In this blog, the first of Luxoft’s new series on data monetization and innovation, we dive into the problem of “where to begin” with data visualization. Without an effective strategy, your business will struggle to derive value from your data.
The three components of effective data visualization
Let’s start with understanding the 3 fundamental puzzle pieces of data visualization so we can expand and unpack new insight.
Data Science: The technology-driven “back end” of a big data solution. Data scientists essentially build the “pipeline” that carries the raw data from the source to the screen.
Business Analytics: The statistical, insights-driven side of the data. Business analysts analyze, consume, and act on the implications of the data they see.
User Experience (UX): The user-focused research and design of the end product. UX practitioners consider and design around the practical situations that the user will encounter along the way.
Without the right user experience, you can easily miss key business insights hiding in your data
User experience might seem like the odd one out in that trio, considering that data science and business analytics seem to be at the core of how we think about data. If you haven’t considered that your data has an audience of users before, perhaps you should. We encounter our fair share of clients who come to us with data and a request to visualize it for them without elaborating further. In one case, I asked one of our clients, a leader in aviation, what data was most useful to them and they replied, “We don’t know. It’s never been done before, so let’s just see what any data looks like.” It’s understandable and common to not have a ready answer for this question. That is why it is so important to consider the use case of your data.
Getting the UX right means asking the right questions
When understanding how to visualize your data, our team always starts by asking these questions:
- Who will be viewing this data?
- What questions are you trying to answer?
- How is this data currently presented, if at all?
- What is your end goal with this new visualization?
- Will they be viewing these graphs on a monitor, laptop, or mobile device?
- Will they be shared over email or within a browser?
- Are you presenting this information to analysts who need granular details or to executives who want a cursory glance?
Ideally, the more details the better we can serve them—but even a few answers help.
Just like a single data point without context lacks meaning, building a graph without understanding its usage context is shortsighted. It’s important to know how your data will be consumed by your audience. It sounds simple, but in most of the situations where a company has data and doesn’t know what to do with it, they probably have not stopped to ask these questions.
Anyone can drop a table in Excel and spit out a pretty graph, but you won’t achieve those desired insights without a dialogue about who your audience is and your business goals. Data visualization goes beyond cranking out graphs in Excel, or even specialty tools like Power BI, Tableau, and raw code. Data visualization engineers maintain a toolkit of graph types, software proficiency, quantitative analytics, and UX skills.
A well-trained data visualization practitioner knows how to ask the appropriate design questions upfront and how to adapt data to the right audience. For instance:
- What types of data are being visualized (time-series, parts of a whole, categorical vs. continuous, etc.)?
- What branding considerations need to be made? This can influence color palettes, font choice, and spacing.
- What questions is the data answering? Is there anything non-obvious that needs to be mentioned in an annotation?
- What terminology is appropriate for the audience viewing this? Are they experts in the subject matter or is this meant for public consumption?
Luxoft is here to help you craft effective data visualizations – fast!
The key elements of user research and design involve stepping back to understand the experience of your end-users. This isn’t just a suggestion, but an inevitable necessity. The amount of data in the world
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