Creating Tools for Data-Informed Decisions

Data-based decisions. It is a phrase that has become dull with overuse. It even suggests that choices are made obvious if you have the right data.

Data-based decisions seems to ignore the fact that decisions, for the most part, are still made by people — people who have colleagues and bosses and customers and stakeholders to balance. Results shown in data often run counter to long-held assumptions or exclude important, unmeasured factors. Data-based decisions aren’t make in a vacuum; they are made in our social, political workplaces.

Perhaps we’d be better of focusing on a similar phrase: data-informed discussions. Before a decision can be made, there is a discussion. Data can, and should, influence these discussion. It is left to humans to sort out options, weight outside factors, and evaluate risks that are not evident in the data.

Consider any discussion you’ve had recently that should be informed by data…a political discussion about climate change; optimization of your marketing tactics; education options for your children. Why is it so hard to bring data into these discussions in ways that enlighten and encourage smart decisions?

A few of the common problems include:

  • Misalignment about the nature of the subject, the decision to be made, or the meaning of concepts;
  • Failing to make assumptions explicit;
  • Different conceptual models of how the world works;
  • Different sources of information, leading to conflicting results.

Ideally, these issues would be addressed ahead of a discussion. What if every difficult discussion could be framed by data so the dialogue could focus on the most important things?

A couple years ago we tried to answer that question for one specific discussion that happens all the time. The tool we created — inelegantly called the “Valuation Analyzer” — facilitates discussions between start-up founders and investors. The debate centers around financial projects, value of the company, and the ultimate payback for equity holders. Our tool is intended to get these two parties on the same page.

Here what it looks like:

The Valuation Analyzer provides common and explicit ground rules for estimating the future value of a company. Users can define a small set of assumptions and instantly see how those inputs impact valuation and return on investment.

Each of the items underlined in blue can be adjusted by clicking and dragging left or right (thanks to Bret Victor’s Tangle library for this input mechanism). Most of the important assumptions are in the lower half of the screen: Do you want to calculate valuation based on revenue or EBITDA? What multiple will you use? How will revenue/EBITDA change over the coming years?

valuation-analyzer-detail.jpg

As you make adjustments, the values and visualization updates instantly. As a special treat, we added a “founder ownership” option (see the button under the title) to answer the most urgent question for any start-up founder: what’s my potential financial outcome?

Finally, a scenario can be saved and shared. The save button will keep all the assumptions you’ve entered and create a unique URL for that scenario.

This tool sets the parameters for a productive data conversation. The dynamic sentence across the top explains the meaning of the content. The assumptions are flexible, obvious, and explicitly stated. Two people sitting across a table can focus on the important values that impact equity owners’ outcomes. The discussion can happen in real time and the results can be saved.

It would be impossible to have a data discussion tool for every situation — there are too many unique circumstance. Nevertheless, this tool provides a model for those opportunities when a discussion happens time and again.

At Juice, we’ve found that organizations are so caught up in the race to capture and analyze data that they’re rushing past the most critical component – the end user. The best data in the world is useless if the everyday decision maker can’t understand it and interact with it. We’ve created a solution. Fruition [www.juiceanalytics.com] delivers a more thoughtful approach to data visualization. We think about data conversations, not just presentations. Instead of just presenting data, we create more ways for people to interact with, socialize, and act on the data.

End data-phobia: 3 visualization pitfalls to avoid

Here’s the situation: you are at your department’s quarterly meeting and your boss’s boss is presenting a number of charts and tables from the reporting dashboard. The data visuals are not telling a clear story and most are struggling to join the discussion around what impact this data has on the department, which is the entire point of the meeting. In that moment, you really appreciate the kind of graphics that make it easy to “get it” with a glance.

The confusion created from unclear visuals is a common scenario, but it doesn’t have to be that way. Just think of all the visual data we encounter in our everyday lives -- we constantly consume and interact with it. And when done right, the data can help us understand our world and engage curiosity.

Take for instance the weather. Most of us check it everyday to know whether to grab a jacket or an umbrella. There’s usually a nice graphic to quickly get that info across.

And on your way to work, you might check the GPS for traffic and route alternatives. Heading home, you stop by the grocery store and grab a bottle of wine and pick up ingredients for dinner. The wine label even pairs the food for you!

We are looking at data and making decisions all the time. We habitually checkout these data products on our smart devices which are sophisticated yet digestible, so we continue to use them. But what happens when we can’t digest and just don’t get it? Data phobia!

Sadly, most of us suffer from this illness because the data products we depend on don’t tell a clear story. While it often takes more than just ordinary visuals to communicate a complicated message, some cues can be taken from everyday examples to help make complex data more accessible.

Here are 3 pitfalls we’ve found that hinder a user’s understanding of data:

1. Expecting end users to understand your jargon. It may make you look smart, but it makes others feel dumb. Ever see the finance manager presenting to a room full of HR and marketing execs about EBITDA and CAPEX? Consider how you can make the message meaningful to your audience.

2. Dropping end users in the weeds. Give end users a clear and simple starting point, help them out with high level summaries and allow them to progress logically through the data, gradually intruding more complex details. Eliminate those weeds and provide a path from simple to complex with clear visual hierarchy.

3. Telling conflicting stories with different data sources. Make sure not to provide data from different sources that tell conflicting stories. Don't make your end users have to guess which story belongs with which case. Data should be good enough to help a user make a decision and move the discussion forward.

Now take a look at some of the data products you’ve created. Do they pass the pitfall test? It’s always a good idea to run some of the visualizations by a sample of your end users.  Get feedback on whether they can glean insights, and if it spurs discussion and decision-making. If not, find out what is confusing to them. Drill in and think about how to simplify the message, construct a consistent story, and guide the end user down a logical path.

Find out more on effective data visualization from our book, Data Fluency. Do your part to help end data phobia and make data more delicious for everyone.

Get a free excerpt from the book!  Enter the code: FLUENCY-EXCERPT


Excerpted with permission from the publisher, Wiley, from Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani, Chris Gemignani, Richard Galentino, Patrick Schuermann.  Copyright © 2014.

 

Help your audience spread the word

One of the biggest things we’ve learned in recent years when it comes to presenting information is the importance of the secondary audience, your audience’s audience.  The information shared, (e.g. report, dashboard, application), if valuable, rarely stops with the initial or primary audience.  It isn’t just “liked”, but shared and "re"-presented to peers, bosses, etc.  In many cases the secondary audiences are more significant than the primary ones.  In fact, often it is with these secondary audiences and conversations where decisions are made and the data turns into actions.

If these secondary audiences are so important, what can be done to minimize the risk of the telephone game effect (see image above) and help these secondary audiences understand and value what has been shared with them?

Garr Reynolds in presentationzen, a favorite book of ours, offers some great guidance on presentations that is very relevant to sharing data as well.  He says on Page 67 relating to not sharing slides with your audience, but providing handouts, “Since you aren’t there to supply the verbal content and answer questions,  you must write in a way that provides as much depth as your live presentation”.

Here are a few tips and an image below to help you be successful with secondary audiences, give them the required depth and solve for not being there. 

  • Offer Context - Use descriptions and provide access to the details. 
  • Anticipate Questions - Describe what questions you’re able to answer.
  • Explain Usage - Provide guidance and instructions on how to explore the data.
  • Share as Modules -  Give bite sized pieces of information that are easy to digest.

Our epiphany on the importance of these secondary audiences is driving much of the new functionality in Fruition.  Click here to schedule a 30 minute demonstration to see how we help you communicate with secondary audiences.

4 Components of the Data Fluency Framework

Data alone isn’t valuable—it’s costly. Gathering, storing, and managing data all costs money. Data becomes valuable when you start to get insights from it and apply those insights to actions. But how do you empower your organization to do that?

You might be thinking that the answer is that you need another dashboard, or perhaps better visualizations. Sure those things can helpful, but without a data fluent culture within your organization, you’ll still be scratching your heads and wondering why it isn’t working. There are so many benefits of having a data fluent culture, but what path or framework do you need to actually get there?

The key to the data fluency is actually a web of connected elements. It requires (1) people who speak the language of data, (2) skilled producers of data products, (3) an organizational culture with the conditions to support data discussions, (4) and the systems, tools and ecosystem to create and share data products. The Data Fluency framework explains the roles of individuals, the organization and the systems necessary to achieve it.

Individual - The most fundamental element of your Data Fluency Framework is the individual or data consumer. The organization is made up of numerous individuals and enabling these individuals to understand and draw deeper meaning from data is the fundamental condition for a data fluent organization. It takes more than a solitary listener to give meaning to data. When individuals are informed, they can participate in comprehensive dialogue around that data.

Data Producer - Your organization’s data producers must work with your raw data and deliver the content in ways that are easy to absorb.  Each individual comes to the information with different priorities, needs, and perspectives. As a producer of data, your successful translation of data builds on this variety as an asset - everyone in the discussion adds to the overall understanding of the group and finds their own insights.

Data Product Ecosystem - To enable the flow of information and the creation and sharing of data products, you need standards, tools, and process in place. A good example is what Apple did with the App Store in creating a platform and standards by which apps are created, tested, distributed, and reviewed. Your data product ecosystem is no different, you must come up with those same standards, tools and processes to facilitate the data environment:

Standards are the design patterns and style guidelines that make it easier for data producers to effectively communicate with the data.

Tools enable you need to design and build data products and ensure they are discoverable for your the target audience.

Processes encourage the sharing of insights and collaboration between producers and audiences, as well as ensure data hygiene and quality throughout.

Data Fluent Culture - As your company develops more data consumers and producers, the data fluent culture will develop and  flourish.  Your company will develop your own unique dialect of data fluency through defining key terms, data collection and interpretation. This leads your company to actions based on results and goals —and that is a culture everyone wants to have!

Pick up our book, Data Fluency, to learn more about how to create a data-fluent culture within your organization and contact us to get a demo of our product, Fruition, and start turning your data into dollars.

Get a free excerpt from the book!

Enter the following code: FLUENCY-EXCERPT

Excerpted with permission from the publisher, Wiley, from Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani, Chris Gemignani, Richard Galentino, Patrick Schuermann.  Copyright © 2014.

 

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.

 

Excerpted with permission from the publisher, Wiley, from Data Fluency: Empowering Your Organization with Effective Data Communication by Zach Gemignani, Chris Gemignani, Richard Galentino, Patrick Schuermann.  Copyright © 2014.

 

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 info@juiceanalytics.com.

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).

data_fluency_framework.jpeg

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:

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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.