A picture is worth a thousand words. This adage is common innumerous languages today, but it can be traced to Norway and Henrik Ibsen, the playwright, whose original quote was "A thousand words leave not the same deep impression as does a single deed." Over the last hundred years, it has evolved to the form many of us are familiar with. And today, labs everywhere are discovering how true this statement is for their lab data analytics.

When you can take a pile of scientific data and convey the importance of that data in a way that’s beautiful and easily understood, you have added value to your data. But visualizing your data in this way isn’t necessarily as easy as dumping the data into a software solution and retrieving pretty pictures.

Thoughtful structure for your data analytics is needed before you can derive meaning from them. To do that, you’ll need an understanding of what data is used and stored in which parts of your organization.

You may also need to understand what other groups’ needs are to develop a holistic organization-wide view of data management and visualization. An organization’s lab informatics expert is uniquely positioned to champion the development of a comprehensive data structure and analytics program (of which data visualization is an integral part). The work of the lab touches many parts of an organization’s data, making labs a logical starting point for organization-wide data and analytics work.

Data Visualization Begins With FAIR Data

You can’t make visualizations with data you don’t know you have. Your disparate laboratory systems and instruments keep track of their own data, but how robust are the connections among these systems? Understanding what data is available, and where it is stored in your laboratory, is the first step to organizing that data for analysis and visualization. Structuring data to make the best use of it was the idea behind the 2016 FAIR Data principles.

In most cases, your data structure should begin with the metadata, which ideally is in a form that is as standardized as possible. Standardized metadata is a pillar of a sound data governance model, and the basis for ensuring your data complies with the FAIR Data principles, which are intended to optimize your data for reuse. FAIR data are:

• Findable
• Accessible
• Interoperable, and
• Reusable

Completely reorganizing your data from the metadata up may sound like an inconvenient and painful process, but it will be worth it to have well-structured data and a robust data governance policy. Data that is structured can be easily retrieved and repurposed to produce insights. Undertaking this data management work will enable you to make meaningful data visualizations from which your organization can derive real value.

The benefits of structuring your data will extend beyond the increased data visualization and analysis potential. Adopting FAIR data governance and structured metadata puts your organization in a better position to pass an audit from a regulatory body and ensures data integrity.

Understand What Data Analytics You Have and Can Use

Maybe you aren’t ready to take the big step of completely reorganizing your data; it’s a lot of work, we get it! (If you are ready, though, our Data and Analytics services might be useful to you.) If a complete overhaul isn’t practical, you can always start small. Think about what you need to understand about your current systems and data structures to extract the right analytics for meaningful data visualizations. Look at where there are gaps in your current data management system. For example, which of your systems talk to one another now, and which ones don’t? Finding the most efficient way to access all of the data you already have, within your organization or among your customers and suppliers, is the next logical step in developing a data visualization strategy.

There are, of course, pros and cons to cross-domain data sharing. You may run into resistance to data sharing across lines of business within your organization. Intellectual property is more strictly controlled in some businesses than in others, so this is a legitimate concern. Internal data sharing also can create conflicts of interest in some businesses. However, siloed data is a danger as well, because copies of data quickly become inefficient. If there is organizational data that can be shared externally, it may be possible to derive value from licensing that data. Like it or not, data management strategies and big data sharing are the future for organizational growth. 

It is possible to create pathways to access data even when you haven’t built a data warehouse or data lake from the ground up. The point is that all your organization’s data should be available as a source for robust visualizations. Be aware, though, that the more such workarounds that you add to your systems the more complicated your data structure will be. A complicated data structure makes it harder to enable future growth. 

Once you have developed some sort of structure, through which and in which all of your data can communicate, you are ready to create good, maybe even elegant, data visualizations.

Think Strategically About Data Visualization

It’s important to continue being strategic with your data visualization progress. As you work on developing cross-platform sharing within your organization, the thought of what information you need for effective data visualizations should be your focus. Think about why you want a particular visual—what message will it convey or what story will it tell? Your data visualizations should be created to add value for your target audience.

Data visualization is a buzzword that has become so pervasive as to almost lose meaning, and without a thoughtful data strategy you’re merely adding noise. Bad visualization techniques are more common than good ones. To avoid bad visualizations, consider not only what you’re depicting but also how. Familiarize yourself with data visualization accessibility standards and best practices.

Then, when you tell your story, you’ll know where to find the data to support it and how to tell that story in a way that will be easily understood by everyone in your audience. Optimizing your data structure will put you in the best position possible to spot result patterns or data clusters that you might have missed in the past.

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What data management steps have you taken to set your organization up for more effective data visualization?

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