Customer-Facing Data Solutions Need a Product Mindset

If most customer data initiatives are risky, have low adoption, and often don’t meet their goals, what steps can we take to improve success rates?

What we’ve learned at Juice over 10 years and hundreds of implementations is that customer-facing data initiatives succeed when you deliver a product, not a project. You need a product mindset to reduce risks, make customers happy, and deliver revenue. Having a product mindset means:

  1. Acting like you are delivering a game release to millions of gamers for a midnight deadline.

  2. Treating every deadline as a missed product launch (with lost revenue).

  3. Recognizing that disappointing users will hurt your organization’s reputation.

Here is an illustration of how a new way of designing and delivering data products looks compared to the traditional IT BI project.

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Here are three things you need to do to improve your odds for your external data initiative, i.e. customer reporting, embedded analytics, customer-facing machine learning product, etc.

1. Set a product launch date

With a product mindset you have a product launch and not a project plan. Product launches involve more stakeholders, like marketing and sale so accountability is extended beyond the project manager. With many teams committed to an on-time date and increased executive visibility, there are lots of folks with skin in the game.

Products (not projects) have a revenue commitment. As a general rule, you won’t get any of marketing or sales time if there isn’t revenue attached to the initiative. One agile process we love to get the organization bought into the initiative are release readiness meetings. If facilitated well, each team actively participates and has a stake in go/no go decisions. This force stakeholders to make commitments and take interest in the outcome.

Missing dates isn’t the real issue; it’s the consequences of missed dates which dramatically affect your data product initiative. Missed dates makes additional funding more challenging as executives question the initiative’s (and team’s) ability to meet goals. It affects your reputation with customers and their confidence in you to deliver. Note the diagram above. As soon as the launch date is missed there is lost revenue and that gets noticed more than a missed project date.

2. Plan for 2.0 version from Day One

When you have a product and agile mindset, it's all about getting a small, valued first release into the market because you know there will be a 2.0 version. With a 2.0 of your data application, you’re not trying for the perfect data product, but one the market will use. This changes the way you gather requirements and prioritize features. When it's only a one-time project, it is easy to go overboard, taking weeks to define your requirements. Here’s a good article describing an MVP product’s functionality and their illustration of how to prioritize features for your initial product.

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Talk about 2.0 often, particularly with executives, to set their expectation that funding the initiative is an on-going endeavor and not just a single capital or funding request. When executives and customers know there is a 2.0, then every feature doesn’t have to be in the first release, helping reduce product complexity and likelihood of missed dates. Success improves if you build and share prototypes and explicitly deliver an MVP.

3. Talk about apps, not reports

We’ve touched on this point before, but it bears repeating. When you think of your solution as an application, it forces you to solve a problem. With a product mindset you spend more time on understanding the problem you want to solve vs. capturing feature wish-lists. As a data product manager, your new product mindset and focus on a series of apps vs. one large data project forces you to segment your audiences. You can now target each audience’s specific needs. Now that you’re delivering an app, your users will be more inclined to know when and how to use it. Apps, by their nature, have training and support built-in, reducing risks in implementation and adoption. 

There are some challenges that will arise with an "app focus”.  When delivering data apps, you’re creating a more prescriptive experience for your users. Those users or customers wanting to explore insights and define their own solutions could be disappointed. The right way to address this challenge is to view these data explorers as a unique audience with their own application needs since exploring data and presenting results are different problems.

Sure, we’ve now taken your single data initiative and made it into numerous projects. While it might be scary at first, it is most likely the reality of what your customers expect anyway. You didn’t expect to be able to meet the needs of all customers with a single offering did you? With your new found perspective you’re ready to think about your data initiative not as a single project, but eventually as a portal or app store to deliver many new products.

The Product Mindset

The product mindset is rooted in adopting agile methodology. The big IT project mentality hurts your customer data initiative and adds more risk. There are many excuses as to why folks don’t want to adopt a product mindset or agile for BI projects. We’ve heard them all; however if you feel like your organization wants to be part of the data economy, then building and launching data products is in your future.  

Want to learn more about our formula for successful customer data initiatives and see what a successful data product looks like then schedule a minute discussion below.

What does 'Bandersnatch' teach us about data storytelling?

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“TV of tomorrow is now here.” So says The Guardian. The TV show that brought us into the future is Bandersnatch, the recently released interactive television show from the Black Mirror anthology. Bandersnatch is sort of modern reincarnation of the Choose Your Own Adventure books of your childhood. Some reviewers raved about the new experience:

"This is what Bandersnatch gave me that no other movie had ever been able to. Before you say it, I know it’s just an illusion of choice and I had no real control over what played out on screen, but it still provided me with more influence on a narrative that wasn’t my own than I’d ever had before, and I revelled in the possibilities. To me, Bandersnatch is both a movie and a game and something entirely new. It’s a lesson in human psychology, a thesis on the illusion free will, and one hell of an entertaining few hours all rolled into one. And, perhaps most importantly, it’s only the beginning.” Lauren O’Callaghan, Gamesradar

For me, my intrigue with Bandersnatch relates to its similarities with our Juicebox-created interactive data stories. I thought it was worth examining some of the technical and narrative challenges faced by this TV experiment to find lessons for data storytelling.

The very first lesson: Be careful about using the term “Choose Your Own Adventure.” Netflix is getting sued. The other lessons fall into a couple of categories: 1) functionality for interactive storytelling; 2) understanding the audience needs.

Essential Functionality for Interactive Storytelling

Netflix appreciates the necessity of teaching their audience how to use the interactive experience. From the very beginning of the show, you are confronted with the selection mechanism, even before the show begins. To validate that the audience is learning how it works, your next couple of choices are trivial ones, i.e. which type of cereal or music our character would like. These interactions build a sense of comfort before some of the more dreadful decisions arrive.

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In Bandersnatch, as in most analytical exercises, it is possible to make choices that result in a dead-end to your story. For example, when our hero Stefan decides to produce his game with a game company, we quickly learn that this choice won’t result in a realization of his gaming vision. Netflix provides a quick mechanism to teach you that this was a wrong-turn and gets you back to a place in the story where you can pick a better option. Imagine the same careful handholding in an Excel PivotTable: at the moment when you choose an option that creates one of those 200 column nightmares, Excel instantly asks if you’d like to make a better choice. If only.

Perhaps Netflix’s greatest accomplishment is with their underlying technology for Bandersnatch that enables a seamless video experience. As a viewer, you make a choice and the video immediately launches you into the next chapter of the story. The same seamless flow needs to exist in data stories; make a selection, immediately see the results. It is a requirement to make users excited to explore the world you are creating.

The Challenges of Interactive Storytelling

Choose Your Own Adventure-style storytelling comes with its own challenges. The author needs to define many endings. Each permutation needs to find a way to connect — all while still developing characters and themes. In fact, one theme of Bandersnatch is that the complexity of this type of storytelling may drive a person to madness. Here’s a look at the decision-tree behind the show:

Redditer u/alpine’s flow chart of Bandersnatch decision-tree

Redditer u/alpine’s flow chart of Bandersnatch decision-tree

What else do we need to consider in telling stories of this form?

To start, we might consider how different audiences react differently to the injection of choice into their entertainment. For every person who is intrigued by asserting more control over the story, there will be others who are looking for passive entertainment.

"Even the most complex, arresting, emotionally draining show is essentially escapism because all the work is done for us.” …do people want to be the decision makers? It is hard work.

As a viewer, I will wave from the shores of traditional TV, happy to be spoonfed my entertainment and hoping that the young folk are having fun.” — The Guardian

Secondly, we have to ask: Does choice come at the expense of characters, coherence, and clarity of story telling? Bandersnatch struggled to build interesting characters. Traditional stories control the audience’s perception of a character at all times, and therefore can build a foundation of what makes that character work, layer by layer. By ceding control of that process to the audience, the author provides a collection of character “bricks” that haven’t been constructed into anything.

"It rarely deviated from the expected deviance, rarely landed in an unexpected place or – and this was where it most resembled its videogaming ancestry – had energy to spare to make the characters much more than ciphers.” — The Guardian

Finally, the audience of an interactive story has to ask themselves (just as Stefan asks): Are we really in control of our choices, or is there a hidden power that is flipping the switches? Are we only getting the illusion of choice?

Bandersnatch is mostly satire, too, but the "gameplay" jumps around a confusing timeline, making you repeat past scenes with different decisions. How you interact with your therapist, whether you agree to take drugs, and if you manage to open a secret safe, for example, all bring you down different paths and to several Game Over screens. These soft endings then send you back to earlier scenes, so you can choose the "correct" choice to further your progress. You have to do this numerous times to eventually receive a true ending. The concept of "right" and "wrong" choices bothered me and cornered me into decisions I didn't want to pick. — Elise Favis, Game Informer

For TV, this is an evolving entertainment form. In the world of data, we are also creating an evolving communication form. How do we find the balance of choice and flexibility with message? How can we engage and entertain without heaping the burden of authorship on an audience? These are questions we’ll need to continue to explore.

New Years Resolutions to be a Better Data Product Manager

It is the the New Year, my favorite time for New Year’s resolutions. Time to look inward to see how we can change ourselves to change your world.

If you’re responsible for a data product or analytical solution, you might consider a little self-reflection in pursuit of a better solution for your customers. Here are a few places to start:

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Empathy

the ability to understand and share the feelings of another.

When it comes to data products, you’ll want to foster empathy for the users of your data. More likely than not, they have concerns such as:

  • Your data may replace their power in the decision-making process.

  • They don’t have the data fluency skills to properly interpret the data and what it means for their decisions.

  • They are afraid of changes that will impact how they do their work.

Appreciating and acknowledging these fears is a first step in building trust with your users.

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Learn to flow

“I would love to live like a river flows, carried by the surprise of its own unfolding.” — John O’Donohue

We all a little guilty of wanting to make others bend to our view of how things should work. This year, you may resolve instead to “flow like water.”

Data products should enhance how people make decisions, giving them the right information at the right time. This is best accomplished when the data product can fit into the existing workflows so you are augmenting the user’s role rather than trying to change it.

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Patience

“Wise to resolve, and patient to perform.” — Homer

Patience is accepting that progress takes baby steps. This is a critical skill to help manage your data product ambitions. The possibilities for analytical features can seem limitless — there are so many questions that should be asked and answered.

Beware this temptation. You’ll want to find the most impactful data first to allow your users to learn what they can learn. Before you try to do it all, have the patience to gather feedback and plan your next release.

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Growth mindset

“People believe that their most basic abilities can be developed through dedication and hard work.” — Carol Dweck

Analytics is best served by a growth mindset, the belief that taking on a challenge (and sometimes failing) with expand one’s mind and open up new horizons. Useful analysis begets questions, which leads to more analysis and even better questions.

As a data product manager, you want to encourage this growth mindset in your customers, encouraging and enabling them to expand their understanding of their world.

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Inclusive

“We are less when we don't include everyone.” — Stuart Milk

Every year I tell myself I need to be better at meeting new people and keeping up with old friends. It’s a good ambition if you are leading efforts on a data products. It takes a diverse set of roles to get the support and commitment in your organization. Have you gotten legal on board? How about IT security? Does marketing and sales understand the value of your data product and who you are trying to target? You may need to change the way people think about making use of data to build company-wide support for your solution.

10 Visualizations of Juicebox

Christmas is a special time of year. We all have our favorite aspects of the season. In the spirit of Christmas and the Christmas carol, the 12 days of Christmas, here are the Juice team’s 10 favorite visualizations.

You will recognize some of these as your own favorites, but some are exclusive to the Juicebox platform. To learn more about the visualizations exclusive to Juicebox and Juice design schedule some time with us.

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Leaderboard

An exclusive Juicebox visualization. Leaderboards are a great way to look at a dimension or group across multiple rankings. Who really is best on the team? Its never one metric and a leaderboard lets you compare across multiple metrics. Here’s a video showing a leaderboard in action from a few years ago.

 
Flower

Flower

A very engaging way to compare performance across locations, such as hospitals or schools. Each entity is represented as a flower and every metric is represented by a petal. We were inspired by the work of Moritz Stefaner.

 
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Ranked List

Its not just a horizontal bar chart, but an interactive way to see a top ranking as well as a way to explore a long list. The Juicebox way of letting a user explore a long list is unique. Easy to understand because of its familiarity while delivering a lot of interactivity for exploration. Here’s an example from our Notre Dame application.

 
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Sankey

This visualization isn’t exclusive to Juicebox, but well-loved by clients because of its easy way to explore changes over time among groups. Our version is much easier with more options than the Tableau version with its dynamic generated polygons.

 
Distribution

Distribution

This is exclusive to Juicebox despite its less than creative name. The data is binned to show distribution of values while also emphasizing the individual items (by showing details on roll-over) that make up the “bars.” A really easy, yet powerful way for users to explore their data.

 
Orbit

Orbit

A variation on the bubble chart that shows relationships. This breaks some data visualization rules, but is helpful for exploring hierarchy and avoiding too much overlap. Like a bubble chart it uses size and color to convey information.

 
Scatter

Scatter Plot

The scatter plot is a common visualization for data exploration. Juicebox adds panels and panel colors to better help the user understand the values that are good or bad. By clicking on a panel, the user can focus on a specific group of items for action.

 
Map

Map

We love using maps as a filter for other visualizations. Additional encoding with dynamic labels further adds to a user’s understanding of the information.

 
Treemap

Treemap

We’ve been doing TreeMaps since 2009 so they hold a special place for us. This is still one of the best ways to show hierarchical data that has values that can be aggregated.

 
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Key Metric

While not quite a visualization, almost every data story starts with a quick summary of metrics, often with a comparison to goals or benchmarks. A key metric visual sets the foundation for what a user will get in their dashboard or application.

 
Lollipop

Lollipop

The Lollipop is another good way to show comparisons among groups. Lollipop is our preferred way of sharing metrics when the metrics can be compared along a common scale. This is a good alternative to a bullet chart.

Three Types Of Context To Make Your Audience Care About Your Data

The following scene is one of the most pivotal moments in the Game of Thrones series.

As a loyal viewer, this scene represents a turning point for Tyrion. He has reached a breaking point after a lifetime of conflict with his father. His speech is the moment that he sets out on a different path, a path that ultimately leads to (spoiler) the murder his father and (unsurprisingly) a deep schism with his family.

For a new viewer, it is a courtroom confession in costume.

Your experience of entertainment is entirely different based on the context you bring. It makes a world of different to know: Why we are here in this room? Who are these characters? What are their motivations?

Context is the foundation that gives a scene something to build on. Context makes your audience care.

It is the same thing when you design a dashboard, report, or analytical interface (with less beheading and back-stabbing). Lack of context — the set-up that explains the background and motivation for the data — may be one of the primary reasons why dashboards and reports fail to connect to audiences. And it may be the reason you can’t get your colleagues to open that spreadsheet you just sent.

How do you make someone care? You want to anticipate and answer a few inevitable questions:

  1. Why does this data matter to me?

  2. What am I about to see?

  3. What actions can I take based on this data?

Let’s explore these three elements of context with a few examples.

1. Why does this data matter to me?

Context should make it clear why the information is important. At Juice, we always start designing a data story by defining the audience we want to reach. It is best if we can be specific about the kind of person and role that they play in their organization. This person has things they want to accomplish that will make them successful. A good design takes all of that into account.

When it comes time to show the data, there is no reason to be secretive about who should be engaging with the data and why it is designed for them. As an example, take the following introduction to an analytical tool the New York Times’ Buy or Rent Calculator.


2. What am I going to see?

"Tell them what you are going to tell them, tell them, then tell them what you told them."

This famous piece of advice is often ignored by dashboard and report designers. A title isn’t enough; you should explain the scope of the content and, ideally, how the different elements fit together. Is there a structure or framework that undergirds your choice of metrics? Explain this visually before tossing your audience into the deep water.

One way that we’ve found to deliver this context is to provide an automated step-by-step tour of the content. You’ve undoubtedly experienced this approach when to try a new mobile app. The app designers walk you through the workflow and explain features. If done well, you’ve helped new users wrap their head around what they are going to see.

The following example prominently features a descriptive legend showing how to read the glyphs.

You may also want to consider ways how to help the user understand the interactions of your data interface or even show them the types of insights they can glean from the data. Here’s a great example showing survey data about the challenges women face in different countries.


3. What actions can I take on this information? 

Finally, effective context setting explains exactly how the data can guiding your audience to smarter actions. Your report or dashboard should lead to actions, not just show interesting data. It should point to what comes next.

The following example shows data about inequality in travel visas by country. For an individual, the actionable question is: For my country, where can I easily travel to? Data products are inherently personal so you want to highlight this in your context.


Context along a timeline

In summary, we can think about these three essential elements of context along a timeline. You want to explain for your audience:

1) Looking backward, what brought you to viewing this data?

2) Now, what should you see when they engage with the information?

3) Looking forward, what action can come from exploring the data?

2018 Data and Visualization Gift Ideas

We’re continuing our tradition of the annual data gift guide. These are some of our favorite books and gift ideas for the data scientist, designer or analyst in your life.

While you’re here take a look at the Juicebox product page to see what it looks like unwrapped.

Happy Holidays!

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New Books We Love

Books we read in 2018

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Classic Data Books

We’re a little biased in this category, but these are the books on our desks that we refer to all the time.

Data Fluency - Thinking about changing how your team or organization works with data?This is the book for you.

Storytelling with Data - This one already feels like a classic. It provides simple, clear guidance on chart usage and storytelling. Hard not to reference it in the midst of a project.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy - This is the book that keeps us grounded. Despite how much we think data is delicious and fun its serious too.

The Man Who Lied to His Laptop: What We Can Learn About Ourselves from Our Machines - A seminal read on learning about interactions between humans and machines.

Visualize This: The FlowingData Guide to Design, Visualization, and Statistics - Nathan Yau’s book that teaches us something new every time we pick it up.

The Truthful Art: Data, Charts, and Maps for Communication - We love all of Alberto’s books, but this one is our favorite. Wonderful examples throughout the book.

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Art & Posters

Infographics, Maps, Data Art & More

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Data Nerds

This is a term of affection during the holidays.

Trust the Process

My son and I are really excited about the new NBA season. We are Atlanta Hawks fans, so we’re not too optimistic about this year. We know the team is young and has decided to undertake a rebuilding process. Our mantra for this season is the now familiar “trust the process”.

If you’re not aware of the phrase “Trust the Process” comes from the Philadelphia Sixers rebuilding efforts over the past couple of years. What’s most interesting to me is that the formula for team success is much broader now. It is no longer just about having great players, but free agency positioning, analytical prowess, superior facilities and developing long-term successful franchises.

It's all about the process now.

I find the same can be said for delivering customer data and dashboard solutions.

Much of the historical focus when deploying data applications (customer dashboards, embedded analytics, etc.) has been on selecting the right tool. However, despite so many more great tools and increased investment in the BI space, successful implementation rates have not improved.

In a research piece by Dresner Advisory Services from May of this year, they highlight the fact that successful BI implementations are most often tied to having a Chief Data Officer (CDO). This makes a lot of sense because the CDO is just like an NBA team’s general manager. They bring accountability and experience as well as a process to make customer data solutions successful.

Here are some elements that make process so valuable to delivering data applications and solutions.

  • Launch Dates - A process is the best way to mitigate against missing the launch date. The existence of checklists, status updates, and documentation offer a means to anticipate risks that cause delays. Remember that delays to the product launch or release directly impact revenue and reputation. Missing product launch dates is not something that goes unnoticed.

  • Customer Credibility - When delivery dates are missed, requirements miss the mark or dashboard designs don’t serve their audiences product confidence is lost. Its not only the customer’s confidence that we need to be concerned about, but also the sales and marketing teams. Once we lose the trust of these audiences it takes time to regain it, not unlike sports teams who fail to deliver winning teams over many years (see: New York Knicks).

  • User Engagement - When there is no process that means there’s no planned effort to understand the audience and deliver the dashboard design. If users can’t understand the data you’re sharing with them, a cancelled subscription is a near certainty.

  • Applications, not Dashboards - The best dashboards are purpose-driven applications. Tools don’t deliver purpose. The process undertaken to understand and solve a real problem delivers a purposeful solution.

  • A Complete Platform - A dashboard solution is only a means of displaying data. A process defines ALL the requirements. Having a process recognizes that a complete solution is needed which includes security, user administration and application performance optimization.

Much like NBA success, a successful customer dashboard implementation isn’t about picking a product (player), but sustained success over many years of increased subscription (tickets) revenue, fan engagement and loyalty. The path forward for distributing and delivering on valuable data applications is all about your process.

In the event that you don’t have a process or a CDO leading your efforts, click here to learn about the Juicebox Methodologies. It's our way to design and deliver successful, on-time applications as well as wildly loyal fans. Trust the process. It works.

The Future Belongs to Purpose-Built Apps. We're Betting On It.

“Purpose-built apps”

“Low-code app development”

“hpaPaaS”

“Citizen Data Scientists”

“Data monetization”

Witness the cloud of new buzzwords floating in the air. Let me see if I can knit these concepts together to shed light on their meaning and implications for the future of analytics.

Collectively, these phrases are a reaction to the long-standing challenge of getting more data into more hands. “Democratization of data” can seem perpetually right around the corner (if you’re listening to vendor marketing) or a distant illusion (if you are in most organizations).

At Juice we have a picture that we call ‘The Downhill of Uselessness’. It shows how the usefulness of data seems to decline as you try to reach more users. On the far left, the most sophisticated data analysts and data scientists are happily extracting value from your data. But as you extend to the outer edges of your organization, data becomes distracting noise, TPS reports, and little-used business intelligence tools.

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Three barriers to democratizing data

The struggle of getting data to more people in more useful ways boils down to a few unsolved problems.

First, general purpose platforms and tools (data lakes, enterprise data warehouses, Tableau) can be a foundation, but they don’t deliver end-user solutions.

"Vendors and often analysts express the idea that you can master big data through one approach. They claim if you just use Hadoop or Splunk or SAP HANA or Pervasive Rush Analyzer, you can “solve” your big data problem. This is not the case.”

— Dan Woods, Why Purpose Built Applications Are the Key to Big Data Success

Second, reporting and dashboards deliver information, but often lack impact. In our experience, most data delivery mechanisms lack: 1) a point of view as to what is important; 2) an ability to link data insights to actions in a users’ workflow.

Third, the people who truly understand the problems that need to be solved don't have the technical capacity to craft re-usable solutions. We all have that elaborate spreadsheet that is indispensable to running your business and, frighteningly, only understood by a single person.

A better path forward

Finally, there is a realization that these problems aren’t going away. There needs to be better approach. It will come in two parts:

  1. Focus on creating targeted solutions (applications) that solve specific problems. Apps can integrate into how people work and the systems where actions occur. They attempt to let people solve a problem rather than simply highlighting a problem. And applications are better than general purpose tools because they can bake in complex business rules, context, and data structures that are unique to a given domain.

  2. Give greater impact and influence to the people best know the problems. It has always been unfair to ask technologists to create solutions for domains that they don’t deeply understand.

This direction aligns with Thomas Davenport’s view of Analytics 3.0 (from way back in 2013). He postulated that the next generation of analytics would be driven by purposeful data products designed by the teams who understand customers and business problems. (No offense, Tom, but we were griping about ivory tower analytics back in 2007.)

And so emerges a new model and new collection of buzzwords...

Purpose-Built Applications

Solutions that start with the problem and craft an impactful answer. Their success is measured by fixing a problem rather than in terabytes of data stored.

…built using a high-productivity Application Platform as a Service (hpaPaaS)

Cloud-based development environments requiring little coding ability (‘low-code’) — but requiring knowledge about the domain and the problem to be solved.

…to be used by Citizen Data Scientists (CDS).

the people who know the problems most intimately.

At Juice, we may have backed into this trend or cleverly anticipated it. Either way, now I can say that Juicebox is a low-code hpaPaaS designed for CDS to create purpose-built apps. Better yet, we are now fully buzzword compliant.

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.