How do you build a high-impact analytics team? Jamie’s team knows.

Meet Jamie Beason. She is a Senior Director of Business Intelligence and Analytics at JLL, a global professional services company specializing in real estate and investment management. Jamie has built an analytics team with a thoroughness and thoughtfulness that I’ve rarely seen.

If you are in the position of creating your own analytics team—or even if you are an analytics team of one—her approach is a blueprint worth emulating.

Jamie is also modest. What she’s done in her role at JLL is impressive and I wanted to help share some lessons from her analytics team-building approach. Jamie’s Data Fluency team, a subset of her larger Business Intelligence team, is focused on facilitating the use of data throughout the organization. Here’s how she describes the objectives:

At the core of this team is a focus on the “last mile” challenges of analytics—bridging the gap between the data and the decision-makers. The Last Mile of Data is less about technology and more about people-to-people communication. Her team starts with this people-first perspective rather than the all-too-common fixation on the technical issues and tools of analytics. In learning about what she’s done, I bucketed her activities into six key lessons:

1. Build a team that understands the business as much as the data and tools;

2. Educate your customers;

3. Push solutions rather than wait for people to come to you;

4. Even great data products need to be sold;

5. Actively curate the portfolio of data products;

6. Always be proving your value.

Build a team that understands the business as much as the data and tools

A lack of a common language and understanding results in disconnects between data analysts and business decision-makers. Jamie prioritizes filling this knowledge gap:

In my experience, the real magic happens when someone who understands the business unites with someone who understands data and analytics. Most of my team, myself included, are new to this industry, and until we understand the business, we’ll only ever be ticket-takers who build whichever dashboards or automations we’re asked to. My goal is to up-skill BI professionals with the business-specific basics, at a minimum, so that we can connect dots our customers would have never thought to ask for.

Educate your customers

Jamie and team recognize that their analysis will only make an impact if the recipients of information have data skills themselves. To that end, the analytics team proactively delivers learning tools so business customers can become more data fluent.

• Library of Data Moments for brief drips of data education: Similar to the popular Safety Minutes/Moments we see as the common start to meetings, these micro-trainings are designed to weave organically into any meeting. Our intent here is find those that need Data Fluency/Literacy training and awareness where they’re at. We know we can’t wait for them to come to us.

• Data Fluency training program: This will be similar to other L&D content employees take at their company like leadership or presentation skills training. We have our modules defined and are nearly ready to release our first one. It is interactive and intentionally designed to inform participants first of the importance of being data savvy, not just at work but everywhere!

• Customer-facing BI guidebook: This is meant to address the repeat questions we often receive from customers about how to best engage with our team. It’s a laymen’s term quick guide that walks users through how to request access, ask for support, submit a request for a new report, etc. This has been a hit!

Push solutions rather than wait for people to come to you

Over and over, I hear from frustrated analytics professionals who create valuable data products, but can’t get their audiences to engage. One answer: bring it to them in the channels and times when they are willing to give attention. Jamie’s Data Fluency team has searched for opportunities to inject data into existing communication channels and connect with their business users.

• Data Trivia in Newsletters (with prizes!): Who says you can’t have fun at work? Not us! In another effort to meet the customer where they’re at, we’ve partnered with our communications team to create a new section in their monthly newsletter. We pose a data question and hold a random drawing for everyone that submitted the correct answer, and the winner gets $25 to our corporate store!

• Data Tips of the Week for Service Line newsletters & Client Portal tile: We added a new section to the portal we use at work that highlights a quick data tip. This is another effort to get front and center of where our customers live and breathe. If they see us (Data/BI) everywhere, our customers can only ignore us for so long!

Even the best data products need to be sold

Despite the best intentions to bring data into decision-making, business users are busy and distracted. Therefore, it is important to take extra steps to teach your users how to use these products and show why they should be excited about the impact. I particularly like Jamie’s focus on telling stories about “wins” because this is one of the quickest ways to encourage adoption.

• BI User Stories: What better way to bring new customers into the BI fold than have them hear from a colleague the wonders it’s done for them? Sharing a few first-person sentences about how BI is saving the day tackles a few things: the message increases awareness, makes it more approachable (because it’s coming from people they know with less bias than if it came from the BI team), and it helps prove our ROI because some of these include call-outs to metrics that have improved.

• Training Videos (big hitter: BI Portal Overview): The BI content (reports and dashboards) that we create lives on this BI Portal, so it’s imperative that people know how to navigate it. The purpose of creating this video is to make it quick and painless for people to both learn how to navigate but also what all is available for them. Since spinning up our Data Fluency team, we have found pockets within our customer base that don’t know a thing about BI or what we do. So, we start them at the beginning by acquainting them with what already exists.

• BI updates on all regional client-facing QBR’s and internal townhalls: One of the aims our Data Fluency team is to simply be more visible and cross more desks. One way we do this is by volunteering to present on large calls such as our Quarterly Business Reviews or internal townhalls, which so far has been eagerly accepted. Again, if we come to the customer, they can’t help but see us!

• Hosting dashboard walkthroughs: While we deliver training anytime we create a new BI product, we find that with turnover, the memory of that tool can atrophy. Hosting dashboard walkthroughs is our effort to remind people what’s out there today that they could leverage to improve their ability to make data-informed decisions.

• BI Office Hours (added recently): These are similar to the dashboard walkthroughs, but the agenda can range a bit more. In this bi-weekly call, we send an agenda in advance based either on feedback we know people want to learn about or that we think is relevant. BI Leadership is also on the line to field any questions from the wide customer base. This started small but the audience is growing.

Actively curate the portfolio of data products

Many organizations end up with more dashboards and reports than they know what to do with. And the pile of data products only seems to grow. Jamie has found ways to make the JLL data products easily searchable while also trimming those that don’t add value. Here are three approaches her team put in place:

• Customer-Facing BI Usage Dashboard with Recommendation Engine: This is designed for two main purposes. One is to give people, mainly managers, visibility into who’s using what (or isn’t). The other is to help people onboard more effectively. They can filter by their service line and see the most-used reports and dashboards. The viz will also recommend dashboards they should consider using (“People that use dashboard A also use B, C, and D the most”).

• BI Catalog to help customers locate the data, report, or dashboard they need: Our purpose here was to make searching for items easy. Our catalog is in excel which makes CTRL+F easy to locate key words. Users can also filter for their service line and see a list of what’s available. It also includes key details such as refresh timing, owner, key stakeholder, etc. Given the size of our scope and the customer base we support, everything our team does needs to work at scale, and this catalog helps us do that.

• Archiving process for BI products and reports: True to Lean methodologies, we often ask “does this add value” and if it doesn’t, we find a way to remove it. Our archiving process tackles two things: It reduced the technical debt by requiring fewer items be supported, and it leans out our offerings, thus reducing confusion amongst our customers. If there are too many choices, we risk them just walking away, so we want to keep their list of relevant reports as lean as we can.

Always be proving your value

Analytics and data teams can struggle to show the return on investment for their activities. In particular, it is hard to measure the value of the many informed decisions that you might be impacting. Don’t wait for senior leadership to start asking these challenging ROI questions. Jamie and team have proactively developed analyses and reports that explain their impact while also reaching out to stakeholders for feedback so they can continue to improve.

• QBR Decks for Global BI Leadership: These are requested and not something we volunteered to make but in hindsight we should have! These decks give us a chance each quarter to highlight all of the wins across the team in front of leadership. They’re also superb for referencing later and adding up as the year progresses.

• ROI Dashboard capturing BI’s value varietals: I will admit we are still trying to crack this nut but we are well on our way. Quantifying the full ROI of a BI team is a challenge because not everything we do is a simple cost-out or efficiency effort. That said, we capture a variety of different metrics each quarter aimed at telling our full story and articulating our full value.

• Quarterly BI CSAT Surveys: This was one of the first things we did when standing up the DF team. Leading by example, we wanted our actions to also be data-informed and we didn’t have any data…so we collected it ourselves. I have used the results from these surveys in a variety of leadership capacities to illustrate, with data, how many customers consider BI dashboards and reports as a critical part of how their team gets work done and equally as important, how they see their need for BI changing in the next 6 months.

If you are in an analytics leadership role, I encourage you to connect with Jamie Beason on LinkedIn to learn more.

15 Lessons from the Data Story Creative Process

What do you get when you put a Data Scientist and a Data Storyteller in a room full of executives for two days?

Sorry, no punchline…this is serious. The answer is The Data Story Creative Process (DSCP) workshop — a hands-on, case study-based learning event that teaches a framework for using data to drive informed action.

We played with data, explored insights, structured stories, and discussed the barriers to reaching our audience. Here is a tasting menu of the lessons we shared:

A repeatable process

Every data project is unique. Yet our methodology applies common steps and best practices to bring discipline and focus. The DSCP is a more thoughtful approach to solving tough problems with data.

Hands-on learning with real data.

We learned a lot from our workshop. Lesson 1A: People like to get their hands into data in a realistic scenario to apply the concepts and skills we are teaching.

Find your data story

Your data story exists at the intersection of your goals, your audience’s priorities and levers, and the impactful data insights you are able to find.

This is one of many places where we emphasize the need for focus.

Visualize for readability and shared meaning

When it comes to visualizing your data, you have two primary objectives:

Readability: How do you minimize your audience's effort to understand the visualization?

Shared Meaning: Does the visualization support and emphasize your insight?

Deliver more “Aha!” moments in every data presentation. Schedule your free data makeover >>

Guide your audience to actions

Actionable insights should be the goal of your story, even if that action is a need to gather more data. We discussed the characteristics of a data-driven action and the frustrations of presenting insights that can’t be acted upon.

The goals is to change the mind of your audience

Working with data is a matter of mindset as much as skillset. Most importantly, you want to understand the needs of your audience so you can tell a story that changes their perspective.

Where are you in the movie?

Charlie shared a fantastic analogy for considering how to analyze data. You want to think of the data as if you are at a particular scene in a movie. You can look back to understand what got you to that particular point. Then you can use models to predict where the story is going to end up.

Data is a team sport

Our workshop discussion underscored our belief that data is a team sport. You need different players — the analyst, the subject matter expert, the decision-maker — to work together from the beginning to chart out a successful path.

Preparing your data

Preparing your data is the hard work that needs to be done before the fun begins.

For our executive audience, we wanted them to appreciate the potential complexity and effort that data preparation can require.

Demystified data terminology

There are a lot of data buzzwords and abbreviations flying around: AI, ML, lakehouse, data engineering, storytelling. We took some hot-air out of these terms and discussed what they really mean.

Different sources of insights

Finding data insights is part of the “Play” stage in our process. And there are many tools and techniques to reveal those insight. We want to combine both data-led insights (e.g. modeling) with exploration that is guided by human understanding of the problem.

Kill your darling data insights

Analytical play-time is great, but at some point you need to evaluate and extract the most valuable and actionable nuggets. We provided an approach for sifting through insights to find those that are “story-worthy.”

Structure a data story

Data storytelling uses the patterns and expectations of traditional narratives to grab and keep the attention of your audience. We can use the classic three-act play structure to set-up and deliver on inherent story expectations.

The (Short) Attention Economy

With our full inbox and onslaught of text messages, it is hard to grab and keep the attention of your audience. We shared techniques that can help your data story stick: be unexpected, connect to emotions, be specific, and be relatable.

Advocate for your data products

Our workshop was about defining a problem to be solved with data and creating the data story that will lead to action. We closed with a final message: you need to step up to advocate for your results. Think of your dashboard, report, or analysis as a product, one that needs to be marketed and sold for people to get the value.

Want to know more about the Data Story Creative Process and our training programs? Lets talk!

The 7 Stages of Data Projects

Why do data projects take so long? It’s exhausting — finding data, cleaning data, identifying problems in the data, creating presentations, hitting resistance...on and on.

I’ve seen the struggle up close for over 15 years. It is my belief that the challenges of analytics have less to do with technology limitations and more to do with people challenges. The barriers often relate to Psychology, Sociology, Anthropology, and Mindsets.

We will often have clients who are energized to get started, but then disappear for months as they struggle with their data problems. I see people bounce back and forth from optimism to pessimism.

With that in mind, I wanted to offer a framework for thinking about the journey that both people and organizations go through as they tackle data projects. The framework describes the sequence of behaviors and emotions that people express. Getting stuck in these stages helps to explain why data projects can take so long:

1. Skepticism. Like anything that is new, people will start by questioning whether it is worth their time and effort.

2. Irrational Exuberance. The pendulum swings and people get (over-)excited, about what they can do with data. Reality may not match their growing expectations.

3. Confusion. Then back to Earth. When it comes time to embark on an actual data project, the uncertain grows. Where do you even start?

4. Discovery of Purpose. Getting to this step requires finding a small piece of the data potential that can be bitten-off first.

5. Doubt. Now that you’re committed to a direction, the reality of your data comes into play. Will you be able to find value and insights?

6. Denial. Even after emerging from stage 5 with progress, now you face an audience that may not be ready to change. Their skepticism is now your blocker to progress.

7. Acceptance. Finally, the data project comes to fruition, perhaps at a smaller scope than was originally imagined. Time to find the next opportunity.

I made this infographic as a visual display of this framework:

Download the infographic as a PDF.

How to Ensure Your Actionable Insights Lead to Action

“Actionable insights” is the Holy Grail of analytics. It is the point at which data achieves value, when smarter decisions are made, and when the hard work of the analytics team pays off. Actionable insights can also be elusive — a perfectly brilliant insight gets ignored or a comprehensive report gathers dusts on a shelf.

The myth persists: If you build it, they will come. The purity of your insight will compel your audience to action.

Of course, that’s not how it works. There is rarely an analytical finding that is, by itself, so obvious, clever, and compelling that other people in your organization are forced to act.

The reality is that an analytic insight needs to be nurtured and sold. Like a new product, people need to understand it, see how it fits into their life, compare it to the status quo, and see the case for making change.

Your actionable insight needs to connect to the things that are important to your audience. The following diagram shows the eight things that you’ll want to consider to evaluate whether your insight is likely to deliver the action you are hoping for. Consider it a checklist, starting from the top (“Attention”) and proceeding clockwise.

These are the stumbling blocks that we’ve seen as perfectly intelligence and researched insights go to a pre-mature grave. A good insight is a terrible thing to waste.

Keys to Data Fluency: Shared Understanding

Building a Data Fluent organization requires getting everyone on the same page. This common understanding spans everything from cultural expectations to accessing and using data. Below I’ve outlined six areas where creating alignment will have long-term benefit…and where you can go to get started today.

Photo by Nicole Baster via Unsplash

Shared Expectations

Leaders need to define and communicate how they expect data to be used in the organization. As I’ve written time and again, it incumbent on executives to set the standard for data culture.

Where to get started: The Data Lodge provides guidance and training to help data leaders build their organizational culture and capabilities.

Shared Skills

Your team needs the know-how to understand, analyze, and communicate data. These skills are not always a prevalent as we’d like.

Where to get started: DataLiteracy.com provides a growing collection of courses for data skills.

Where else to get started: Quanthub offers an adaptive educational platform focused on building data skills in your organization.

Shared Definitions and Terminology

You want everyone in your organization to know what is meant by the data being shared. Who qualifies as a lead? What is an active customer? How is revenue calculated? Without arriving at shared definitions and terminology, your data discussion will get stuck in fruitless debates.

Where to get started: There are many high-tech Master Data Management solutions…not the place to start. Create a shared document where you define: 1) how your key data/metrics are calculated; 2) where this data comes from; 3) how this data might be improved. Link to it when you present data in a dashboard, report, or data story.

Shared Data Access

Increasingly we’ve run into IT teams who have found a way out of the endless back-and-forth data requirements cycle. Instead, they generate frequently-updated and thoroughly-tested tables for analysis. Now business users can have the flexibility to create with less risk of misinterpreting data.

Where to get started: One of the best tools we’ve found for this is Keboola, a flexible platform for connecting to data, ETL, and providing a data catalog.

Shared Metrics and Goals

Data fluent organizations have a shared set of key metrics and can explain how these metrics link to organizational goals.

Where to get started: We appreciate the simplicity and clarity of Matt Lerner’s Metrics that Matter. His framework provides a roadmap for defining your most important metrics.

Where else to get started: TeamOnUp provides guidance and software for aligning around shared goals and defining clear responsibility.

Matt Lerner | Metrics that Matter | BoS USA Online 2020 from Business of Software Conference

Shared Data Products

A data fluent organization leans on a curated set of data products — dashboards, reports, presentations — for focus and insight.

Where to get started: Juicebox is a lightweight and versatile solution for business users to create reports, presentations, dashboards, and data stories. It is designed to make creating and sharing easier than anything else on the planet.

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Interview with SourceForge: Bringing Data-driven Decisions to a Broad Audience

I recently spoke with the team at SourceForge, a leading platform for the distribution and discovery of software solutions. The interview ended up summarizing our journey as a company to transform how people communicate with data. Here’s the transcript:

SourceForge: You have said that the challenges faced by the analytics industry are more social than technical. What did you mean by that?

We’ve been in the analytics space for nearly two decades. The technology has advanced, particularly in advanced analytics, but the same problems persist. There is still a lack of engagement with data by many people in organizations. People have discomfort with using data to drive everyday decisions. That stuff doesn’t get solved through more features. And for many leaders, there is a feeling of frustration for all the money they have spent on data projects. Where is the payback? How long are they going to have to wait? We can’t climb our way out of these problems by always betting on the machines to do more.

For all the advances in artificial intelligence and machine learning, it seems to me that the people-side of analytics continues to be neglected. When I say people-side, I mean: what skills do everyday information workers need to be successful? How does the culture of an organization need to change to embrace using data? How do we meet people where they are to help them become more data fluent?

SourceForge: Given that, why have you focused your company on building yet another tool?

Fair point. It is because creating change in the workplace is often the intersection of new behavior and new, easier ways to enable those behaviors.

Take Slack and how they transformed workplace communication. Our email inboxes were exploding, and adding more features to email clients wasn’t solving the problem. Slack came along and re-thought how to make it easier for people to collaborate in teams.

That’s how we think about Juicebox. There needs to be a fresh approach to how people take spreadsheets of data and turn it into something useful. The new approach needs to put people first, not by making it more complex or feature bloated.

SourceForge: But there are a lot of tools for visualizing data. Why did you feel like the world needed another one?

The world certainly doesn’t need another dashboard-creating tool, that’s for sure. Nor do we need something to try to replace the visual analytics behemoths Tableau and PowerBI. Those tools are essentially Excel on steroids. More capable. More visual. And more complicated.

What these tools don’t focus on is how do we make sure the data gets communicated effectively. That’s the missing link. We sometimes call it the last mile of analytics. What has been missing is a solution that provides an easy, accessible bridge between people who work with data and the minds of the decision-makers who should understand that data.

With Juicebox, we created a solution that is lightweight and accessible to everyone. It is easy to learn, easy to get started. The everyday information worker doesn’t want to have to get an advanced certification to be able to visualize, present, and share data in their organization. They need something radically simpler. But also something radically more powerful than the Excel and PowerPoint that they are currently using to present data.

That’s where Juicebox fits in. We experienced first-hand the frustration people feel. We set out to deliver a better mousetrap for communicating data.

SourceForge: Let’s talk about those people. What have they struggled with, and how does Juicebox help them?

ZG: I believe there is a silent majority of people in the workplace who want to do more with data but don’t yet have the skills or tools.

Think of it like all the want-to-be cooks who admire recipes online but find it too much effort to gather all the ingredients and learn to make the meal. For these people, meal prep solutions came along, like Blue Apron and HelloFresh. Suddenly anyone could whip up a darn good at-home meal. What did it take? It took some guidance, some simplifying of the recipes, and more convenience.

It’s exactly the same for data in the workplace. Sure people have Excel, and maybe access to a powerful analytics BI platform…but that doesn’t make it easy. With Juicebox, we want to make it easy for these people to whip up something delicious with their data.

SourceForge: You are also the author of a book called ‘Data Fluency’ in which you present a path toward more effective use of data in organizations. How does your product fit into this framework?

We wrote that book because it was clear that many organizations were struggling to really unlock the power of their data. I’m not talking about hiring more data scientists or applying machine learning models. They just want to know what is most important, define key metrics, see trends, and find insights they could act on. It is the world of small data that still has so much untapped potential. We saw that the issues were about mindset and skillset, not technology.

In our book, we propose four pillars that an organization needs to build to become data fluent. The pillars are: data consumers that are data literate; data authors that know how to communicate effectively; an organizational data-driven culture; and an ecosystem for designing and sharing what we call data products.

Juicebox is a key that can help unlock some of these challenges. It gives data authors the most user-friendly solution for communicating, and it serves as an integral part of a data product ecosystem. More than ever, we believe that we need to make data a medium for communication. Juicebox is one piece of the puzzle.

Keys to Data Fluency: New Decision-Making Behaviors

How do you know that your organization is becoming more data fluent? You’ll see new behaviors such as the language people use, the things they focus on, and the way meetings are run. Data analysis and key performance measures are elevated from after-thought to starting-point.

Here are five behaviors that you should start to expect from your team as your data fluency matures:

Everyone Knows the Key Metrics — and Debates Them

Data fluent organizations have a common understanding of how progress is measured. People at all levels have become familiar with these metrics and think about how their work relates to these numbers.

At Juice, we have a North Star Metric (the unchanging measure of progress) and three “key drivers” that are the current bottlenecks or leverage-points for improving the North Star Metric. We encourage discussion about how to best measure and what the results imply.

Product Features or Investments Are Weighed Against Key Metrics

Data fluent organizations evaluate new investments by how they are likely to impact key performance metrics.

At Juice, our product roadmap is increasingly guided by our quantified understanding of user behaviors. We look for the most frequent blockers to success and consider new features as ways to knock down those walls.

Investment ROI calculator http://labs.juiceanalytics.com/valuation/index.html

Anecdotes Get Tested

We talk about data storytelling all the time. However, individual stories are often just a clue to a pattern — or simply a one-off outlier.

Does it happen often? What is the implication? Why did it occur?

At Juice, we’ve discovered all sorts of user behaviors in Juicebox that we had not anticipated. For example, our European users often load CSV (comma delimited files) that are delimited by semi-colons. We discovered this through one user’s story, but then validated the frequency through data.

via Saturday Morning Breakfast Cereal https://www.smbc-comics.com/index.php?db=comics&id=2159

Important Processes Get Focus Through Data Products

For each of the priorities in the organization, someone should create a data product that sheds light on the progress toward this goal. The data product may be a quarterly summary of results or a real-time dashboard of operations.

Is there an important element of what your organization does that is not transparent and could benefit from an effective data product?

One-off Analyses Bloom Everywhere

Data fluent organizations aren’t satisfied with tracking data. People want to get to The Why. They are hungry for data sources that will help them explain customer behaviors, operational issues, and marketing performance. They want to put key metric results into context: What is the goal? How does that compare to industry benchmarks?

At Juice, our focus on key metrics has spawned dozens of analyses to understand what it takes to get a new user to succeed with Juicebox.

Photo by keith davey on Unsplash

Keys to Data Fluency: Matching Tools to User Needs

A data fluent organization should have a massive appetite for data. As you build your data fluency in front-line decision-makers and create a vibrant ecosystem, the demand for data products will grow. And if there is one truism in analytics, it is:

Good analytics generates better questions.

Discover & share this GIF with everyone you know. GIPHY is how you search, share, discover, and create GIFs.

In what form do you answer the growing array of questions and needs? What data solutions or products do your data consumers needs? There are many choices:

• Dashboards

• Reports

• Self-service BI tools

• Predictive models

• One-off analyses using slides

• Spreadsheet models

It is a confusing array of ways to deliver data to these data consumers.

What’s the right tool for the job?

Of course, there isn’t a single answer; it depends on the specific needs. Start by considering these two dimensions:

1. How much flexibility and control does the data consumer need? Do they need to be able to dig deeply into the data, or can results be shared with a static presentation of insights?

2. How much will the raw data be enhanced with analysis, modeling, and pre-digested insights? For some audiences, simply knowing the trend of key metrics is sufficient.

Across these two axes, it becomes clear there are a wide variety of different forms of data products.

Take the marketing function as an example:

• Analytics Tools (upper left): A Marketing Analyst wants to explore the performance of different advertising campaigns to understand what creatives are working best.

• Data Storytelling (upper right): A Director of Marketing needs to present a data-driven plan for spending to convinced the executive team to allocate budget.

• Data Access (lower left): A Data Analyst needs to extract data from a 3rd-party platform to explore the behaviors of new users.

• Performance Reporting (lower right): The CEO wants an overview of marketing performance to share with the sales, product development, and the Board.

With so many different needs and use cases, it seems evident that there isn’t just one tool that can fill all these situations. My friend at GoodData, Roman Stanek, has been talking about ‘Data as a Service’, the transformation from traditional, tightly-coupled data platforms to a new model:

The data industry now has a unique opportunity. Cloud-based data infrastructure can allow every decision to be data-driven. And as both people and machines make decisions today, this new infrastructure needs to support automated decision-making as well. We need to break down the monolithic nature of existing BI tools, and we need to deliver Data as a Service to every device and person so that access to data becomes truly pervasive.

He recognizes the diverse needs of data consumers that we see as organizations become more mature in their data fluency. A restrictive dashboard tool isn’t the right answer for telling a data story nor does it serve the data scientist who wants to spend less time extracting data and more time exploring the data.

If you’ve made the commitment to becoming a data fluency organization, you’re already thinking about how to better serve all the people who might be working with that data. Mapping the right tool to each specific job-to-be-done is an essential step.

Keys to Data Fluency: Believe in Your Front-Line Decision-Makers

In 2007, Professor Thomas Davenport wrote an influential book called Competing on Analytics: The New Science of Winning. At the time, he stoked a smoldering ember into a flame by examining the power of analytics to improve organizations. The book was a catalyst for a generation of business leaders looking to find value in their data.

For all its influence, we had a quibble with Davenport promotion of a centralized model for analytics, where the data is managed at an enterprise-level by a cadre of data scientists building complex models to drive decisions throughout the organization. He believed that the best organizational structure is:

central analytics and data science organization, based in a Strategy function, with analysts assigned to and rotated among business units and functions: This is, I think, the optimal structure and home for analytics and data science. The central function allows for a critical mass of quants and for central coordination of their skill and career development. It should be a shared service, where anyone with the money and the high-priority requirements can get the help they need.

To this day, the question of where analytics should happen is still unclear for many organizations. Research by Deloitte shows that many organizations are confused or conflicted:

We are advocates for bringing analytics to the front-line decision-makers of your organization. The marketers, operators, managers, salespeople, and customer service teams all need to understand data to be better at their jobs. For us, management guru Peter Drucker sums it up best:

Most discussions of decision making assume that only senior executives make decisions or that only senior executives' decisions matter. This is a dangerous mistake. Decisions are made at every level of the organization, beginning with individual professional contributors and frontline supervisors. These apparently low-level decisions are extremely important in a knowledge-based organization.

Senior leaders in your organization may make the so-called “big strategic” decisions, in effect choosing the path to travel down. But the speed with which you travel toward your goal and stay on course when distractions arise—these decisions are controlled by your front-line staff.

This belief that data can inform better decisions throughout an organization is part of our motivation for Juicebox.

Data needs to be formed into targeted, purposeful solutions to be of use to most people. The common practice of delivering a general-purpose analytical tool to end-users and expecting something useful to happen with it typically results in little added value. People are busy with their jobs. The last thing most information workers have time for is to learn how to use a new analysis tool, figure out what data might be relevant to them, and dive deep into a data analysis exercise. It is the difference between throwing someone an anchor and throwing them a lifeline.

We have also made clear our belief in people over technology. There are many suitable technologies for capturing, managing, manipulating, and presenting data. Better technology or tools is seldom the problem. Actually, many of the data challenges that required large information technology investments a decade ago can be done quickly and economically today.

The challenges are in the skills and collaboration of the people that use those technologies. Poor communication, misalignment of values, limited data communication skills, unfocused messages…these are the challenges that most organizations we work with face today. The good news is that these are all solvable by focusing on the skills of your people.

Keys to Data Fluency: Creating the Data Product Ecosystem

For data-driven thinking to flourish in your organization, you need to give people easy access to ‘data products’ that will answer their pressing questions.

Easier said than done.

In fact, for most organizations, the collection of dashboards, reports, and analysis tools feels like a chaotic mess. When we worked for a global manufacturer, a survey of information workers revealed that the top problem was an inability to find data products that served their needs. Also a big concern: the quality and usefulness of those data products.

This is the challenge of creating a data product ecosystem. Creating a vibrant ecosystem for data products requires processes and tools. Processes set standards and ensure that the right priorities and qualities are built into every data product. Tools gather data, visualize the results, and distribute data products to users. Here are the six conditions (“the Six Ds” shown below) that are essential to building this ecosystem:

Demand

What are the most important areas that would benefit from the insights and guidance of better data?

There is nothing so useless as doing efficiently that which should not be done at all.

—Peter Drucker

We begin with the end in mind. The consumers of data have needs. A healthy ecosystem will support those needs through the right data products. Discovering the information that will best serve the organization is the first step.

Understanding data consumer demand is not a one-time endeavor. It requires a process of continually mapping the important decisions made by the organization and evaluating whether and how data can improve those decisions.

One framework to use: Map the expressed needs of your data product consumers into a matrix to evaluate a) whether the data product will bring real value to the organization; b) whether a solution can truly drive better decision with data. This model will reveal data product concepts with the potential to deliver the greatest impact.

Design

What processes and tools can help ensure the effective design of data products?

Less than 30 percent of the potential users of organizations’ standard business intelligence (BI) tools use the technology today. This low take-up is due to the fact that longstanding tools and approaches to BI are often too difficult to use, slow to respond or deliver content of limited relevance.

—Gartner

The three reasons cited by Gartner for this problem are:

1. Ease of use (“is hard to work with”)

2. Performance (“users are frustrated by delays”)

3. Relevance (“does not express content in line with their frame of reference”)

The first and last reasons link directly to issues of poor data product design.

In our role as dashboard and analytical application designers, this is an area that is close to home. We see it all the time: reports and dashboards that lack focus and a message that targets their audience, which is often undefined. We see poor choices in data visualization that distract from the important elements in the data and put the burden of deciphering meaning on the readers. We see data products that lack an obvious starting point and logical flow to conclusions.

Poor design is wasteful. It results in solutions that users don’t want to use, as noted by Gartner. It wastes the audience’s valuable time as it struggles to comprehend the data. And it wastes the development and distribution efforts necessary to deliver the data product.

Juicebox delivers beautiful data presentations with good design decisions built-in.

Develop

What processes and tools support the efficient production of data products, including gathering multiple data sources, presenting this data, providing user customization, and delivering the information to data consumers?

Ideally, you want to have a small set of data tools that support the variety of types of data products your organization needs. A single solution is unlikely to offer the breadth of capabilities necessary. In our experience, four to five tools for data presentation are usually sufficient for most organizations.

There are many forms your data products may take. And for every form, there are many technology options. However, here are some common features that are worth evaluating in almost every case:

• End-user customization—Some presentations may target a single audience. This is the exception to the rule. Most often, a data product goes out into the world alone and is used by many people, each of whom comes from a unique perspective. Whether it is their department, region, or products, all audience members will want to see data that is customized and scoped to their situation. Many interactive applications can support this ability to filter the relevant data.

• Sharing support—Data should spur conversation. However, some solutions for data products create an isolating environment. The data product should make it easy to share, discuss, and capture insights— whether the discussion happens online, offline, on a desktop, or on mobile devices.

• Quality visualization—It matters how data is visualized. Clean, clear charts can make it easy for readers to quickly understand the data. The default settings for data visualizations should adhere to the fundamentals shared by well-known data visualization authors like Stephen Few and Edward Tufte.

• Fit workflows—Finally, it is important that data products integrate into how people do their jobs. If the consumer of data is constantly on the run, bombarded by information of all types, an effective data product will deliver simple, narrow content to this person. If the consumer wants to drill deeply into the data to understand underlying assumptions, the functionality should exist to allow for this need.

Discover

How can you help people discover the many data products in your organization and find the right information for their individual needs?

Data product discovery should mirror the capabilities of online content subscription services. Podcasts, blogs, or Twitter, all have established features for ensuring an audience can find and access the latest content. These include:

1. Searching of metadata about the content, including title, author, and description

2. Browsing of content sorted into categories and ranked by popularity or ratings

3. Surfacing of related content based on the consumer’s expressed areas of interest

4. Subscribing to allow consumers to sign up to receive updates to content

5. Automated pushing that allows consumers to receive updated content automatically rather than having to remember to return to the source

6. User permissions to control who has access to applications and content

Browsing Spotify

Discuss

What capabilities encourage data consumers to take the insights they find in the data and share these insights with others?

The best data ecosystems don’t simply assume discussions will occur. They encourage discussions through mechanisms for sharing, capturing, and saving information and insights. The discoveries found in the data are treated as precious assets—after all, they are the purpose of all the effort put into creating data products. Finally, the ecosystem should encourage people to take action when the discussion is complete.

Some organizations consider data products a one-way information broadcast. They implicitly assume that a dashboard is intended to deliver an information result, not drive action.

Discussions on data—like most of data fluency—are more a social and human problem than a technology problem. The technology approaches may be simple. For each data product, create a document or folder for capturing insights. The document may simply be screenshots of the relevant part of the content along with an annotation explaining why it is interesting. As a historical artifact, this document will reveal patterns of common issues and best practice approaches for responding to those issues. More important than a complex technology solution is an organizational culture that encourages dialogue and action after the insights are first found.

Source: https://www.ontotext.com/knowledgehub/fundamentals/dikw-pyramid/

Distill

How do you filter out the irrelevant content and provide feedback to enhance those data products that remain?

The scourge of data in most organizations is the ever-growing collection of reports that get generated month after month. New reports are created but seldom killed. The mass of data products quickly becomes difficult to navigate and the right information is hard to find. Even for the data that has found a suitable audience, there is seldom a feedback loop. The direction and needs of the organization may change, yet the content does not change to fit evolving demands.

Data products should be living documents. They should improve over time or be removed if they are no longer relevant. It is a matter of survival of the fittest.

Data fluent organizations recognize that too much content—particularly data content—will clog up the channels of communication. The data products must be distilled to the essential information. You want to clean out distractions and emphasize the most useful remaining parts.

To filter and clean your data product ecosystem, you need processes in place to gather feedback from your users. The feedback needs to impact how data products are designed and produced. There are at least three ways to continuously distill the best data products:

1. Create a lightweight feedback mechanism, like a simple “star” system.

2. Track usage both on the volume of usage and the levels of engagement.

3. Conduct content reviews by gathering the audience for a product and having a focus group-style discussion.

Because knowledges are so specialized we need also a methodology, a discipline, a process to turn this potential into performance. Otherwise, most of the available knowledge will not become productive; it will remain mere information. To make knowledge productive, we will have to learn to connect.

—Peter Drucker, Pro-Capitalist Society

It is hard work to create an environment that enables the creation of high-quality data products, ensures those products get into the right hands, and has mechanisms for self-improvement. But without doing this work, all the data investments your organization makes will struggle to reach the important decision-makers and consumers of that data.