Give them cake - and a great user experience

My favorite marketing story from a class in college was about cake mixes. It always stuck with me because of the way consumers drove the development of the product. For a product that is so prevalent these days, I was amazed to hear that when it first came out, it was kind of a flop. Why? The end user.

When first created, cake mixes were made for convenience - making it easy to have a cake at home. In trying to make it as painless as possible, cake mixes initially touted that you only had to add water. That’s it! Could it be any more convenient? And yet, sales didn’t take off like expected. 

It was only when market research was done that they discovered that there was a psychological element to cake mixes that had been overlooked. Women wanted to feel more a part of the cake-making process. As a result, they changed box mixes to add water and eggs. Sales went up and now cake mixes are widely used and loved by all.

Of course, I’m simplifying the story a bit, but the point is this - the end user. The consumer. You have to know who they are and what they want in order to meet that need and for your product to be successful. Whether it be a cake mix for consumption of cake, or a data product for consumption of information. If it doesn’t meet their need, it won’t be used. 

One of the most important things we can do when designing is to consider the end user. It’s all about them. Even in this era of Big Data, the data is just the beginning. The ultimate goal is to be able to do something with that data, likely using some kind of data application. The person using that data to make decisions, take action and be productive needs to be able to glean valuable insight from it. If it doesn’t enable them to do that, they won’t use it and the benefit of all that data will be lost.

When using our product, Fruition, to build data applications, we always start a design by talking about the end user and what their goal is. Then we work backwards to figure out how to create a design that helps them achieve that.

Here are some things to keep in mind about the end user when working through your own design:

1. Reduce new features and improve current ones to improve experience.

"You have to deeply understand the essence of a product in order to be able to get rid of the parts that are not essential." - Jonathan Ive, Senior Vice President of Design at Apple Inc.

2. It’s not about you. Consider your user’s needs. How can you help them complete tasks in the most intuitive way?

3. Workflow integration. How can your design fit into it?

4. Offer guidance and instruction for your users where needed. 

5. Consider a user’s comfort level with data and visuals and design something appropriate. 

6. Make it conversational

7. Keep it simple.

8. There’s a difference between interesting and useful. Lead the user to take action.

In addition to those things, have conversations with clients and figure out what they actually need to accomplish. You know the saying, "If you build it, they will come"? Well, they lied. If you build it with the ability to help them solve a problem, they will come.

4 Benefits of a Data-Fluent Culture

Businesses are aggregators of data, yours being no exception. And with technology advancing at exponential speed, we’ve been able to collect massive amounts of data—some would argue that we’re drowning in it. Much to the dismay of your college statistics professor, most data is underutilized and  rarely makes it off the spreadsheet. And so, we must ask, does your data matter without someone to read, understand and use it? 

One of the ways you can compete today is to learn how to transform that wealth of information into valuable actions. We don’t mean that it falls solely on you or even that small team of number-crunching analysts in the basement. We are doing away with the antiquated model where data nerds are the gatekeepers of all that big data and information flow. In essence, the entire company needs to become fluent in the language of data. Everyone from the people curating the data to the executives who are making decisions needs to know and understand the metrics being measured, how to talk about them, and how they impact your business.

Here are four benefits of creating a data-fluent organizational culture and how it will impact your business:

1. Make Informed Decisions

Your company’s clear understanding of data and the ability to transform it into meaningful analysis will move your organization away from historical biases, decision by anecdote, pure gut instinct, and urban legends. Data fluency creates a more systematic and objective approach across the board so that everyone can make smarter decisions and react with better judgement. “Because I said so” no longer flies far these days.

2. Save Time and Get to the Point

Staring at 80-page slide decks full of charts and graphs rarely inspires productive discussion. More time and energy is focused on deciphering the busy graphs and tables than having meaningful discussions. One of the barriers to progress lies in miscommunication and misinterpretation of data itself. But, in a data-fluent culture, it’s easier for everyone to understand and discuss the key takeaways—getting right to the point.

3. Be Transparent and Accountable

It’s tough to measure progress and responsibility when data isn’t available or is confusing. Clear visualization and communication about key metrics give everyone an understanding of priorities and how their actions influence those priorities. In data-fluent cultures, it’s easier for team members to hold themselves and each other accountable.

4. Cultivate a Learning Culture

Discussions about data often raise more questions than they answer. In an organization where data communication works effectively, new and better questions are asked. This inspires creative friction and changes the conversation from explaining to questioning. These questions lead to deeper knowledge, more insights, and, hopefully, change.

When understanding and speaking data becomes part of your company’s culture, the dialogue changes from gut instincts and misinterpretations to bigger thinking and informed decisions.

Pick up our book, Data Fluency, to learn more about how to create a data-fluent culture within your organization and start turning your data into dollars.



Customer Reporting is Different

The biggest difference between customer reporting and internal reporting is understanding responsibility. In customer reporting the burden of understanding is on the author not the reader. When doing internal reporting its reasonable to expect staff, peers and colleagues to invest time, know the business and react to what's been shared.  Unfortunately, the same expectations can’t be placed on customers. If the report isn’t useful they won’t use it nor do they have to.

To ensure customers value your reporting investments and actively use them, report authors should:

  • Be Captivating -  Grab their attention.  There’s only a few moments to engage the reader and a couple of minutes to have them use what you’ve given them.   This is less about styling and more about having intention and clarity.   Squeezing every number they need onto one screen isn’t the answer.  Juice’s Design Principles offer some structure on how to get started on the right track.

  • Guard against misinterpretation - It isn’t good enough to make the information available; use visual cues and functionality to draw the reader to the things that matter most. A few mechanisms that can help are alerts, positioning on the page, and intentional use of color and emphasis.  Also, reveal information as the user expresses interest. In other words, don’t bombard the user with all the information at once. We frequently use levels of increasing detail from (a) key metric to (b) context around the metric to (c)  full breakout detail for the metric. (Data Fluency page 121)

  • Provide a To Do List -   Is it clear for the reader what they should do next? This is the hardest part. Deliver specific metrics where actions are obvious.  For example, customer satisfaction is an often used metric, but the next steps are never clear.  Also, ensure they have the same metric definitions and understand so that actions are clearer.  Then there’s always the obvious.  Tell them.  You’re the expert and want to be a trusted advisor.  Maybe include a tooltip on hover or some dynamic text, e.g. "If X goes below 5% be sure to…” Getting the To Do List right is the hardest, but when it works you’ve achieved report utopia. 

While not as critical, here are a few other important items for customer reporting.   These can certainly throw a monkey wrench into your delivery and customer expectations if not handled correctly.

  • Web-enabled - Be prepared to deal with the cross-browser issue.  Will you restrict which browsers you support or will you ensure to test all reports and modifications across browsers (and screen sizes too).

  • Secure access - This is not only about SSL vs. TLS and data security, but getting users to change passwords, timeouts when idle, as well as displaying only the relevant data to them.   If they already have a company login, is  single sign-on an option?

  • User Management - An ability to monitor how customers are accessing and using their reports  as well as a means of adding and removing users.  Consider giving your sales and support teams access to this information.

  • Licenses - While the organization may have a corporate dashboard or reporting platform, that doesn't mean you have web licenses for all customers.   Have a good sense as to how you plan to share information and what are the licensing implications.   Quite a few folks get surprised by how their software provider handles customer access licensing.

  • Education - A means of getting new or novice users up to speed.  This can also be important for support or sales teams that have to answer customer questions related to reporting that is shared.

This is just the beginning.  If you’d like to learn more or possibly discuss this in more details don’t hesitate to reach out at

9 Reasons We Resist Making Data-Driven Decisions

If the goal is more informed decisions, better tools to analyze and present data just scratch the surface of the solution. There are many cultural and personal reasons why people struggle to rely on data to improve their work. Here are nine common barriers to data-driven decisions -- as illustrated by my 6 year-old daughter:

1. Head in the Sand

The truth can be painful, especially if knowing that truth means letting go of long-held assumptions. Analyzing data holds the risk of revealing new insights that are contrary to someone’s experience about how the world works. One symptom of this type of data resistance is described in a Harvard Business Review article about big data and management:

"Too often, we saw executives who spiced up their reports with lots of data that supported decisions they had already made using the traditional HiPPO approach. Only afterward were underlings dispatched to find the numbers that would justify the decision."

2. Aversion to Math

"Twelve years of compulsory education in mathematics leaves us with a populace that is proud to announce they cannot balance their checkbook, when they would never share that they were illiterate. What we are doing—and the way we are doing it—results in an enormous sector of the population that hates mathematics. The current system disenfranchises so many students." -- Teaching Math to People Who Think They Hate It (The Atlantic)

This subsegment of our society is immediately resistant when presented with numbers. Their reaction may have very little to do with the message and everything to do with the medium.

3. Analysis Paralysis

Some people may embrace data-based decisions…a little too much. Because data is often incomplete or insufficient to draw firm conclusions, it can be easy to keep searching and analyzing in hopes of more clarity. 

When is good enough good enough? RJMetrics suggests that “data driven thinkers avoid analysis paralysis by sorting out when it’s worth taking action now, and when it’s better to pause and collect more data.” 

4. Fear

If the decisions are based on data, why am I necessary?

The fear of displacement can animate some people who resist using data. In their mind, they were hired for their experience, expertise, and gut instinct. These people may not appreciate the important synthesis of data and business understanding that is required to make analytics useful. 

In an American Banker entitled Bank CEOs Fear the Data-Driven Decision, an experienced banker explains: “…most bankers got where they were using their ability to 'read' the situation, a relationship, a deal or a market opportunity based on their gut and their personal skills and experience."

5. Uncertainty and Doubt

Inexperienced users of data will often question their own ability to understand what the data means. They wonder if their interpretation is right and how exactly to read data visualizations.

Sometimes these questions are turned outwards. Can I trust what this data is telling me? Do I feel comfortable with the sources of the data? Or most cynically, do I trust the motivations of the person who provided the data?

6. Preference for Stories

Narratives are easily digestible. The lessons are often clear, as are the heros and villians. Audiences love them. In an effort to commandeer a bit of stories’ attraction, the data analytics industry has focused on the concept of data storytelling. Even so, for many executives, telling a story unencumbered by the facts is a more compelling approach than being tied to the data.

7. Unable to Connect the Dots

Data decision-makers need to make the link between the data they see and the actions they can take. Sometimes this is an organizational problem: the data insights are being generated in a data science team while the people at the front-lines are some distance away. Another disconnect may be between the presention of data and the audience’s ability to absorb the message.

8. Impatience

Relying on data can mean taking the time to find the right data, test hypotheses, and evaluate results. In our fast-moving world, who’s got the time to do the analysis before making every decision?

In response to a Quora question 'What are executives' biggest unanswered questions about data in decision making?’, one respondent noted: "Someone once told me they'd rather rely on heuristics because data analysis is laborious, time consuming, expensive, noisy."

Clearly, data doesn’t need to drive every decision, and making smarter decisions will always save time and resources in the long run.

9. Lost in the Weeds

Pablo Picasso said “Computers are useless. They can only give you answers.”

It isn’t hard to find yourself surrounded by numbers from reports and dashboards, and in the process lose a sense of what it all means. The numbers often don’t help you understand what are the right questions, and what you should do with the answers. People can become fixated on the details and lose the ability pull themselves up to a level to appreciate the implications of those details. 


These nine problems -- and many more that you may have seen -- are more emotion than technical and depend more on mind-set than skill-set. Overcoming them requires executive leadership, clarity of message in data communications, explicitly linking data to actions, and a collaborative, pro-data environment. These are a few of the topics we explore in our book Data Fluency.

Shutterstock Builds a Data Fluent Culture

Rare is the organization that is 'fully fluent' in its ability to analyze, use, share, discuss, and act on data. In most cases, there is at least one component of the data fluency quadrant that lags — whether it is the skills on staff to communicate effectively (upper right in the figure below) or the tools in place to make data accessible (lower right).


Shutterstock may be an exception that proves this rule.

Last week I had an opportunity to chat with David Cohen, Director of Information at Shutterstock. He’s been with the company four years and has helped transform the culture, build the analytical talent, and put processes that place data at the center of the company’s decision making.

The impetus for the conversation was a data communication program that Shutterstock had instituted called the Daily Dose of Data. Every day, a short e-mail is sent company-wide with an interesting analysis or view of data. Here’s a recent example showing weekly customer searches for the term Ebola:


The purpose of the Daily Dose program is simply to get people thinking. Some messages are interesting factoids while others may spark substantial debate in the company as new people are exposed to realities of the business. Like many good ideas, Daily Dose started small. It originated as an email to a select group of colleagues, but once the company’s founder and CEO caught wind of it, he saw an opportunity to build a more data fluent culture.

David had a lot more to share about how Shutterstock had successfully incorporated analytics into how decisions are made. I was particularly intrigued by his description of how analysts are integrated into the engineering and business teams.

On the one hand, new analysts are required to spend their first three months learning the business, much of that time with the engineering teams to get a deep understanding of the data sources, systems, and metrics at the core of the digital imagery and music marketplace. In Data Fluency, We had emphasized a similar point about building a shared understanding of metrics and where the data comes from. Without this understanding, people don’t know what data they can depend on and what actual actions and events it represents.

Shutterstock analysts are then moved to product and business teams. David explained the unique incentive structure that he feels makes this arrangement successful. Analysts are partially compensated based on achieving measurable business impacts through analytical findings. Which is to say: analysts get rewarded if they can find an area for improvement, convince the business to make a change, then track and validate the results. For Shutterstock’s case, this incentive instills productive behaviors:

  • analysts need to communicate their analytical findings effectively to product managers;
  • analysts need to look for opportunities that product managers can actually act on (i.e. theoretical concepts need not apply);
  • analysts need to establish test and control models to evaluate the impact of their change.

As an added benefit, Shutterstock analysts often are able to move beyond the worst part of their job: report order taking. By working closely with the business team, they are able to teach others how to access and understand data on their own.

Through training, incentives, and hiring skilled analysts, Shutterstock has been able to bridge the divide between the data-have’s and the have-not’s and build a culture that gets people working together using data as a language of business.

I'd love to hear more stories like Shutterstock. Send me a note.

4 Ways Companies Struggle with Data Fluency

Our new book Data Fluency is about the individual skills and organizational capabilities necessary to communicate effectively with data. We are fascinated by the interplay and interdependence between the two. That is, it takes people who are skilled with presenting data to enable the sharing of insights; equally important, data fluency requires a organizational culture that values decision-driven discussions.

In fact, the framework we introduce in our book is one step more complex. We present the distinction between data consumers and data authors. Those who use data to inform their work versus those who’s work it is to inform people with data. This distinction applies both at the individual level and at the organization level, where we consider the data fluent culture (how do people consume and make use of data?) and the data product ecosystem (what capabilities, processes, and tools are in place to produce effective data products?). As a result, we end up with four building blocks that compose a data fluent organization.

However, it is rare to find a companiy that is strong in all four of these quadrants. Chapter 3 of our book identifies some of the common challenges we see as companies stumble in their efforts to make use of their data. Below are four situations we’ve seen in our experience working with dozens of companies trying to build analytics into how they run their business:

Report Proliferation

Reports have a way of multiplying like rabbits. Start with a perfectly useful and important report: a monthly sales report with product enhancements and utilization metrics sent to strategic accounts to make them aware of improvements coming and past usage. Customers see the information and want to know more. The report grows. A missing metric is added along with a detailed breakout. New reports are spawned, but the old ones don’t go away. Someday, somebody might still find them useful. The general thinking is: “If we report on everything, surely the right information will exist somewhere in a report.” Perhaps they’re right, but if no one can find what they need, everyone’s left sorting rabbits.

Balkanized Data

Departments in an organization can easily become independent silos, operating with their own set of norms, conventions, and terminology. This impacts what you can do with your data and what you can understand. You’ve experienced this problem if you’ve ever been on a customer service call where you give all your personal information at the start of the call and then have to give it all again every time you’re transferred. Each organizational department may use different data systems and terminology, processes, and conventions in data conversations and products.

Data Elitism

Working with data can require a lot of technical skill. And data can tell stories and reveal truths that an organization may not want to share broadly. Why not centralize your efforts and limit access to data to the highly trained few who can be trusted to bring order to chaos?

Like an over-eager police force hunting down deviants, this IT-led vision of business intelligence focuses on control, consistency, and data management. An extreme approach, however, comes at the expense of the individuals who use the data. Distancing analysis from the people who must use it results in data producers and their products that are disconnected from the decision- making process. Data products aren’t trusted and they often aren’t useful. All the problems of a command and control economy emerge.

In Search of Understanding

An organization’s capability to make fluent decisions from data depends on how well the organization knows itself. Self-awareness helps you answer the difficult questions: What does success look like? Are we moving in the right direction? Who should we compare ourselves to?

For a new organization—especially one in an emerging market—it takes time to figure out what matters most. These organizations often lack focus in their data analysis, measurement, and communication while on the path of discovery. Even with the best intentions, organizations can struggle to make good use of their data as they search for the information and metrics that will align with their emerging strategy.


Each of these areas is a failure of one or more of the quadrants in the data fluency framework. It may be a lack of leadership, a organizational culture that prefers anecdotes to data, or sparse skills for delivering data in ways that are easy to digest. 

For more, buy our book — or download chapter 1 to see if it seems like something that might be useful in your work.

Sometimes, more data hurts your cause.

I heard this article on NPR this morning that talked about an experiment in which one group was presented with a picture of a child in need and another group was presented with a picture plus data about the global need. The objective was to prove that picture+data resulted in a more generous response.

The surprising result was that picture+data actually resulted in a less generous response from the peeps.

The first hypothesis was that this occurred because the picture alone elicited an emotional response, while the picture+data balanced emotion with logic, reducing the emotional response of charity (you know, left brain v. right brain).

However, what they believe occurred was that the picture+data peeps actually had conflicting emotions: the picture made them feel compassionate, but the data made them feel hopeless. The additional data made the problem feel so big that there was no sense that involvement could make an impact

I think the lesson learned is this: when presenting data, make sure you also enable people to feel like they can have a positive impact if they expend energy acting on what they see. In other words (once again), "carefully selected and action-oriented data" trumps "more data" every time.

Our new book Data Fluency may not be for you

...but you probably know someone who should read it.

If you've been following Juice for a while, you know what it is to effectively communicate with data. You know that sweating over a finely-honed analysis is of little use if your audience misses your message. You understand the frustration of presenting a beautifully-designed dashboard only to have the discussion derailed by a debate about what a metric means or how to read a chart. You might agree that the challenges in analytics are less about technology, and more about people, culture, and shared understanding. You've seen that healthy discussion about data is as important as "Big Data" or creative visualizations. 

Sorry, we didn't write Data Fluency for you. We didn't need to.

We wrote the book for those around you to expand appreciation of these principles to others. We believe that data fluency (which we define as the ability to use the language of data to fluidly exchange and explore ideas within your organization) needs to be pervasive to be truly effective. Being the only one in your organization who is great at communicating with data is like being the first fax machine.

How do you expand these pockets of data fluency to entire organizations? How do you create a culture that encourages effective data communication? What capabilities and skills are necessary to put data to be put at the center of decision-making and discussions?

We wrote Data Fluency for the people who can be part of this change. Perhaps it is your boss who needs to better understand the untapped potential of data. Or a colleague responsible for reporting, but who is still learning how to communicate with data. Or your sales team who may be both data-starved and a little data-phobic. 

When Nathan Yau and Wiley approached us about writing a book, we knew that the world didn't need another guide to dashboard design (we'd already written a white paper that did a pretty good job), more lessons in visualization fundamentals (Stephen Few has that covered), or a practical guide for visualization practitioners (Nathan's done that with Visualize This and Data Points).

We wanted to provide a fresh perspective that answered a different question: How can organizations more effectively incorporate data into their decision-making?

Data Fluency is intended as:

  • A roadmap for transforming an organization with a lot of data to one that uses that data to share ideas and knowledge.
  • Practical advice for both consumers and producers of data products (reports, dashboards, analyses). It takes both an effectives presenter and a willing audience for the data to flow freely.
  • A guide for executives who are energized by the opportunities to make a smarter organization, but puzzled by their organization's struggle to be more data-driven.
  • An inventory of the skills and capabilities needed to be data fluent, and an opportunity to see where you stand.

At the core of the book, we've provided a framework for thinking about all the parts that need to come together to build a data fluent organization. We have identified the four elements you need to build a data fluent organization. They are:

  • Engaged and educated data consumers;
  • Skilled authors of data products;
  • A culture that encourages communication with data;
  • An ecosystem of people, processes, and tools that supports the production of quality data products.
Our Data Fluency Framework

Our Data Fluency Framework

Our hope is that this book starts a new kind of conversation in the analytics field -- one that incorporates the people side as much as the tools, techniques, and technologies. We hope it spurs individuals and organizations to start on a journey toward making data a more useful tool for sharing ideas.

Nathan Yau is making a chapter from the book available on his site Flowing Data. Or skip straight to Amazon to buy the book Data Fluency.

Analytics 3.0 and Data Monetization

When it was published in 2007, Competing Analytics: The New Science of Winning sparked the imagination of many business leaders. It opened eyes to the concept of analytics as a strategic capability, beyond basic reporting, financial analysis, and basic marketing optimization. The authors Thomas Davenport and Jeanne Harris established themselves as leading thinkers in an emerging field. However, the book found its critics among some analytics professionals (like us) who felt it delivered a superficial understanding of the real challenges “on the ground” and offered guidance that was abstract, academic, and anecdotal.

At the time, we said his advice for becoming an analytics competitor was "a good example of condensed misperceptions about what analytics can and should do."

Others were less circumspect:

  • The top review on Amazon states: “This is the glib, anecdotal book built around a basic, almost stereotypic Harvard Business Review five-level model, this one focusing on various levels of use of analytical methods, systems and processes.” 
  • From Neil Raden: "what Davenport is implying is not only centralized control, but also centralized design. This is another naive assumption, because many organizations are not only decentralized—they’re dysfunctional."

Even with mixed feedback, it was clear that Competing on Analytics hit a nerve. Davenport and Harris continued their research and evaluation of the analytics world, and in my opinion, have made progress in reflecting the realities and challenges of analytics practitioners. In 2010, they released a book entitled Analytics at Work that focused more on the front-line realities of information workers. In December 2013, Davenport and Harris published an article in the Harvard Business Review entitled Analytics 3.0.

With this evolution, I feel they have begun to capture the essence of the analytics opportunity ahead. In particular, they have begun to focus on how data can be used to enhance product offerings — shifting the focus from smarter internal decisions to smarter, higher-value product offerings. In their conversations with data savvy companies, they describe seeing "a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy."

They go on to say:

Today it’s not just information firms and online companies that can create products and services from analyses of data. It’s every firm in every industry. If your company makes things, moves things, consumes things, or works with customers, you have increasing amounts of data on those activities. Every device, shipment, and consumer leaves a trail. You have the ability to analyze those sets of data for the benefit of customers and markets. You also have the ability to embed analytics and optimization into every business decision made at the front lines of your operations.

The Analytics 1.0, 2.0, and 3.0 framework from the International Institute for Analytics

The Analytics 1.0, 2.0, and 3.0 framework from the International Institute for Analytics

In the HBR article, Davenport and Harris describe what it takes for companies to engage in Analytics 3.0, and include some of the standard messages that we’ve become so accustomed to from the era of Big Data: more data, more “data management options" (i.e. Hadoop, NoSQL, in-memory, etc.), faster technologies and faster analysis. Perhaps least compelling for me is a concept of creating "analytics on an industrial scale.” Apparently IBM has created a data model factory and assembly line to make and maintain 5,000 models a year. It isn’t clear whether this kind of approach would be appropriate or useful for most other companies.

However, when they delve into the human and organization challenges, their message is more resonant with our experience. For example, they highlight the need for:

  • Focus on delivering analytics to the front-line decision makers. These are the people who are making everyday decisions that impact customers. The best analytical solutions do a good job of presenting data that helps people within their current workflow in ways that are easy to understand and act upon.
  • Time and resources need to be invested into data discovery to understand data before delivering analytical products. Too often we see analytics and reporting rushed out to an audience without a clear sense of what metrics matter and what information will actually help the recipient.
  • Collaboration must occur between the business, analysts, and IT. This is certainly one of those concepts that is easier said than done. Nevertheless, it is better to recognize this challenge up front than believe a cohort of elite data scientists will be able to bring analytics to the masses.
  • Top-level leadership needs to support the deep embedding of analytics into products and services. While this is true, we have also found that achieving successes at a grassroots level can help convince leadership of the opportunity in analytical products.
  • Prescriptive analytics (the effort to use data to specify optimal behaviors and actions) will be more valuable than descriptive analytics and more common than predictive analytics. We’ve found that the analytics needs to have a clear and strong point of view to guide users to insights and actions.
  • Organizations need to focus on transforming how decisions are made. Davenport and Harris state: "Managers need to become comfortable with data-driven experimentation. They should demand that any important initiative be preceded by small-scale but systematic experimentation.” The challenge is in building a culture of data fluency — something we tackle in our upcoming book.

Every day we talk with companies that view their data as an asset that can help them either 1) augment and enhance their existing solutions; or 2) generate new revenue streams. Like Davenport and Harris, we feel these are the early days. Some companies still view customer reporting as an unfortunate requirement rather than an opportunity to build loyalty. Other companies haven’t had the time or resources to find ways to make their products more powerful with data. That understanding will come. Gartner predicts that 30% of businesses will be monetizing their data by 2016 and McKinsey Consulting is seeing similar evidence that companies are finding ways to turn data into dollars. We are excited to have a front-row seat.