15 Smart Playbooks to Improve Data Adoption

If you want to improve data adoption, you’ve come to the right place. Our team was lucky enough to interview analytics leaders that have tackled this problem.

In this book, you’ll learn actionable strategies to increase data adoption and prove the ROI of your investment.

Download your digital copy to get 15 real-world lessons from data & analytics leaders at companies like Rockwell Automation, Cisco, Apple  & more.

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Introduction: So, you want to improve data adoption?

Don’t worry, you’ve come to the right place. 

 

Our team was lucky enough to interview analytics leaders with experience facing this problem head-on. 

 

You’ll learn the “playbooks” they use to encourage and enable their business to actually make data-driven decisions.

 

No fluff. Just tactics you can take and implement in each chapter.

Playbook 1: Start Simple, Start Small, and then Formalize Your Process | Donald Farmer, Principal at TreeHive Strategy

The best way to create a data driven culture is to just start having data conversations all across the business.

 

Simply start to think about data and work your way toward becoming data-driven. There is no reason to wait.

 

You don’t need advanced reporting or an analyst team to simply get started.

 

Instead, the next time your team is focused on answering a business question, think about what data you can incorporate to the answer.

 

Remember to think about data on an organizational level rather than an individual level. 

 

Analytics is not just a personal skill, it’s an organizational skill.

 

Having one great analyst may prove merely incidental if the rest of the organization has no way to use their output.

 

Instead of the individual analyst, build a community of practice, where different aspects of our skills and commitment come together.

 

Then formalize a data process by setting a company-wide expectation as part of your business strategy that you will use data to make business decisions.

 

Make it known that all visions for the business need to be supported by data.

 

About the Data Leader:

 

Donald Farmer is an internationally respected speaker and writer with over 30 years of experience in data management and analytics. He advises investors, software vendors, and enterprises on data and analytics strategy. In addition to working with startups, Donald led teams at Microsoft and Qlik, building some of the most important and innovative analytics platforms on the market.

 

His consultancy, TreeHive Strategy, works globally with teams from the newest startups to some of the world’s largest businesses. In addition to data and analytics strategy, Donald frequently consults on governance and compliance issues, particularly for companies implementing self-service architectures. His workshops on innovation strategy have helped to drive significant changes in numerous businesses in fields as diverse as fashion design and high-tech manufacturing.

Playbook 2: Grow Your Community of Data Practitioners Beyond Your Technical Employees | Dr. Christian Vogt, Chief Innovation Officer of Data & Analytics at Cisco

Investing in data literacy will pay off handsomely down the line.

 

Why?  The ability to draw insights from the right data helps employees make better decisions, faster.

 

But data literacy is not just about providing employees with tools.

 

Rather, it is about the entire process from asking the right questions, to working with high-quality data, to presenting the results in a way that resonates.

 

These skills are not only required of technical employees, such as data scientists, data engineers, or data analysts.  They are competencies that everyone should have.

 

A data-literate person asks the following questions and knows how to get to an answer:  

 

  • What data do I ideally need? 
  • If the data is hard to get, what is the closest proxy?
  • Is this data accurate?
  • Is the data biased, i.e., are the circumstances under which the data was produced different from those I want to apply it to?  Note that bias goes beyond gender, ethnicity, and age.
  • Am I interpreting the data correctly?  Will others interpret it the same way?  Is there a risk that it is misunderstood?
  • What are the tools I plan to use?
  • How should I visualize the results in an intuitive manner?  
  • How do I tell a compelling story?

 

At Cisco, our goal is to enable and continuously grow our community of data practitioners.  We want everyone to be data-literate, including technical employees, non-technical employees, and executive leadership.  We accomplish this through a comprehensive offering of educational programs, various annual events, and a collection of tools that we provide the community.  

 

About the Data Leader:

 

Dr. Christian Vogt is the Chief Innovation Officer of Data & Analytics at Cisco in Silicon Valley.  He is responsible for driving the adoption of digital, analytics, and artificial intelligence at Cisco, for accelerating the company’s business model transformation towards subscription software and network-as-a-service, and for incubating and scaling the capabilities needed to accomplish this, both inside his organization and across the company.

 

Prior to Cisco, Christian was Chief Digital Officer at STIHL, where he oversaw the digital transformation of the $4.3-billion power tool maker, reporting to the Chief Executive Officer.  He also was Managing Director of STIHL Digital, STIHL’s newly established venture arm.  Before STIHL, Christian was a management consultant at McKinsey & Company in Silicon Valley, where he served a variety of clients from Fortune-50 corporations to growth-stage startups.

 

Christian has a Ph.D. in engineering from Karlsruhe Institute of Technology, one of Germany’s designated elite universities, and an MBA from the Wharton School of Business.  He has published numerous books, articles, academic papers, and patents.  He has lived and worked in Silicon Valley, Los Angeles, Finland, and Germany.

Playbook 3: Start with the Question and Design for Action | Kimberly Herrington, Data Journalist and Creator of Buffalo Business Intelligence

Even if you have the most accurate report possible – if your audience doesn’t know what it means –  then it isn’t actionable.

 

One small but very practical tip that I recommend to improve adoption: Name your report after the question it solves.

 

This makes the goal abundantly clear to your audience that this is what you’re solving for.

 

When your stakeholder revisits the report, after a long day of other responsibilities, this naming structure makes it easy for them to reacquaint themselves with what they’re looking for. 

 

Before you build anything, ask them about the actions they might take from your report. 

 

The best type of data story is simple. They know what the goal of it is, they can see if it’s good or bad, and then they know what action to take because of that.

 

About the Data Leader:

 

Kim is a data storyteller and community builder dedicated to the transformational promise of data literacy.

 

Kim Herrington is a Data Journalist and Creator of the buffalo Business Intelligence group. She is responsible for growing data visualization user adoption, increasing communications, and increasing organizational data literacy. She does this by working with cross-functional teams from Analytics, IT, Marketing, Business, and other corporate servicing functions. 

 

While her career path has maintained a focus on healthcare since 2004, it has paralleled a range of topics from Administration, Digital Marketing, Customer Service, and Event Planning. She has extensive experience in applying creative problem solving methods for process and performance improvements in analytic and healthcare operations. This unique range of experience combined with her communications expertise has made her an effective analytics translator and culture change champion. Kimberly holds a Masters degree in Healthcare Administration from D’Youville College and a Bachelors in Biology from SUNY Oswego. Kim is married and he has one daughter.

Playbook 4: Create a Consistent Experience for Your Stakeholders | Brent Silberman, Director of Revenue Operations Manager at Mud City

Consistency in design alleviates the stress of change. 

 

Because data is constantly changing and the story of the data is constantly changing, your presentation should stay consistent. 

 

For example, consistency in both color and layout makes absorbing the insights in the dashboard simpler.

 

Always stick to a theme. Make sure people – across all your work – know exactly where to go to drill down further or find “what’s most important”. 

 

Avoid playing around with a very large color palette or having multiple varieties of charts. This can draw attention away from the actual analytics and insights. You should choose substance over style.

 

About the Data Leader:

 

Brent Silberman is the Director of Revenue Operations Manager at Mud City also known as “the data poet”. He has experience in both GTM reporting, revenue operations, and business intelligence with a Master of Science degree in Information Management & Data Science from Syracuse University.

Playbook 5: Watch Someone Use Your Report & Explain It to Others | Amy Jarrow, Senior Business Intelligence Analyst at Insight Global

As “data people”, we see data and problems from our own lens. 

 

We can be so ingrained with the data that we end up presenting it in a data forward way that may not work for our stakeholders.

 

So, how do we change that? 

 

Two things I like to do: 

 

  1. Watch someone use a report that I built. You’ll learn their intuitive click-path and where they are going to get the insights. It’ll help you answer questions like: 
  • Are they reaching a point where they wish they had more data? 
  • Should there be a drill down feature that allows them to go into more depth in understanding what makes up that number?

Sit in on a meeting and see how they present the data. You’ll learn more about the inputs that go into their decisions and how they intend to use your report. This also helps you iterate by basing the dashboard on what was seen in action, not just heard during requirements gathering.

 

About the Data Leader:

 

Amy Jarrow is an experienced data analyst specializing in Microsoft Power BI. She has a passion for making sense of data to provide business insights for better decision making. 

Playbook 6: Advocate for Your Work with Individuals First | Eric Weber, GM of Experimentation at Yelp

All data projects require buy-in from a larger group, especially getting others to integrate data into their business process.

 

For any project you’re working on, if you walk into a meeting and say, “This is the truth, this is what we should be doing.” You probably won’t get your desired outcome.

 

Why? People tend to think in groups, which makes it very difficult to actually change anything. 

 

But if you’ve already sold your work to people individually, when you go to that group setting it becomes a totally different conversation. 

 

By creating buy-in with individuals first, you’re more likely to see success when you go to the entire group.

 

I recommend having one-to-one conversations ahead of time,  so you position your work with ease, explain why it’s important, and convey the message in a way that it resonates without “group think” influencing the outcome.

 

About the Data Leader:

 

Eric Weber is currently the GM of Experimentation and a data science leader at Yelp. He’s worked in leadership and individual contributor roles at Yelp, LinkedIn, and CoreLogic after an academic career as an assistant professor. He loves working with data, educating others about data’s value, and helping people excel in technical roles. Eric has a Ph.D. and Masters in Mathematics from ASU, a Masters in Business Analytics from the University of Minnesota, and he is currently completing the Executive MBA at the University of Chicago-Booth. 

Playbook 7: Don’t Underestimate the Power of Building Relationships | Matt Lee, Senior Analyst at Apple

There’s only so much you can do to push for data adoption.

 

The most successful analysts realize that people matter in data, more than just the tools.

 

Why? Because data can be difficult for people to understand and if trusted individuals are involved early-on, adoption rates can soar. 

 

When you take the time to establish a relationship with someone one-on-one, believe it or not, they will become more interested in the insights you provide and subsequently, their data adoption will increase.

 

This work must happen before you need their buy-in.

 

Take the time to understand who they are, their role and goals, as well as the history of their work. In return, they’ll learn more about who you are and how your insights can help them based on your track record.

 

This turns them into a data champion who can influence others in your favor, which is invaluable.

 

About the Data Leader: 

 

Matt Lee is currently a Senior Analyst at Apple providing actionable insights through relationships + data viz. He is passionate about data, visualization, and storytelling.

Playbook 8: Use Your Customer Lens. Find the Subtext Behind What Your Customer Asks For | Charisma Wickham, Lead Business Performance Analyst at Alliant Energy

When you focus from your perspective and not your customer’s perspective, more than likely you’ll get your report wrong.  Use the lens of the customer to anticipate what they need and how they will use the information.

 

To create something that actually serves your customer, you have to take the flavor of what they requested and dig deeper. As their data expert, it’s your job to find the subtext in their initial request because they don’t always know exactly what they’re asking for.

 

To do this, I recommend always asking your customer these two questions: 

 

  1. What action are you going to take from this? It’s important to have your customer tell you what they plan to do with this report rather than just infer it. By knowing the actual action they will take, you get more insight into what they really want and not just what you think they want.
  2. What else would be helpful to know that would make this information useful? This question pertains to the actual metrics you plan to share. By asking this, you uncover additional pieces to the puzzle that they need in their report because without them it wouldn’t make sense. For example, a sales leader looking at new pipeline each month will want to know how many opportunities were created and who they are, but they might also ask for things like average deal size and total revenue generated.

 

By asking these two questions you will create an end result that your stakeholders actually use.

 

About the Data Leader:

 

Charisma Wickman is a self-described spreadsheet ninja and data geek with a knack for data mining and visualization who also happens to be a Business Analyst. Charisma credits her passion for change management, early adoption of technology, 23 years of experience in the utility industry and her ability to tell stories with data as the keys to success as an analytics leader.

Playbook 9: Become an Expert in the Business Before You Share The Data l Bruno Reis Enterprise BI Manager at Howmet Aerospace

Determining value is the name of the game for many functions within a business, but this is especially true for those in analytics. 

 

Creating connections first, with the different stakeholders of the business, is a very important part of the role of an analyst.

 

It is easy to forget that you’re part of a bigger ecosystem and you need to understand how the other areas work to be successful. 

 

When analysts are asked to make reports and they don’t determine the needs or values of the people behind them, they’ll create something but end up with low adoption.

 

We must understand what other functions do and what’s important to them. Be visible and create a relationship with them. 

 

This will inherently improve the reports you create, the logic behind other’s decisions, and ultimately lead to data-driven decisions at your organization.

 

About the Data Leader:

 

Bruno Reis is a data professional and has an undying thirst for knowledge. He has a track record in delivering creative and innovative data solutions that add value to the organization.

 

For 15 years, he has worked internationally in a multitude of environments, filling diverse roles within the organizations he has worked with. This has given him an expanded view of the organization’s structures and the social dynamics within.

Play 10: Optimize for Mobile First and Simplicity | Charlie Dunn, Project Storyteller, DPR Construction

Don’t fall into the trap of creating a massive, complex BI empire. 

 

Both executives and your field team don’t have time for a Power BI dashboard that’s got 30 options to click and drill through. 

 

Instead, they want a simple story.

 

So, how can you solve for simplicity? Make everything mobile-first. This forces us to have the discipline to not put too much out there.

 

Just like people constantly checking their phones to monitor social media or the status of their airline miles, you want your team to have an addiction toward monitoring their reports. 

 

They should be thinking…

 

“Hey, I want to look at this” 

 

“I wonder how we’re doing?”

 

And they should be able to get to that answer without having to ask someone else. 

 

Before you create anything, ask yourself: 

 

  • Who is consuming this? 
  • What is important for them to see to take the right action to do something meaningful?

 

This allows you to focus on creating something simple and straight to the point. 

 

Because at the end of the day, data does not drive project success; people do. It’s our job to ensure the decisions they make are driven by data and not the seat of their pants.

 

About the Data Leader:

 

Charlie Dunn tells a good story. From cash rich development on the Las Vegas strip, to oil fueled expansion in Edmonton, Alberta, to energy plant upgrades throughout the Southeast, he thrives in the challenging delivery gap between “as is” conditions and the “to be” designed state. 

 

His career highlights include the on-site project management of over 2.5 million square feet of new distribution space across North America with zero recordable injuries, and WANO (World Association of Nuclear Operators) recognition for the use of 4D technology in project delivery. He knows that projects succeed because of people—what they believe, strategy—for creativity and innovation, and tools—to deliver value and drive out waste. 

 

Charlie joined DPR Construction in 2016 where he continues to learn about the power of autonomy and the passion to build great things.

Playbook 11: Tell a Compelling Data Story | Zack Mazzoncini

Knowing how to tell a data story is critical to ensure people will take action on your findings. 

 

Why a story? Our brains are wired to be drawn in and connect to stories.

 

However, there are a couple of challenges when it comes to creating these stories:

 

  • How do you know what is most important?
  • How do you design the right stories?
  • How do you deliver the insights?

 

I recommend these 3 steps to transform confusing data into stories that create clarity and inspire action.

 

The Blueprint: Find the right data stories

 

Ask yourself the following questions before even beginning the story. What questions will be answered by my story? What actions will be taken as a result of my story? What results are expected by the reader?

 

The Canvas: Design the right data stories

 

First, you need a purposeful layout to your story. You should follow the intuitive path and ask yourself where do the eyes lead naturally. Then there should be intuitive visuals. Aim to create quick cognitive insights with your visuals. Utilize color to draw attention to what is important. Never underestimate the power of concise and clear text to propel a story, this is pivotal to getting the insights across. Optimize for the stories for sharing. Do they need it on mobile? Do they want to email it?

 

The Story: Deliver the insights 

 

The first 30 seconds of your story matter the most. Studies show we gain or lose the audience in the first 30 seconds, you must do something to grab their attention! Once you have their attention, you have to be able to keep it. Bring emotion and passion, show the group why you are interested in this data story, your passion will spread into the room. Finally, practice! Presenting and delivering compelling data stories is hard, practice will alleviate those nerves and result in a better delivery.

 

Following this three-step process will aid anyone in becoming a better data storyteller.

 

About the Data Leader:

 

Zack Mazzoncini is the Founder of Data Story Academy and a Co-Founder of Seattle-based data and analytics firm Decisive Data. Over the years Zack has helped hundreds of organizations and individuals develop data-driven cultures centered around data storytelling. Zack graduated from the University of Washington with a degree in communications, rhetoric, and public speaking. He is considered one of the most entertaining and informative speakers in the analytics industry. His audiences are inspired to find the most important stories in their data and lean into their creativity.

Play 12: Become a Subject Matter Expert in the Data | Zach Mullins, IAM Role Security Analyst at Highmark Health

When someone comes to you and says “Hey, I need this report”, you need to ask follow-up questions that get to the problem they are solving that created their need.

 

I’ve found that by having one quick phone call to ask more questions can help you understand what they’re talking about and their level of need. But in order to ultimately create the data story they’re looking for, you also need to become a subject matter expert when it comes to the data. 

 

For example, let’s say you have a request from a city planning team. They’re asking, “How many people pass through this intersection every day?”

 

When you start to familiarize yourself with the data you need to answer this question, you might find out that there isn’t any data. 

 

It’s up to you to understand what data you need to create… 

 

  • Maybe you need to have cameras set up. 
  • Maybe you count the number of cars by their license plate.
  • Maybe you need to gather each car’s speed. 
  • By doing this you move from analyst to architect, and become the owner of gathering the right data points that make it happen for your customer.

 

About the Data Leader:

 

Zach is a graduate of the Pennsylvania State University working in the Greater Pittsburgh Area. He is currently the IAM Role Security Analyst at Highmark Health in downtown Pittsburgh.

Playbook 13: Model Your Data Story After the Avengers | Chris Wagner, Microsoft MVP and Analytics Architect at Rockwell Automation

When The Avengers movie came out, it raised 8 billion in the box office. 

 

Why was it so successful? The answer is the story behind the films.  

 

Believe it or not, the same storytelling concepts are valid for data stories. 

 

As we want to approach our data like the creators of the Marvel movies approached their characters. We want to have clearly defined characters, character arcs, and time to develop.

 

Clear characters with flaws

 

We have to gradually and clearly define our data, including metrics, targets, and general terminology. Additionally, we need to make sure our audience is familiar with the most important things for them.

 

This means we cannot introduce all of our data at once. 

 

We should start with a simple overview and then build to the complexity needed to answer the questions.

 

We also need to be honest when it comes to the characters in our data stories — articulate the strengths, and more importantly, the weaknesses of our data. 

 

It does no one any good to hide the faults in the data so be upfront about the limitations of the data. 

 

The questions the data can’t answer are just as important as the questions it can answer.

 

Character arcs

 

Every character in an Avengers movie has a distinct character arc. This covers where they come from, who they are, and where they will go after the movie. The same goes for our data.

 

Phase 1: Leading Indicators

 

In this step, we have to determine what led up to both you, the analyst, asking for this specific data, and what happened in the data leading up to the particular numbers you pulled. This requires high familiarity with your metrics and an intense understanding of how they all work together. 

 

Phase 2: Transformation

 

Here our story becomes more complicated. The more we tell our story, the more complex and refined our data will become. We don’t focus on just one singular metric or KPI in this phase. We expand on our knowledge of the numbers and metrics and build out the story around our metrics. This is where the details of the story are discovered and pulled together.

 

Phase 3: Anticipation of trailing indicators 

 

In phase three, we determine what our data will do after this exact moment in time. Data will always change and progress. This is where we must predict what comes next in our story. Additionally, you must determine what the result or outcome of your work will be. You found the story in the data, now what? Where does it go from here? 

 

Time to develop

 

When new initiatives or projects begin, it is essential to monitor the data over time.

 

See how it changes, reacts, and adapts to changes in your business and in the world. No character has their full story told in one day, neither does your data. 

 

Every analyst knows it is essential to look at your data over time. Now the task is to articulate this to others and get them to have a watchful eye on the story’s progression over time. 

 

Don’t forget about the sequel of your data story.

 

About the Data Leader:

 

Chris has been working in the Data and Analytics space for nearly 20 years. Chris has dedicated his professional career to making data and information accessible to the masses. A significant component in making data available is continually learning new things and teaching others from these experiences. To help people keep up with this ever-changing landscape, Chris frequently posts on LinkedIn and to this blog. He is also the Founder of Kratos BI designed to help people become Data Gods.

Playbook 14: Remember That Both Visuals and Narrative are Important | Brent Dykes, Author of Effective Data Storytelling

Many analysts feel logic and reason should be sufficient to influence a decision and convince someone of an insight’s merits. However, in practice, emotion often plays a larger role in decision making than many people realize. Even if you’ve visualized your insight effectively, you still need to connect with the emotional side of your audience’s brains. Narrative offers you a tried-and-true way of bridging logic with emotion. Narrative provides structure, meaning, and focus to your data communications. Unfortunately, the narrative aspects are often overlooked or underutilized in today’s world of advanced dashboards and visualizations. And yet, there is so much more to storytelling than just the visual component. When you combine narrative with visuals in an effective manner, you ultimately make your insights more engaging, memorable, and persuasive. If your goal is to drive action with your insights, you need both visuals and narrative to be successful.

 

 

About the Data Leader:

 

Brent Dykes is the author of Effective Data Storytelling: How to Drive Change with Data, Narrative, and Visuals. Brent has more than 15 years of enterprise analytics experience at Omniture, Adobe, and Domo. His passion for data strategy and data storytelling comes from consulting with many industry leaders including Nike, Microsoft, Sony, and Comcast. He is a regular Forbes contributor and has written more than 35 articles on different data-related topics. In 2016, Brent received the Most Influential Industry Contributor Award from the Digital Analytics Association (DAA). He is a popular speaker at conferences such as Strata, Web Summit, Shop.org, Adtech, Pubcon, RISE, Crunch, and Adobe Summit. Brent holds an MBA from Brigham Young University and a BBA in marketing from Simon Fraser University.

Playbook 15: Build a Team of Data Champions | Tommy Puglia, Microsoft MVP

Data adoption is critical for any organization. That is not news at this point.

 

Without effective data governance, your organization can lose trust in the data and therefore stop using it.

 

Create a governance team that creates the policies around governing data. They should implement and enforce these procedures once agreed upon.

 

In addition to your governance team, I would also recommend building a team of data champions. This is a cross-functional team of people who see the value in data-informed action and advocate for the trust and usage of data.

 

They can advocate for data initiatives and instill confidence on their own teams resulting in data trust across the organization. The higher up the org chart you can create these data champions, the better.

 

About the Data Leader:

 

Tommy Puglia is passionate about Power BI & how Data Analysis can impact decisions. Using Power BI since it was known as “Power BI Designer”, he is a Microsoft MVP and consumes whatever he can to constantly learn and apply BI for Marketing & Sales. He is also a leader of the Chicago Power BI User Group. He is also MCSA certified in BI Reporting by Microsoft.