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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:
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:
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:
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.
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:
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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:
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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