are always working on new and exciting ways for people to explore data and gain deeper insights. 

A common desire when examining data is an eagerness to dive deeper. Simply knowing the answer to a question isn't always enough - sometimes you want to know the ins and outs of "why". Take a metric for example. Knowing your sales number is great, but context is equally as important. Is that number higher or lower than last month? Where did the sales come from? Is there potential for growth with new customers? 

For example: when I go to Google Maps, I am usually looking for a good place to grab a meal, find a friend’s house, or maybe a local park to take my daughter to. Once I have located where I want to go, I usually zoom in to see what area of town it is in. After I get an idea for where it is generally located I’ll usually want to go deeper to see if I am familiar with that area of town. Lastly, and this may just be me, I switch to street view so that I can see what the area looks like, occasionally you will see individual people walking on the street, running, or maybe eating on a patio somewhere. The idea behind Google Maps is that you can see clearly from any level; from 20,000 all the way down to 20 feet.

At Juice, we wanted to mimic the behavior of diving deeper with our new visualization. It's appropriately named "Bubbles" and is a visual way to get an enterprise view of a large set of data - staffing data, in this case. If you are a leader of a large organization, we have created a way for you to - like a Google Map - get an enterprise view of your organization with the unique ability to drill into different departments, supervisors and individual employees. Interested in understanding the reporting relationships at a deeper level in your organization? This visualization can walk you through these relationships to discover hotspots where your organization can optimize the workforce.

We are passionate about helping businesses discover new insights in their data in creative ways and this is just one of the latest features. For more on our product and all that it offers, get in touch with us. We'd love to have a conversation about how to help you move your business forward.

Choosing the Right Proposal Measure

Folks in the research administration community are talking more and more about data management and reporting at their respective universities. When we talk about data, we also need to talk about metrics. Tracey Robertson, the Director of Sponsored Research Accounting at Princeton University tells us that choosing the correct metric can:

  1. Change behavior
  2. Drive performance
  3. Support investments

Failing to choose the right metric to present research activity data will not only confuse people, but will also lead to missed opportunities and a failure to answer important questions that researchers and campus leaders may have.

A couple of years ago we wrote an article about using the right metric for your data presentations, and people really loved it. It’s summarized by this diagram:

However, we wanted to make it “real” for our research community so we decided to give some more insight on how we used these concepts to design our office of research reporting application. Here’s what we came up with.

Actionable

To make a metric actionable, start by making sure it accurately addresses a real question or need. If your goal is to create a report on how successful a college or department is in getting funding for their proposals, your report would be lacking if you only included number of awards received in this performance metric. Why? Because this metric alone does not adequately capture proposal success. Including the number of proposals submitted as a reference to the number of awards granted captures the performance metric and accurately addresses the need. Here are the metrics we selected:

To enhance the actionability of these metrics, we also added the change from the previous month for each metric. In this example, for instance, the number of proposals was down 157 from the prior month. This gives the users some insight into context and hotspots for follow up action.

Additionally, when a user selects a metric, other information on the page (such as trend over time, or breakout by sponsor) is updated to reflect more detail on that selection. Interesting detail means action.

Common Interpretation

Your metric should be one that everyone can easily understand without much thought. Keep in mind that some (if not most) of the people to whom you are reporting your school’s funding data are not analytical experts. Think layman's terms here.

In the Research app, we made sure the labels of the metrics were simple, common and easily understandable. The labels “Proposals” and “Proposal Dollars” clearly represent what they mean and are common to the lexicon of our targeted users.

Additionally, we wanted to make sure there is a delineation between proposal and award metics by separating the key metrics into two representative rows, using the gestalt rules of association to connect the related metrics.

Accessible, Credible Data

A good metric is one that should be easily accessible and tenable. Many schools run into the issue of being able to track down and organize the data for their grant funding activity reporting.  

The platform that we used to create our research application (i.e., Juicebox™) is based on the premise of accessibility. But the credibility factor is tied to the data. Make sure that the data that you use to calculate your metrics is well understood and comes from a respected source. A good litmus test is to ask the question to your users: “If you wanted to know the number of awards, where would you look to figure that out?” Your data source selection means more if people confirm your source as one they’re already trusting for their work.

Transparent, Simple Calculation

When an administrator, dean, or professor looks at the reported metrics, they should be able to recognize how your team reached that value and what it represents. If they cannot decipher how it was calculated you lose credibility and gain confusion.

The metrics we selected for our research application are what we call “simple metrics” in that they are not complex assemblies of multiple metrics (otherwise known as composites, indexes, or franken-measures.) But to make sure the selected metrics are as simple as possible we narrowed them down to core concepts that people understand: the number of proposals and awards, and the dollars associated with proposals, awards and expenses — concepts most anyone in the research world readily understand.

Want to see more about useful proposal metrics?

We’ve taken the principles illustrated in this article and beyond and have applied them to our own product that can deliver accessible and actionable data insights to anyone who uses it. Check out the demo video.

Video: Turning Data into Dollars

When someone says the words "company data", what comes to mind? If you're like most people, you probably picture endless spreadsheets, stacks of reports, and multiple analyses all used to make decisions within your organization. What if you were able to take that same data and reach external audiences, while generating profit as you did?

Data monetization, or the process of turning your data into dollars, is doing just that. It can be difficult knowing where to start, so we've laid out some of the best practices on building data products and monetizing your data. Watch the video below (or download the slides) to learn the four main steps to data monetization, how to communicate with different audiences, familiar examples of data products, and much more.

Want to know more? Check out some of the success Juice has had in the past building data products, or send any questions to us at info@juiceanalytics.com.

Success Story: Predikto Is Right on Track

Ever been in this situation? Your organization generates massive amounts of data critical to its success and you share it across your organization, but it's not being used successfully. The visuals showing the insights aren't clear (or worse - they're buried in a spreadsheet), people can’t make heads or tails of it and as a result you don't hit your business goals.

If that sounds familiar, you’re not alone. Predictive analytics company Predikto found themselves facing the same problem. Their data product was being used to anticipate when railroad hot box detectors (or HBDs - monitors that detect train failure) would malfunction. The goal was to be able to get a maintenance crew out to fix an HBD before it could malfunction and stop any trains, costing Predikto's client big bucks. But with multiple tracks throughout the country and massive amounts of data being generated, crews weren’t able to make sense of the data and get to the problematic HBDs in time.

After evaluating their different options, Predikto chose to implement Juicebox to visualize the information in a simple and actionable way. Using the data product, maintenance crews are now connected to HBD health-check displays, making it easy to identify potentially problematic HBDs and fix them before they can breakdown. Juicebox provided Predikto with the tools to save time, money, and most importantly, their workers’ sanity.

Want to know more about Predikto and their data visualization challenges? Download the official case study below. Or if you'd like to know more about how Juicebox can help you communicate with your end users, drop us a line at info@juiceanalytics.com.

How to Build Better Data Products: Getting Started

This is the first in a multi-part series on launching successful 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?

“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.

Next up: A design approach that leads to high-impact, high-value data products.

Creating Annual Reports People Love to Read

It's no secret: annual reports are typically a pain to create and dull to read. They're one of the best opportunities we have to share everything we've done in the past year with people, so why is it that so often they fall flat?

We've found that there are a few things that can really make or break annual reports. Design, layout, and voice are just some of the things that all go into making annual reports that are not only easily understandable, but that people enjoy reading. A few weeks ago, we hosted a webinar (link to webinar at the bottom of the post) with our nine-and-a-half steps to making your data delicious and how to take your annual reports from "yuck" to "yum." Throughout the webinar, we surveyed attendees to get a better idea of their annual report practices and pains. Here's a breakdown of what we asked and the answers we received. They shed some light on current practices, and help to figure out what the future holds for annal reports.

Question 1: Does your annual report allow people to understand and act on the data?
We found that most people are dissatisfied to some extent with the clarity in their reports. It's not a new finding: confusion created by data has been discussed in multiple business and tech journal articles, and demonstrates the need for clear, concise, and direct communication in annual reports (skip to 6:22 in the video for more on using language effectively in reports).

Question 2: Is color used effectively?
If you're a long-time reader of the Juice blog, you'll know that color has meaning and is essential when sharing information. We found that most people use color in their annual reports, but realize that it's an important tool and want to know more about how best to utilize it. For more on the subject check out Juice's collection of design principles, many of which focus on color use in reporting.

Question 3: Do you see utility in using an online, interactive annual report?
The results of this question were overwhelming: attendees preferred online, interactive reporting over more traditional methods such as Excel or PowerPoint and printed reports. While there are different pros and cons to making the switch to online annual reports, it's important to note that in a few years online annual reports could be the standard (see more on the subject by skipping to 29:20).

If you'd like to talk more about annual reports, or data reporting in general, we're always around to chat. Take a look at your schedule and set up a time that works for you, or send us a message at info@juiceanalytics.com. Happy reporting!