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.

A 12-Point Checklist for Public and Open Data Sites (with Examples)

Let the data run free! Government organizations, academic institutions, non-profits, and even passionate sports fans are gathering and sharing valuable data sets with the public. The topics are wide ranging, from climate change to health to inequality to happiness. It is a powerful way to support a cause and encourage data-driven analysis.

These open data sets are set loose on a website in hopes that interested visitors will come flocking. How do you make that site as effective as possible? Simply posting the data in a searchable format isn’t enough. To achieve impact, you need to make it easy to understand, manipulate, and explore.

The following checklist is a collection of best practices and reminders for your open data project.

1. State Your Purpose

To start, you need to address the question: WHY this data? And WHAT can a viewer gain from using the data? The following examples feature prominent statements about the purpose.

https://www.oecdbetterlifeindex.org/

https://blackwealthdata.org/

https://champshealth.org/

2. Segment by Audience or Topic

There are always many ways that someone could analyze and explore your data. Do them the favor of explaining HOW the data can be used. The best sites provide separate sections based on different users of the data and/or topic areas.

https://www.earthdata.nasa.gov/

https://dataunodc.un.org/

https://blackwealthdata.org/

3. Provide example insights

Naturally, you would like your visitors to find their own insights. However, they will benefit with a gentle nudge toward the types of insights that are available in your data. In the following examples, a few key insights are featured as a teaser to dive deeper into the data.

https://www.boxofficemojo.com/

https://www.nashvillehealth.org/survey/data/

https://blackwealthdata.org/

4. Let users find their own data story

Too many open data sites simply provide downloadable access to the data. This is a missed opportunity, particularly for visitors who may not have advanced analytical skills. Interactive, exploratory visualizations give your visitors a playground to find their own insights in the data. This is where the leading data storytelling platform can lend a helping hand.

https://seer.cancer.gov/statistics-network/explorer/application.html

https://www.oecdbetterlifeindex.org/

https://www.nashvillehealth.org/survey/data/

5. Encourage sharing of insights

If your site enables your visitors to find insights in the data, the natural next step is to let them share what they have found. Ideally, you’ll want the ability to capture specific visuals and share via social media.

https://www.oecdbetterlifeindex.org/

https://news.crunchbase.com/web3-startups-investors/

6. Use simple, intuitive visualizations

Keep in mind that the visitors to your data site are just coming up to speed on your data. Complex visualizations and sophisticated analysis tools are likely to overwhelm them, and cause them to bounce. You want to lower the cognitive load by finding simple and familiar ways to present the data.

https://www.nashvillehealth.org/survey/data/

https://www.movebank.org/cms/movebank-main

https://www.oecdbetterlifeindex.org

7. Include real-life examples

By its nature, data is an abstraction from reality. It summarizes and aggregates many individual data points to find trends and insights. However, this abstraction can separate your visitors from the specific things that are represented in the data. Take a moment on your site to reconnect your visitors with the actual subjects of the data.

https://data.unicef.org/

https://dmp.unodc.org/

8. Explain your metrics

For many public data sites that are focused on a particular topic, there will be a few key measures of performance. You want to ensure your visitors have a full understanding of these metrics so they can interpret the results accurately. We found many sites that do this well; others fail to provide the labeling or context to clarify what the data means.

Not so good: https://climate.esa.int/en/odp/#/dashboard

9. Explain why the data is credible

There is a lot of data out there. Why should your visitor trust what they are seeing? Take the time to explain the diligent work and research that went into gathering your data.

https://blackwealthdata.org/

10. Make the raw data accessibility for advanced users

For the novice data users, interactive visualizations are a great entry point into your data. The advanced users will have their own ideas about how they want to manipulate the data. You’ll want to give these users the ability to search your catalogue of data and download the raw data files.

https://search.earthdata.nasa.gov/

11. Provide resources to learn more

Data is great — but it is better with context. You want to include resources that give interested visitors the chance to learn more with relate research and other content.

https://www.earthdata.nasa.gov/

https://blackwealthdata.org/

12. Don’t forget the outreach and marketing

You’ve made an amazing public data website. The job’s not done. You need to make sure people know it exists. There are a lot of options: search advertising, organic search, social media, and lists of open data sources. A great starting point is to reach out to sites that relate to your topic area and make them aware of your data as a valuable resource.

Deliver more “Aha!” moments in every data presentation

What Can WordleBot Teach Us About Actionable Data Insights?

I’m a Wordle obsessive. Which is to say, every morning I find myself staring deep into my coffee in search of an elusive 5-letter word.

The New York Times (who bought the word game from software developer Josh Wardle for $3 million) knows their audience. We may be playing with words, but the analytical nature of this game is the appeal. It is a game of odds and mathematical deduction as we try to reduce the potential options available.

To appease us (and sink the hook a bit deeper), the NYT recently released WordleBot. Here’s how it describes itself:

I am WordleBot. I exist to analyze Wordles. Specifically, your Wordles.

In the next slides, I’ll examine your puzzle and tell you what, if anything, I would have done differently. Words I especially recommend are marked with my seal of approval. And, if you’re curious, I’ll show you the math behind my recommendations.

Below is an example of what WordleBot shows about a game (I chose a particularly lucky game for me).

WordleBot delivers data insights in a particularly clever way. Rather than guiding you to an answer (I built a data app for that — and it immediately sucked the fun out), it takes a different tact. It guides by teaching. Let’s see how:

There is so much good data communication here:

1. WordleBot teaches me how to think about my Wordle performance by defining different measures that impact success. Getting to the answer is a combination of Skill and Luck with the goal of reducing the number of potential Solutions Remaining.

2. In straightforward language, it describes my performance on the first guess.

3. Rather than telling me what I should have done, it provides alternative options and explains how the outcome could have been different. I can play out different scenarios.

After stepping through the series of guesses, WordleBot summarizes my choices compared to the optimal, data-driven choices.

This is a non-traditional way of sharing data insights — and something worth learning from. If you approached delivering data insights like WordleBot, you would focus less on telling people what they should do or, worse, what they should have done. Instead, you would ask yourself, how can I teach by showing different decisions and explain the resulting outcomes?

In other words, show how to think, not just what to think. In this way, the role of data in informing decisions will become evident through the evidence.

Brace your audience for impact. Get started with Juicebox

A Guide to Building Better Data Products

At Juice, we’ve helped our clients launch dozens of data products that generate new revenue streams, differentiate their solutions in the market and build stronger customer relationships. Along the way, we’ve learned a lot about what works and doesn’t. In this series I’ll take you through what you need to know to design, build, launch, sell and support a data product.

Part 1: Getting Started

The first step in building a great data product is to pinpoint a customer need and determine how your unique capabilities will solve for that need.

A successful data product lies at the intersection of the three circles in the following Venn diagram:

1. Your customer’s pain point, an urgent problem they want to solve;

2. The characteristics of your data which can be brought together to solve that problem;

3. The capabilities you have to enhance the value of the data to make it as useful as possible.

Take the Academic Insights data product we designed and built for US News and World Report as an example of finding this intersection. (1) Their customers, university administrators, needed to understand how they compare to peer institutions and where they could best invest to improve their performance and stoke student demand. (2) US News was sitting on decades of detailed survey data and rankings to compare universities of all types. This data was unique in its breadth and historical coverage. However, the data was essentially stored in old copies of the paper magazine, not a format that was conducive to delivering insights to their target audience. (3) That’s where our data visualization and user experience capabilities helped them turn this data into a web-based analytical tool that focused users on the metrics and peer groups they cared about.

Let’s dive a little deeper into those three elements:

1. Pain Points

We’ve noticed a temptation with data products to forget the cardinal rule of any product: it needs to solve a specific problem. Without this focus, a data product comes in the form of a massive 100-page PowerPoint deck or a collection of raw data tables. There may be value in the data, but it is clear the product manager hasn’t thought deeply about their customers and what the data can do to solve their problems. I spoke to a credit card executive recently who mentioned how his bank spent huge sums of money on benchmarking reports. Despite his deep experience, he was unable to make sense of the reports he was sent. These are lost opportunities to deliver powerful data products.

“Your users are your guidepost. And the way you stay on the right path in the early stages of a startup is to build stuff and talk to users. And nothing else.” -- Jessica Livingston, co-founder of Y Combinator

With data products the core question of your user is: What information or insights will let you make better decisions and perform better in your job? 

Look for those unique situations where indecision, ignorance, or lack of information are blocking smart actions. Rather than solving your user’s pain, you need to enable them to solve their own pain. Physician, heal thyself.

2. What’s unique about your data?

The foundation of your product should be data that is somehow unique, differentiating, and valuable. In our experience, the right raw materials can come in a few different forms:

Breadth: Do you have visibility across an entire industry? Or population segment? Breadth allows you to provide benchmarks and comparisons that aren’t otherwise visible to your customers. One of our clients has data on the learning activities of more than 60% of all healthcare workers.

Depth: Can you explore deeply the behaviors of individual people, companies, or processes? By drilling into these activities, you may have the power to predict future behaviors or find correlations that aren’t visible to others. Fitbit tracks massive amounts of personal activity data from each individual user.

Multiple data perspectives: Are you in a position to combine data sources across industries or connect disparate data sources? By bringing together different perspectives on your subject, you may be able to answer new types of questions or explain behaviors through a multi-faceted perspective.

Naturally, having breadth, depth and multiple perspectives is best of all. Companies like Google, Apple and Amazon have profound data assets because they can both see human behaviors across a large audience and they know a lot about each individual.

3. Your value-added data package

It is seldom enough to create a data product that is simply a pile of data. That isn’t to say we haven’t seen many companies that believe that a massive data extract represents a useful solution to their customers.

People don’t want data, they want solutions.

How are you going to turn that data into a solution? There are many paths to consider:

• Visual representations that reveal patterns in the data and make it more human readable.

• Predictive models to take descriptive data and attempt to tell the future.

• Industry expertise to bring understanding of best practices, presentation of the best metrics, analysis of the data, and thoughtful recommendations. Bake your knowledge of the problem and the data into a problem-solving application.

• Enhancing the data through segmentation, pattern recognition, and other data science tools. For example, comments on a survey can be enhanced with semantic pattern recognition to identify important themes.

• Enabling users with features and capabilities to make them better in their job. The user's ability to analyze, present and communicate insights can be a value-add to the raw data.

If you can determine the right recipe of customer need, data and value add, then you've gone a long way toward defining the foundation of your data product. But before getting down to designing the data product, you'll want to get the right people in place.

4. The right product manager

We’ve helped launch data products in many industries including healthcare, education, insurance, advertising and market research. The most important factor in turning a concept into a business is a quality product manager. The best product managers have a vision for the product, understand the target customers, communicate well, are definitive in their decisions and recognize the reality of technical trade-offs. For a more complete list of general product manager skills, check out this Quora answer.

For data products, we’d emphasize a few more skills. The product manager needs to understand the data, what it represents and the business rules behind it. It helps if she is a subject matter expert, but if not, she should know when to bring in more expertise. Finally, she needs to understand the technical challenges involved with building a data product and be able to weight the impact of changes (which are often necessary as you learn more) against the benefits of launching sooner and gathering customer feedback.

5. Get stakeholder buy-in early

Kevin Smith of NextWave Business Intelligence (a consultancy focused on data products) warns: “Get the critical stakeholders involved and in agreement early or you’ll end up reciting the history of the project and why key decisions were made many times for many people.”

Launching data products is a journey that doesn’t end at the product launch. It also can push your organization into new and uncomfortable ground. These realities highlight the need to build broad support early in your process. Ask yourself:

• Is IT on board to provide development support, data access and data security resources and sign-off?

• Is the COO ready to provide resources after launch to support and maintain the product?

• Is your legal team confident that the data you’ve been collecting and incorporating into your data product is available for this new purpose?

• Is the marketing team ready to support a product launch that includes all the resources, collateral and creativity required of any new product?

• Is the sales team in place to understand the product, the target audience and establish the sales framework for pushing the product?

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“The secret to getting ahead is getting started.” 
― Mark Twain

For data products, this means finding your sweet spot at the intersection of customer needs, your data, and data product value add. And then getting the right people lined up to make your product a success.

Part 2: Development

If “Data is the Bacon of Business” (TM), then customer reporting is the Wendy’s Baconator. Sure it contains bacon, but nobody is particularly happy with themselves after eating it.

In a recent blog post, we described the differences between customer reporting and data products. Those differences result in some very different functional requirements. In particular, data products require more C.L.I.C. D.R.A.G.

• Context — Benchmarks, comparisons, trends, and/or goals that encourage decision-making.

• Learn — Help and support features to train users to get value from the information.

• Integration — Connections with other software systems to integrate with data and enable operational actions.

• Collaboration — The ability to save insights and communicate them with other people. Decisions aren’t made on an island.

• Documentation — Because data products live on and touch many people within your organization.

• Reporting — To track usage of the data product.

• Administration — Features to manage users and control permissions.

• Guidance — To point users to the most effective ways to explore and understand the data.

This collection of capabilities gives some indication of the gap between your standard customer-facing reporting and a complete data product. To accomplish all of these, you’ll need more than a talented BI report writer and access to your database. In our experience, the recipe for building a successful data product is dependent on a number of specialized roles.

Product Manager

The Product Manager sets the vision of the product. He gathers the necessary resources to make the team successful and builds organizational support for the product.

UI/UX Designer

The UI/UX Designer understands the user’s workflow and how to best guide the user to decisions. She crafts the interface and interactions to make the data intuitive. She's also in charge of design application styling and all visual elements.

Business Analyst

The Business Analyst translates application design into technical and data requirements. She's responsible for documenting business logic as product decisions are made.

Front-end Application Developer

The Front-end Application Developer's role is all about building interface elements, interactions, and data visualizations.

Back-end Application Developer

The Back-end Application Developer does everything the Front-end Developer does, only backward. Just kidding! But he does build the application server environment and define data queries to support UI interactions.

Data Guru

In addition to having the coolest title, he provides access to raw data sources. He understands and communicates the meaning of data fields and calculations to the development team.

Data Scientist

The Data Scientist defines the questions that will help end-users make better decisions. She enhances data through predictive modeling and other advanced data analytics techniques.

Technical Architect

He's the general technical architecture of the product, responsible for figuring out how the application connects to data sources and integrates into other systems.

Quality Assurance Engineer

The Quality Assurance Engineer evaluates whether the data product meets the need and requirements set out in the design process. He also tests data accuracy and product functionality.

It's a big load. That’s why you might want some help before going at it alone. At Juice, we've built a technology solution and an expert team that fills out many of these requirements. We have a platform, Juicebox, that ensures your application is a first-class user experience. Combined with our experienced design and implementation teams, we’ve got many of the resources covered. Our clients bring the product vision; we make it happen.

Our goal at Juice is to streamline the data product launch process so you can launch innovative data products in weeks, not months. Want to know more? Give Juicebox a try.

5 Rules for Successful Success Metrics

Here’s an analytics truism: everyone wants a dashboard (a.k.a. key performance indicators (a.k.a KPIs), success metrics, scorecards). Managers want a barometer of performance, a hammer to use on their subordinates, and a straightforward quantification of their business. Below are a few of the guidelines we use when we take on this task:

1. Actionable metrics

Ask yourself: what would I do if the metric is out of line? Do I have the levers that can impact it? Measures that track final outcomes like revenue or total customers don’t give you much time to react or guidance about what to do next.

2. Less than five.

When I first started at AOL, a friend of mine pointed to the dozens of reports flying around the organization and remarked (I paraphrase): "This many ’important’ metrics just indicates that nobody really understands this business." If you struggle to boil down, you should spend more time defining success and understanding the factors that drive performance.

Sprint Advertising Campaign

3. Simplicity over comprehensiveness

We don’t agree with Thomas Davenport’s call for more proprietary metrics:

You know you compete on analytics when...You not only are expert at number crunching but also invent proprietary metrics for use in key business processes.

In our experience, you’re better off if you choose metrics that can be understood outside your corner of the world. One common trap we’ve seen is a desire to create a single comprehensive metric; this metric is often an index that combines a number of factors into an overall measure of performance. The result: numbers that are meaningless without a lot of context and difficulty in interpreting deltas.

NFL Passer Rating Formula

4. Presentation matters

Your dashboard should be easy to understand and provide enough data to give your audience context. I’ve seen many dashboards that stubbornly show only the current state of a metric and the change from the previous week. Why so stingy with historical data? At Juice, we always show trending and try to give users a means to "cut" the data - by business line, customer type, month, etc. 

Juicebox dashboard

5. Evolve to goals.

Metrics without goals can be a waste. Unfortunately, getting people to agree to specific targets can be painful. After all, goals start us down a slippery slope toward clear accountability. Here’s what I’ve found works: start by focusing your energy on getting people to buy-in to the success metrics. Get clarity on definitions, show trending, and incorporate them into the organization’s vernacular. Be patient: one day someone will raise their hand in a meeting and ask if there are targets for the metrics. Pretend to act surprised by the cleverness of this suggestion.

SMART goal setting

6 Differences Between Data Exploration and Data Presentation

Let’s start by defining our terms:

• Data exploration means the deep-dive analysis of data in search of new insights.

• Data presentation means the delivery of data insights to an audience in a form that makes clear the implications.

Your toolbox for data exploration tools is flush with technology solutions such as Tableau, PowerBI, Looker, and Qlik. "Visual analytics" tools give analysts a super-powered version of Excel for dicing data to facilitate the search for valuable insights. Flexibility and breadth of features is critical; the user needs to handle lots of data sources and doesn’t know in which direction she will go with the analysis.

Data presentation is a different class of problem with distinct use cases, goals, and audience needs. Think about the incredible data stories delivered by the The Upshot, Fivethirtyeight, and Bloomberg. These data journalists often demonstrate data presentation at its finest, complete with guided storytelling, compelling visuals, and thoughtful text descriptions. When compared to these examples, it becomes obvious that the best efforts by a data exploration tool cannot deliver high-quality data presentation.

Data exploration tools generally try to cram all the information on a single page; data presentation needs better flow and explanation to tell the story properly.

You need a specialized solution if you really want to communicate data in ways that engage your audience. To understand the differences between data exploration and data presentation tools, let me offer six key ways that the activities are fundamentally different.

1. Audience — Who is the data for?

For data exploration, the primary audience is the data analyst herself. She is the person who is both manipulating the data and seeing the results. She needs to work with tight feedback cycles of defining hypotheses, analyzing data, and visualizing results.

For data presentation, the audience is a separate group of end-users, not the author of the analysis. These end-users are often non-analytical, they are on the front-lines of business decision-making, and may difficulty connecting the dots between an analysis and the implications for their job.

The needs and interests of a non-analytical manager will be wildly different from the analyst who speaks the language of data.

2. Message — What do you want to say?

Data exploration is about the journey to find a message in your data. The analyst is trying to put together the pieces of a puzzle.

Data presentation is about sharing the solved puzzle with people who can take action on the insights. Authors of data presentations need to guide an audience through the content with a purpose and point of view.

Data exploration is a journey to find truth; data presentation should guide your audience to focus on the most important data and insights.

3. Explanation — What does the data mean?

For the analysts using data exploration tools, the meaning of their analysis can be self-evident. A 1% jump in your conversion metric may represent a big change that changes your marketing tactics. The important challenge for the analysts is to answer why is this happening.

Data presentations carry a heavier burden in explaining the results of analysis. When the audience isn’t as familiar with the data, the data presentation author needs to start with more basic descriptions and context. How do we measure the conversion metric? Is a 1% change a big deal or not? What is the business impact of this change?

Fivethiryeight provides explanation surrounding their visualization to ensure readers understand what they are looking at.

4. Visualizations — How do I show the data?

The visualizations for data exploration need to be easy to create and may often show multiple dimensions to unearth complex patterns.

For data presentation, it is important that visualizations be simple and intuitive. The audience doesn’t have the patience to decipher the meaning of a chart. I used to love presenting data in treemaps but found that as a visualization it could seldom stand-alone without a two-minute tutorial to teach new users how to read the content.

My love for treemaps has been replaced by visualizations (like the leaderboard) that are more immediately intuitive to users.

5. Goal — What should I do about the insights?

The goal of data exploration is often to ask a better question. The process of finding better questions gets to new insights and a better understanding of how your business works.

Data presentations are about guiding decision-makers to make smarter choices. Much of the learning (through data exploration) should be done, leaving the equally difficult task of communicating the insights and the actions that should result.

In all these ways, data exploration and data presentation are different beasts. This is why we’ve chosen to focus on building the best possible data presentation tool, Juicebox.

6. Interactions — How are data insights created and shared?

Data exploration can be a lonely endeavor: Analysts work on their own to gather data, connect data across silos, and dig into the data to find insights. Data exploration is often a solitary activity that only connects with other people when insights are found and need to be shared. That is, when…

Data presentation is a collaborative, social activity. The value emerges when insights found in data are shared with people who understand the context of the business. The dialogue that emerges is the point, not a failure of the analysis.

Finding the Middle Ground: Data Storytelling

There is something between the extreme ends of data exploration and data presentation. We believe data storytelling lies in this intersection. Data stories aren’t entirely about “telling”, nor are they in the wilderness of “finding”. It is the opportunity to explain the data in a guided, narrative way where message meets exploration.

While there are tools for exploration (e.g. Tableau) and tools for presentation (e.g. PowerPoint), it is only recently that you’ve had the change to bring both together in one solution.

The Last Last-Mile of Analytics

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I was wrong about the “last mile of analytics.”

Over a decade ago, this was a term we started using to express the challenges of the analytics (then: business intelligence) world. We highlight how many organization struggled to bridge the gap between their data investments and the minds and actions of decision-makers:

This critical bridge between data warehouses and communication of insights to decision-makers is often weak or missing. Your investments and meticulous efforts to create a central infrastructure can become worthless without effective delivery to end-users. “But how about my reporting interface?” you wonder. That’s a creaky and narrow bridge to rely on for the last mile of business intelligence.

When we talked about “the last mile” we emphasized the need to better visualize data and communicate insights.

But I missed something. You can make a data product that is intuitive, friendly, simple, useful…but it still needs to be sold.

Sold?! It is an ugly word for many data people. But if want people to use your data, you need to change behaviors and assumptions. You need to convince your audience that it is worth their attention.

For example, we’ve been working with a global manufacturer committed to becoming more data fluent and data-driven across their worldwide operations. They have invested in data warehouse efforts and designed thoughtful new dashboards. Fortunately, our client realized that “built it and they will come” is a fantasy. Instead, we’ve helped them with a comprehensive plan to ensure their data has impact:

1. Train a cohort of evangelists in data storytelling to improve the quality of the data products;

2. Develop an internal communications campaign to go alongside their data product rollouts, explaining the value and purpose of each solution;

3. Create a support structure and tutorials to ensure that data product users fully understand each data product;

4. Gather feedback and update their data products.

More than anything, the data leadership team recognizes that technology and design are not the complete answer. They also need to change the culture and attitude of the organization.

This is the Last Last-Mile of Analytics, the selling and changing of minds to ensure your data gets used.

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Getting Data Product Requirements Right

Often customer data products or applications go awry because of poor requirements.  While customers can describe a billing workflow or a mobile app feature, explaining how data should be used is less clear. Merely documenting a wish list of reports, fields and filters is a recipe for low adoption and canceled subscriptions.

To ensure that data requirements are relevant and the solution is useful to customers (profitable too) consider the expression Walking a Mile in their Shoes.   The original expression, which has evolved and used in different forms is before you judge a man walk a mile in his shoes.  Collecting good requirements is less about a laundry list of charts and metrics, but an understanding of how information can transform the business from how it exists today.

In 2017 I had the opportunity to work on an insurance industry project for the first time.  The challenge was to deliver the industry’s first insurance agency analytics solution.  The product team showed us their competitor’s dashboards and suggested we replicate them. The support team demanded more ad-hoc reporting functionality on top of the Crystal Reports report writer.   Customers wanted an embedded BI tool to make themselves more data-driven. Needless to say all parties were miffed when we accommodated none of their requests.

What we did was contrary to what everyone expected.  We didn’t talk about the data (at least not in the beginning) or ask them their report wish list, but strived to understand the drivers of success and behavior within an insurance agency.  To walk in their shoes, we scheduled agency office visits, had discovery meetings with executives, observed workflow and documented data usage.  In the discovery meetings we asked questions related to the end user’s data experience, how and when information was being used and what decisions were made using data.

Here’s a sample of our questions.

Data Consumers (Users)

1. How well does the user understand the data?

2. How much expertise do they have in the industry?

3. What were some examples industry best practices?

4. Are customers looking for data or insights?

5. Does the end user trust the data?

Data Consumption

1. What are some examples of business processes being influenced by data insights?

2. What are the top 3 questions each audience wants to answer? 

3. When is data being used and how, e.g. daily, weekly, monthly, in account reviews etc.

4. How is information currently be displayed and disseminated?

Decision-Making

1. What are the current metrics that measure business success?

2. What are the key decisions made every day?

3. What are the decisions not made or delayed because of missing data?

4. What are the top data conversations had or that need to be improved?

5. What are the metrics that drive top line revenue?

6. What business processes will be impacted by this new information?

7. What are some example actions that might be taken as a result of insights? 

Data

1. What are the relevant time intervals that information will be updated, distributed and reviewed?

2. What are the most relevant time comparisons, prior week, prior month, prior year? 

3. Are these dashboard(s) meant to be exploratory or explanatory in nature?

4. What offers the most relevant context to the end user?

Getting users to adopt 20 new reports or a single new dashboard can be challenging when habits are already in places. Your best bet for successful data product adoption is to improve existing workflow and/or meetings using the newly uncovered insights.  In the case of the insurance project customers already had access to 300 reports before we created their new analytics solution. 

As it relates to the insurance project our first phase developed three new data applications.

1. Daily Cash Flow Application (printed) 

2. Weekly Sales Meeting Dashboard (TV Monitor) 

3. Monthly Carrier Marketing Meeting Presentation (2 Desktop Dashboards)

These solutions or apps solved specific problems and fit into their existing workflow.  In each case we developed a data application based on industry best practices.  

Just “knowing your audience” isn’t enough to get data requirements right.  Walking in their footsteps means understanding how their business works and how the right insights can impact it.  Some of the other benefits from this approach are:

• Quantifiable Returns - It was easier to talk about the benefits of a data product when tied to a process where time or effort saved can be measured.

• Increased Credibility - By taking the time to walk with customers we establish credibility.

• Improved Stickiness - Tying new applications to existing processes not only aided in adoption, but made them harder to turn off over time with increase usage.

Much of what was discussed above can be found in the Juice design principles, resource page or in Data Fluency; however the quickest way to find our more is to schedule a case study review.  Click the button below, send us a message and indicate your industry.  We’ll walk you through a successful case study in a relevant industry and answer your questions.

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Your Healthcare Analytics Solution: From Concept to Launch in 100 Days

Below is the video recording of a Juicebox healthcare analytics product webinar. The video is about 45 minutes and includes our tips for successful launches, a quick data product demo, and a Juicebox Q&A session at the end.

Our tips include which project tasks are best done slowly (e.g. getting your data ready) and which tasks need to move fast (e.g putting a working data product prototype in front of customers). Knowing what to do fast and what to do more slowly is a big part of the tips we share on the video.

We also touch on Juicebox strengths and alignment to support fast and slow tasks, such as:

🐇 Design development agility

🐢 Enterprise-grade app development

🐇 Understandable data stories

🐢 Capture your customer needs

Enjoy the webinar. After watching you can schedule a personal demo where we can go into Juicebox or case studies. https://www.juiceanalytics.com/get-started

New Years Resolutions to be a Better Data Product Manager

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

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

Empathy

the ability to understand and share the feelings of another.

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

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

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

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

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

Learn to flow

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

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

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

Patience

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

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

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

Growth mindset

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

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

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

Inclusive

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

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