Category Archives for "Data visualization"

Dashboard Design Case Study – Product Sales Dashboard

A case study of the key steps to designing a product sales dashboard with real-world challenges

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How to deal with strong opinions and inflexible thinking

The cast of characters

Who the dashboard was designed for

Dashboard Design Case Study – Business Performance Dashboard

Dashboard Design Case Study - Business Performance Dashboard

A case study of the key steps to designing a business performance dashboard from an international thought leader

Get your PDF of the entire case study. Just enter your email to receive yours.

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How to deal with strong opinions and inflexible thinking

The cast of characters

Who the dashboard was designed for

How to Get Data Visualization Buy-In From Your Boss

How to get data visualization buy-in and other practical tips from NickSight

Getting the budget needed to ramp up data visualization operations is a challenge I see many companies, even the most progressive and deep-pocketed, face. In this post, we will walk through how to apply a marketing approach to selling your boss on investing in data visualization by highlighting the function’s value proposition as it relates to the business.

Let’s start by sharing a story from my experience in the field as a data dashboard consultant.

Years ago, I was hired by a director of analytics to help steer him and his team in the right direction when it came to data dashboard best practices. It was made clear from the beginning that the company wasn’t entirely keen on moving to a “new a way of thinking/doing things.” The company was wildly successful and had been maintaining profitability for the last 10 years. The executive team was very risk-averse and had the mindset of “if it’s not broken, it doesn’t need fixing.”

During the workshop sessions, the analytics director, let’s call him Steve, came to me and wanted a few minutes of my time outside of our workshop.

Steve was brought onboard three months prior and was considered “progressive” by his company’s definition. He explained to me that he wanted to sell his boss on a more effective and efficient way the analytics team can take the workshop knowledge and implement it into a software program that was currently only licensed out to a few of the team members. Added licenses were a significant financial jump and required an account upgrade, but to meet expected demand and to scale their analytics business function, the entire analytics team needed to have their own software license.

During their last directors’ meeting, Steve had presented a detailed cost-benefit analysis of adding licenses and upgrading the current account for entire analytics team. He had also looked at the company’s leading competitors and analyzed their output which clearly showed the competition was beating them by both volume and quality. He ended the presentation with simple math executives like to hear: if you invest in X now, you will receive Y business impact, which will result in higher quality deals, customers who stick around longer, and better annual revenue numbers.

Yet, the other directors remained on the fence about the purchase decision, causing Steve’s boss to still be unsure about the investment.

Does this sound familiar?

In my experience, leadership buy-in for data visualization has two components:

  1. A clear and well-articulated value proposition; and,
  2. Education on what data visualization is and what it can do not only in the short-term but in the big picture strategy of the business’ growth trajectory.

To learn what a value proposition is, take the lead from your marketing department.

Without getting too granular with marketing terminology, a value proposition essentially is a summary of why someone should buy a product or service that is different from similar products or services offered by other companies. You can think of it as the hook that makes people choose to purchase from company A over company B.

In this use case, the value proposition of data visualization is entirely unique to your company and its main objectives. Perhaps for your business, investing in data visualization will:

  • Contribute to the company’s competitive advantage
  • Retire manual processes and create more efficient and effective ones bringing products to market faster and with more accuracy
  • Enable smarter and more precise decisions to be made
  • Offer deeper and more detailed insights into product development

Once you list out what the core differentiators of data visualization are at your company, summarize them into a few short succinct sentences to form a value proposition statement. This statement should explain the relevancy, quantified value, and unique differentiators of data visualization.

If you find yourself struggling deciding what makes a data visualization program different from other potential company investments competing for budget dollars, think of how marketing teams list out a product’s value propositions in preparation to market a product to consumers.

For example, say you’re getting ready to market a pair of running shoes. The competition is high for this product, so you must clearly differentiate it from other running shoes for consumers to see value in purchasing them from your company versus another. Your company’s running shoes are:

  • 5 x lighter than the leading competitor
  • Locally manufactured and handcrafted by retired athletes
  • Eco-friendly, but durable — lasts an entire running season without loss of tread or grip
  • When the soles do wear down, our company will give you half off your next pair
  • Free shipping and guaranteed delivery in less than 24 hours
  • 30-day trial period — you hate ‘em, return ‘em at no cost to you
  • Bluetooth enabled — connect your shoes with the fitness app of your choice for a more accurate tracking of your activity. This feature adds no weight to the shoe.

When you look at this list, make sure none of the differentiators are offered by your competitors. If they are, they are not considered a differentiator and should be removed from the list.

Start your pitch with clear benefits and the importance of data visualization to the business.

Every data dashboard workshop I run begins with a short set of slides that clearly lay out the benefits of why data visualization is important and how it translates data to business value.

It’s wise to keep these benefits simple and clear. Don’t over complicate and always have supporting evidence to your projections.

In short, make the investment’s high-level data visualization benefits crystal clear to your boss and in layman terms, such as pitching something like, “If we make our data look this way, our customers will not only understand it better, but they will begin to change their purchasing behavior, which increases our net margins by X% over X amount of time. To make our data appear in an actionable way, my team and I need these tools. Your investment of $X now will bring you $X in one year, $X in two years, and $X in three years.”

The aim is to make the value proposition so enticing that it would be illogical not to invest. Value statements can come in many forms:

  1. It will allow us to improve sales, margins, etc.
  2. Our competitors are doing it, we’ll get left behind. We lost 7% market share last year and we need to get ahead.
  3. It will reduce our costs by X% year over year.
  4. We will free up Y number of staff to do higher value, higher impact projects.
  5. We can reduce our licenses in legacy technology and replace with a lower cost dashboard solution.

Ideally, there will be examples you can point to in your company. If not, running a low-risk, low-cost pilot, can be used to demonstrate the value.

The exercise here is about negotiation and making your case — don’t back down, and always bring data and evidence to the table while being respectful.

Use terms and benefits that are applicable to your boss and what matters most to him/her.

When constructing your pitch, go through line-by-line of it and conduct a litmus test of sorts that challenge each statement against the following:

  • How can data visualization help my boss look good?
  • How can data visualization help my boss get a promotion?
  • What will make my boss look like an innovative leader in front of the other executives?

Positioning the investment to your boss’ benefit will ultimately benefit you the most. Often times, however, a boss only hears what will help them and make the business more money. Pitching your value statement using these parameters will go much further than saying you or the analytics team needs some tool the executive is likely never going to see or use.

Eliminate any uncertainty. When getting buy-in, always think two or three steps ahead so you will be prepared for any follow-up questions.

Go through the exercise of what you expect your boss to ask immediately following your pitch to them based on your experience with them in the past. What do they usually hone in on? Have these potential objections solve for and in your back pocket.

Not only will you be more prepared, but your boss will see you’re looking at the big picture and not just your own needs.

Why Are My Data Visualizations Ineffective?

Ineffective data visualizations are a top frustration felt by many data analysts and their teams.

Before we overview a handful of data visualization techniques that you can begin to leverage when building effective dashboards, it’s important to take a step back and ask yourself who you want the dashboard to be effective for? In this post, we will provide insights in how to solve for this common frustration and provide tips to start using right away.

Before you begin building a data visual, get to know your audience first to increase effectiveness

Often times, data visualizations are ineffective because they are built for the wrong audience in mind. The perceived value of dashboards, too, gets lost due to poor communication with the end users.

Find out all you can about the audience you will be presenting the data visualization to. Meet with as many members of the audience as you can to learn their biggest pain points, their work goals, and ultimately, how the data visualization will help them make better decisions.

Don’t be afraid to learn about your audience, even if it’s just a few members of it, from a personal level. Consider setting up a lunch or a coffee meeting to just get to know each other. This will start to establish a trust between you and them and reveal helpful anecdotes that can be lost in the formality of the day-to-day. If you’re short on time or your audience is not on site, try as best you can by email or phone.

The more of a connection you can find with your audience, the better build you will have because you will know a bit more about how they tick and what they’re looking for from your visualization.

Common reasons why data visualizations are not effective

  1. Failure to understand what the business value could and should be for the available data;
  2. Not engaging dashboard users early in the process to learn what they want from the available data;
  3. Insufficient or ineffective communication between stakeholders, users, and the data architects;
  4. Poorly designed dashboards and charts without a path to behavior change


Without properly planning ahead of a build with these common adoption barriers, at least at some level, it will be hard for your visualizations to have long-term success.

While most dashboard architects do their best to consider these adoption barriers in their planning processes, it’s not uncommon to become sidetracked mid-process with other tasks and in-the-moment priorities that take the focus away from the prep work.

After all, as critical as this industry has become for businesses, the demand for trained professionals is much greater than the supply. Anyone working with data is stretched thin to do “all things data-related” even if it steers clear in the other direction of their core responsibilities.

All of the above points require interaction with people, and in an age where data is king, trying to convince people to engage in human stuff is a bit of a sales pitch that many folks either don’t want to do or, perhaps, know how to do it at all.

Let’s look at each one in a bit more detail.

Failure to understand what the business value could and should be for the available data

Any successful endeavor needs a clear and well-articulated goal — an objective to strive for. Don’t bother even starting to create visualizations without knowing what the desired outcome is. Again, it sounds like common sense but I’ve seen it far too many times where people dive right into the data without a concise vision.

Don’t fall into this trap, the work up front is worth the payoff. Building a dashboard against a proven failed strategy is like running in place — you put in all the energy and resources and get nowhere.

Not engaging dashboard users early in the process to learn what they want from the available data

Bridging business goals, data and users together is damn hard even when done by a pro. If we are missing the user element then we have data, we have a vision but we don’t have a means to actualize it effectively. Sure, you might get lucky from time to time by not engaging users, especially if it is a small user base, but not doing so is an open invitation to add risk and poor to no user adoption.

A user has needs and it’s part of your responsibility to know what they are and build a dashboard to meet those needs.

Insufficient or ineffective communication between stakeholders, users, and the data architects

Data quality is a behemoth breathing down the neck of the analyst. It is often a burden that is unshared and uncommunicated responsibility until it is forced into the light through user interaction. Time spent as an analyst is finding and formatting the data to analyze, limiting their bandwidth to perform a thorough analysis.

While not the only reason, getting an open channel of communication between all interested parties is a mechanism to address data quality issues early and devise strategies to mitigate or improve. There’s no logical reason to not communicate.

Poorly designed dashboards and charts without a route to behavior change

Solving the three previous factors will not be an easy feat if not successfully manifested into a user interface.

Business goals, data, and user needs are all brought together in the user interface, typically resulting in a complete dashboard.

When these three variables are not accounted for it’s due to:

  • The dashboard having no connection with what the business is seeking to solve
  • The dashboard has little or no intuitive actionable outcomes for the audience to walk away with
  • The dashboard was not built with data visualization best practices in mind

The value of data visualization is rooted in early and often communication

At the end of the day, improving the effectiveness of a data visualization has nothing to do with the skills needed to build the product. Rather, it’s a balance of good communication practices that are fine-tuned over time. And because communication is not necessarily a top priority for those in the data industry, it’s a shared frustration.

And while not exactly earth-shattering or breaking news, good communication skills for many people are a learned and practiced behavior through the application of the process. Simply put, it takes time and patience. Yet, the better you get at the human-side of this process, the better and more effective your visualizations will be.

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The Insight Burger – A Primer on Behavioural Change with Data

A burger sits before you, and a decision. To eat or not to eat. Would access to data cause a behavioural change?
What if you could see the impact of eating the burger before you ate it. Your risk of heart attack goes up .3%. Your weight will go up .1lbs by next Tuesday. And your LDL cholesterol will enter a warning range.
Why might that data make you think twice. If it was personal to you and not some average of a bunch of people. The impact is compounded as you ate a pizza yesterday and a carbonara the day before. You receive the information in a timely manner, at the point of eating the burger, not 3 weeks afterwards. The information is relevant to what you are doing, i.e. it is health and diet information. And, finally, it is actionable. Will I eat it, yes or no. The information allows for a change in behaviour.
Maybe you will look at burgers differently now, but hopefully you will see how data can help you act differently and, hence induce a behavioural change.


In the next article we will explore how this becomes manifest in an enterprise-grade dashboard and why a dilligent design process leads to increased profits.

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