Is It Time to Jump-Start Your Data Offense?

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Legendary Alabama coach Bear Bryant believed in defense:

“Offense sells tickets, but defense wins championships.”

Legendary boxer Jack Dempsey saw virtue in offense:

"The best defense is a good offense.”

Legendary analytics guru Thomas Davenport takes a more neutral stance in his Harvard Business Review article What’s your Data Strategy?

"The key is to balance offense and defense.”

Davenport goes on to say:

“Data defense is about minimizing downside risk, including ensuring compliance with regulations, using analytics to detect and limit fraud, …and ensuring the integrity of data flowing through a company’s internal systems.

...Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction.

…The challenge for CDOs and the rest of the C-suite is to establish the appropriate trade-offs between defense and offense and to ensure the best balance in support of the company’s overall strategy.”

Balance is fine. But at Juice, we’re all about building data products. That’s an offensive data strategy (we’re with you Jack Dempsey, June Jones, Mike Leach, and Mike D’Antoni).

In practice, most organizations start from a defensive crouch. The relevant question is: when is it important that you shift to a more offensive data strategy?

Davenport shares a few indicators that suggest more data offense is warranted. For example, offensive strategies are often employed at organizations that operating in largely unregulated industry where customer analytics can differentiate. He also sees opportunity for offensive data strategies at that those organizations with decentralized IT environments and where “Multiple Versions of the Truth” are encouraged.

His HBR article even provides an evaluation tool to determine whether your organization has shifted its balance toward offense or defense, giving you a snapshot of where you’ve (organically) evolved. It doesn’t tell you where you should be.

When we think about the dozens of companies we’ve worked with who are launching data products, some common patterns emerge in terms of the characteristics of those organizations. Here are four categories where an offensive data strategy provides like a good fit:

Government, non-profit or public-service organizations

These organizations aren’t necessarily in the “competitive” markets that Davenport describes. Nevertheless, they are sitting on tons of valuable data that can shape conversations and influence the decisions of their constituents. We’ve worked with Chambers of Commerce, Universities, and State Departments of Education that are taking on offensive data strategies.

Data science startups

There are hundreds of start-ups who are building their businesses on offensive data strategies. These companies have mechanisms for collecting data across an industry and are adding value through predictive algorithms, identifying patterns, and ultimately helping their customers make smarter decisions. We’ve working with a couple healthcare start-ups who have proprietary methods for predicting performance of healthcare providers. This is deeply valuable information for health systems and employers, and a purely offensive strategy.

Consultants

We’ve seen a couple different offensive data strategies by consulting firms. First, if they are delivering a project with an analytical deliverable, why not make the deliverable a recurring data solution? Another approach by the most innovative consultants is to view data collection and data products as an opportunity to proactively identify problems for clients. An annual survey of customer brand awareness can be turned into an incisive discussion starter, spurring clients to pursue the next project.

Companies with dominant market shares

If you are a market leader, you may be collecting enough data from your customers to be able to provide benchmarking solutions. In some cases, this offensive strategy is core to the original purpose of the business (e.g. US News & World Report’s surveying of colleges). In other cases, the opportunity to create new data products can be a result of “data exhaust”.

If you find yourself wondering how your data might be turning into a revenue-generating or customer-differentiating solution, you should download our ebook Data Is the Bacon of Business: Lessons on Launching Data Products.

Is Your Data Product Ready for Launch?

Looking to transform your data into a valuable, customer-facing data product?

From concept to design and launch, we've worked with dozens of companies to create successful data products. Our checklist provides seven evaluation criteria to see if your data product has what it takes to succeed.

Does your data product...

  1. Solve a distinct problem?

  2. Meet users where they work?

  3. Guide users to insights and actions?

  4. Make users feel safe and in control?

  5. Bring credibility to your data?

  6. Have the ability to operationalize the solution?

  7. Support customers for success?

Download the PDF here.

Specificity is the Soul of Data Narrative

The folks in the front of the room stared with a forced intensity at (what must have been) the 23rd straight slide showing data about website performance. Their glazed eyes would have been entirely evident if the speaker wasn’t so intently focused on pointing out the change in bounce rate between August and July. In the back of the room, Brian wasn’t able to summon the energy to care. The gentle hum of laptops, dim lighting, and endless onslaught of data practically begged his mind to wander...

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Specificity is the soul of narrative

This is a frequently-repeated lesson from John Hodgman's excellent podcast Judge John Hodgman. His fake Internet courtroom demands that its litigants share specific information and stories to bring their arguments to life.

Unfortunately, this lesson is often lost when people use data to communicate. Which is not to confuse detail for specificity. Detail — at least in the data communication context — simply means the access to more and more granular data. Specificity requires something more: delivering information that is familiar to your audience, letting them connect with the subject matter at a more personal level. The data is no longer an abstraction, it is something tangible and real.

How do we deliver more specificity in our data stories? Here are three ideas:

  1. Remind your audience of the people behind the data

  2. Begin with an individual story

  3. Explore individual patterns and behaviors

1. Remind your audience that we are talking about individual people or things.

Data is an imperfect reflection of activity in the real world. You want to find ways to emphasize the connection between real people and the data points shown on the screen. A few examples:

 Use icons as a subtle reminder that we are talking about people

Use icons as a subtle reminder that we are talking about people

 Use images of people to humanize the data

Use images of people to humanize the data

 Use individual components (people) to compose the visualizations. A tradition bar chart is transformed into a stack of the individual units.

Use individual components (people) to compose the visualizations. A tradition bar chart is transformed into a stack of the individual units.

In one memorable meeting, I was demonstrating our workforce analytics solution to a prospective client. I was showing the distribution visualization (above) and was careful to roll over individual people to help explain its meaning. As I was highlighting an employee with 40 years of experience at their company, an executive burst out: “Wait a second, that woman was my elementary school teacher.” The data came to life for him that day.

2. Begin with individual stories before showing the big picture.

One of the all-time best specificity-is-the-soul-of-narrative visualizations is the Gun Deaths visual created by Periscope. Take a moment to experience it.

 To create emotional impact from the data, the designer starts this visual by showing one gun death at a time.

To create emotional impact from the data, the designer starts this visual by showing one gun death at a time.

 Gradually the animation speeds up until the viewer understands the terrifying weight of the many lives cut short.

Gradually the animation speeds up until the viewer understands the terrifying weight of the many lives cut short.

Your data story may be on a more banal topic, but there are still ways to show the individual stories. What does a prototypical conversion in your sales pipeline look like? What is the financial impact of an individual patient going to an abnormally expensive healthcare provider?

3. Provide your audience with the ability to dive into many individual patterns and behaviors.

One compelling anecdote may hook your reader; the ability to see many stories can provide a powerful tool for analysis.

A long time ago we introduced the concept of customer flashcards — visualizations that tell the story of individual people or things, create a language for reading behavior patterns, and the opportunity to flip through many of these visuals. Finding patterns doesn’t have to be the exclusive domain of machine learning — as humans, we are pretty good at seeing and interpreting patterns ourselves. 

Here’s an example from a project we did to see patterns of online learning. Once we found an effective way to show how students took courses, we quickly identified common behaviors that would have been lost in the typical summarization of data. 

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Data storytelling is still finding its fundamental principles and discovering how effectively impact readers. Bringing specificity into these data stories may just be a bedrock principle that we can adopt from a wise Internet judge.

Education Leaders Embrace Data Storytelling

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The Data Storytelling Revolution is coming to the K-12 Education world -- in its own unique way. Two days at the annual National Center for Education Statistics STATS DC Data Conference in Washington DC gave me an up-close view of how education leaders were using data to drive policy and understanding school performance. This insiders view was thanks to an invitation by our partners at the Public Consulting Group, one of the leading education consulting practices in the country.

After attending a handful of presentations and hanging out with industry experts, here are a few of my impressions:

Education leaders have a fresh energy about data visualization and data storytelling.

To start with, the conference was subtitled: “Visualizing the Future of Education through Data”. To back this up, the program featured more than a dozen presentations about how to present data to make an impact. There was good-natured laughing and self-flagellation about poor visualizations, and oooh's and aaah's at good visualizations. There was also a genuine appreciation for how important it is to “bridge the last mile” of data to reach important audiences.

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Unsurprisingly, Educators understand the need to reach and teach their data audiences.

For many of the attendees, their most important data audiences (teachers, parents, school administrators) are relative novices when it comes to interpreting data. There was a general appreciation that finding better ways to communicate of their data was paramount. The old ways of delivering long reports and clunky dashboards wasn’t going to suffice. The presenters emphasized “less is more” and the value of well-written explanations. I even ran into a solution vendor committed to building data fluency among teachers.  This sincere sensitivity to the needs of the audience isn’t always so prevalent in other industries.

Data technologies and tools take a backseat to process, people, and politics.

On August 20th and 21st, I’ll see you at the Nashville Analytics Summit. When I do, I bet we’ll be surrounded by vendors and wide-eyed attendees talking about big data, machine learning, and artificial intelligence. Not in the Education world. After the lessons of No Child Left Behind and years of stalled and misguided data initiatives, Education knows that successful use of data starts with:

  1. Getting people to buy-in to the meaning, purpose, and value of the data;
  2. Establishing consistent processes for collecting reliable data;
  3. Navigating the political landmines required to move their projects forward.

The Education industry is more focused on building confidence in data, than in performing high-wire analytical acts.

Education has not yet found the balance between directed data stories and flexible guidance.

I sat in on a presentation by the Education Department where they shared a journalism-style data story that revealed insights about English Learners. There website was the first in a series of public explorations of their treasure-trove of data.

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On the other extreme, the NCES shared a reporting-building engine for navigating another important data set. On one extreme, a one-off static data story; on the other, a self-service report generation tool. The future is in the middle — purposeful, guided analysis complemented by customization to serve each individual viewer. The Education industry is still finding their way toward this balance.

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Every industry needs to find its own path to better use of data. It was enlightening for me to see how a portion of the K12 Education industry is evolving on this journey.

Data Storytelling: What's Easy and What's Hard

Putting data on a screen is easy. Making it meaningful is so much harder. Gathering a collection of visualizations and calling it a data story is easy (and inaccurate). Making data-driven narrative that influences people...hard.

Here are 25 more lessons we've learned (the hard way) about what's easy and what's hard when it comes to telling data stories:

Easy: Picking a good visualization to answer a data question
Hard: Discovering the core message of your data story that will move your audience to action

Easy: Knowing who is your target audience
Hard: Knowing what motivates your target audience at a personal level by understanding their everyday frustrations and career goals

Easy: Collecting questions your audience wants to answer
Hard: Delivering answers your audience can act on

Easy: Providing flexibility to slice and dice data
Hard: Balancing flexibility with prescriptive guidance to help focus on the most important things

Easy: Labeling visualizations
Hard: Explaining the intent and meaning of visualizations

Easy: Choosing dimensions to show
Hard: Choosing the right metrics to show

Easy: Getting an export of the data you need
Hard: Restructuring data for high-performance analytical queries

Easy: Discovering inconsistencies in your data
Hard: Fixing those inconsistencies

Easy: Designing a data story with a fixed data set
Hard: Designing a data story where the data changes

Easy: Categorical dimensions
Hard: Dates

Easy: Showing data values within expected ranges
Hard: Dealing with null values

Easy: Determining formats for data fields
Hard: Writing a human-readable definition of data fields

Easy: Getting people interested in analytics and visualization
Hard: Getting people to use data regularly in their job

Easy: Picking theme colors
Hard: Using colors judiciously and with meaning

Easy: Setting the context for your story
Hard: Creating intrigue and suspense to move people past the introduction

Easy: Showing selections in a visualization
Hard: Carrying those selections through the duration of the story

Easy: Creating a long, shaggy data story
Hard: Creating a concise, meaningful data story
 
Easy: Adding more data
Hard: Cutting out unnecessary data

Easy: Serving one audience
Hard: Serving multiple audiences to enable new kinds of discussions

Easy: Helping people find insights
Hard: Explaining what to do about those insights

Easy: Explaining data to experts
Hard: Explaining data to novices

Easy: Building a predictive model
Hard: Convincing people they should trust your predictive model

Easy: Visual mock-ups with stubbed-in data
Hard: Visual mock-ups that support real-world data

Easy: Building a visualization tool
Hard: Building a data storytelling tool

Let's Meet Up at the Nashville Analytics Summit

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The Nashville Analytics Summit will be on us before we know it. This special gathering of data and analytics professionals is scheduled for August 20th and 21st, and should be bigger and better than ever. From my first experience with the Summit in 2014, it has consistently been a highlight of my year. My first Summit took place at the Lipscomb Spark Center meeting space with about a hundred attendees. Just a few years later, we'd grown to more than 450 attendees and moved into the Omni Hotel.

Mark it on your calendar. I'll give you five reasons why it is a can't-miss event if you work with data:

  1. We've invited world-renowned keynote speakers like Stephen Few and Thomas Davenport. You won't believe who we are planning to bring in this year.
  2. There isn't a better networking event for analytics professionals in our region. Whether you're looking for talent or looking for the next step in your career, you'll meet kindred spirits, data lovers, and innovative businesses. For two years in a row, we have hired Juice interns directly from conversations at the Summit. 
  3. It's for everyone who works with data. Analyst, Chief Data Officer, or Data Scientist... we've got you covered. There are technical workshops and presentations for the hands-on practitioner and case studies and management strategies for the executive. We're committed to bringing you quality and diverse content.
  4. It's a "Goldilocks" conference. Some conferences go on for days. Some conferences are a sea of people, or too small to expand your horizons. The Analytics Summit is two days, 500-something people, and conveniently located in the cosy confines of the Omni Hotel. It is easy to meet new people and connect with people you know.
  5. See what's happening. Nashville has a core of companies committed to building a special and innovative analytics community. We have innovators like Digital Reasoning, Stratasan, and Juice Analytics. We have larger companies making a deep commitment to analytics like Asurion, HCA, and Nissan. The Summit is the best chance to see the state of our thriving analytics community.

Now that you're convinced you can't miss out, you're may wonder what to do next. First, block out your calendar (August 20 and 21). Next, find a colleague who you'd like to go with. Want to be even more involved? We invited dozens of local professionals to speak at the Summit. You can submit a proposal to present

Finally, if you don't want your company to miss out on the opportunity to reach our entire analytics community, there are still slots for sponsors.

I hope to see you there.

Data Monetization Workshop 2018: Key Themes & Takeaways

“Data Monetization is a hot topic because it has two words that everyone loves. We all love data, and who doesn’t want to monetize something?”

These were the words that kicked off the 2018 Data Monetization Workshop to a roomful of attendees and industry experts who had gathered to discuss the question that followed this observation: what does Data Monetization actually mean?

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This question was discussed at length over the course of the half-day event and was the impetus for speaker topics related to using data for social good, how to account for data on a balance sheet, how AI will affect the future of Data Monetization, and more. Here are some of the most important themes and takeaways from the discussions of the day.

What Is Data Monetization?

  • Data monetization is about data value, not data dollars. It’s not about selling customer lists, but about deriving value.

  • Data Monetization encompasses business intelligence and takes a much broader perspective on what can be done with data. Analyzing what options exist outside the enterprise, what products and services can be created using data, and trying to get data into the hands of decision-makers are all components of Data Monetization.

Data for Good

  • Most organizations aren’t trying to sell your personal data; they’re focused on using information to improve city performance, prevent mass shootings, and rescue people from sex trafficking.

  • Nobody owns data. Companies and organizations have rights to data, but in order for progress to be made data must be shared and communicated.

The Dark Side of Data

  • While data offers many beneficial opportunities, there also exists a dark side of data. What complications does something like what happened with Cambridge Analytica have on future opportunities for Data Monetization?

  • Using certain data is not always a question of “Is this legal?” but rather “Is this ethical?” Sometimes data is available but not right to use, which can feel like a restraint at times but leads to being an organization being perceived as trustworthy. It is important to have a solid core philosophy on what data you do and don’t use before it becomes necessary to bring in lawyers and PR teams.

Education, Train, Explain - Data Literacy

  • Poor data literacy is seen across the board. If you don’t read the fine print, you can sign your data rights away. Many problems with the use of personal data are often due to mismatched expectations.

  • People don’t always understand how valuable data is and what an asset they hold. You have to teach people to think in technicolor. Some companies try to exclude information, but more information changes the landscape and provides more context.

  • Creating data products with different derivations is one way to communicate data to different roles (e.g., an analyst versus a CEO). You have to meet people where they are.

  • Being transparent with a product roadmap is a great way to demonstrate to people that data products will look different as time goes on. Users can know what features they can expect and when.

Doing Things Differently and Looking to the Future

  • There are emerging technologies that can help make processes easier. Right now you just have to ask yourself, “How can I do things a little bit differently today?”

Doug Laney Is One Cool Dude

  • Doug Laney was kind enough to join us remotely from his vacation to answer audience questions about his book Infonomics -- of which every audience member got a free copy!

Special thanks to all of the speakers, to MapR for sponsoring the post-workshop networking reception, and to everyone that attended! If you have questions or comments about the Data Monetization Workshop, feel free to reach out to info@juiceanalytics.com.

Related Reading:

4 Steps to getting started with data products

Over the years, we’ve had the pleasure to work with many great individuals and companies and through our work have gained the ability to sympathize with their experiences of what we like to call “going from 0 to 100."

No, we’re not endorsing excessive speeding in your car. We’re talking about going from having nothing but hopes and dreams about delivering engaging analytics (0) to having an interactive data story that your users don’t want to put down (100).

Because we’ve focused our efforts on taking clients from 0 to 100, commonalities or trends for best practices in the data and design experience (read: everything between 1 and 99) have become increasingly clear. Use these four tips to make your introduction to data products a better, more frictionless experience.

1. Know your audience

  • What do the end users you have in mind for the product look like? What questions will users ask of the data? What actions will they take with the answers to these questions? These are all things you should know before beginning to work on data products.
  • Be specific about for whom you are creating a data product. If you try to provide insights for too many types of business roles you run the risk of making it too broad for any role to gather insights from the data.

2. Gather the right data

When putting together the data to be used in your product, it’s important to discern the difference between “more data” and “more records."

  • More data: It’s not always in your best interest to gather the most “data” possible. By doing this, you run the risk of gathering data that you may not use and wasting money in the process.

  • More Records: Gathering “more records” (read: rows of data) is a better strategy as you prepare for your data product. Doing so can alleviate the effects of outliers and unearth trends in the data.

3. If you’re new to the data, begin with an MVP (Minimum Viable Product) and let your users determine what features should be included

Building out all the bells and whistles you think you might need at the beginning the data product’s life can be expensive. Starting with an MVP that is put in the hands of actual end users will help determine what data is actually needed and what design aspects are best for your purposes.

  • Helps with data: Starting with an MVP helps determine the shape and caveats that exist within your data, and allows your users to make decisions about what data is most important to them.

  • Helps with design: By starting with an MVP, all of the questions that you and your users have for the data are answered by the design. Additional features can then be added from that point on in a more cost-effective manner.

4. Be open-minded about visualizations

  • We won’t get into data visualization principles in this section because that warrants a totally separate article, but a simple point here: just because you saw similar data in a pie chart once doesn’t mean that is the only (or best) way to visualize your data.

  • Because your users are the ultimate consumers of the data, let them be the judges of what visualizations will be most effective for them.

Easy peasy, right? We think so, but maybe that’s only because we’ve helped so many customers get from 0 to 100. If you're still not sure what your next steps should be, we’re here to help. Learn more about our 0 to 100 process by checking out the document below.

What's in a Juicebox: Connected Visuals

The ability of an excel novice (i.e. me) to use a pivot table is basically naught. My ability to manipulate data does not exist, and yet I work for one of the most forward-thinking data presentation companies! Nevermind why I was hired, I quickly learned how to use a Juicebox application because Juicebox is designed with the everyday end user in mind. We have tackled the problem of data delivery to both analytical and non-analytical groups. In this post, I want to chat about one of the features that make that possible: connected slices. What is a slice? A slice is a Juice term for a data visualization within a section of Juicebox application.

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I have mentioned before that Narrative Flow is important to Juicebox. Our applications are web-based and users expect to move and navigate from top to bottom, like when interacting with a webpage. Part of that movement from top to bottom in Juicebox means that as the user is making selections within the application, those selections should not only carry down the page but that they should also inform the visuals that follow.

We strive to be the world's best platform for telling data stories and because of that connecting our visuals together is vital. When someone makes a selection in the topmost slice, it places a filter on the data and the selection they make. This filter helps the user narrow down their selection and drill into the data.

Much of the problems with static reports and dashboards is that they only give the user a top-level view of his or her data. Traditional solutions do not provide the ability to drill further to discover what factors could be driving the data. In essence, today's charts, dashboards, reports, and BI solutions give the user a snapshot and not the whole story. 

Curious to see what else is included in Juicebox? Check out some of these posts highlighting other unique features:

Getting unstuck: Give your data a jumpstart

It’s a predicament that we’ve seen many times over: your data is stuck. You’ve tried some reporting through some Excel pivot tables, or you’ve messed around with a Tableau trial, but felt like there wasn’t enough engaging content to get your users excited. Rationalizing why you can’t get your data to be impactful for your business, you think things like, “maybe my users are talking about the data but I just don’t know about it” or “maybe the data isn’t structured in a way that allows for valuable insights to be extracted from it."

If you’re sitting there thinking that your mind is being read by our artificial intelligence, you’re wrong. It's because at Juice we have seen this scenario played out too many times and we’ve made it our mission to make these issues a thing of the past. What you need to do is give your data a jumpstart.

Here’s our suggested plan of action for getting your data unstuck and giving it the jumpstart it needs:

1. Get your data into a readable structure.

  • The first row of your data should always represent the column’s title
  • Columns should contain the same type of values, respectively

  • Each row should represent a case or a single instance within the data and should contain a date of when that data was collected. This means that two different rows in the data can represent the same entity with data collected for it at different points in time.

  • As a consequence of the rule above, the data should include a row identifier column that can be repeated to indicate that different rows of data are representing the same entities.

  • Make yourself a metadata sheet (also commonly known as “data definitions”) that you and other users of the data can refer to.

Here are some simple example data & metadata using the principles above.  

2. Present your data in hierarchical manner catered to specific audiences.

  • Give your audience a call-to-action, let them know why the data is important and why they should care.

  • Begin with presenting high-level key metrics. Think about what the most important numbers are you to your intended audience(s).

  • Give your audience the option to select a few different categories in which to segment and parse-out those important numbers. Doing this will allow your audience to drill-down in the data to get from a high-level to a granular level.

  • Allow your audience to take the data they have drilled down to with them. This could be one row of data out of the thousands they started with at the high-level.  

Here’s an example of this data presentation flow.

3. Engaging your audience in data discussions

  • This one is self-explanatory: talking about the data with others is the best way to squeeze the value from it.

Here’s an example of effective data discussions.

Sounds like a good plan of action, right? If you're still not sure what your next steps should be, we’re here to help.

We’ll work with you to get your data in a structure that makes it valuable, or even create data for you. We’ll build you a data story with that data that helps you and your users understand the data so that you can turn data insights into business actions. We’ll get your users engaged in data discussions and app design feedback so that you know they’re engaged with the data and you know how valuable they perceive the app to be. So drop us a line, we’re here to help.