A picture is worth a thousand words. This adage is common in numerous languages today, but it originated in Norway with 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. You need a thoughtful structure for your data analytics before you can derive meaning from them. An organization’s lab informatics expert may be uniquely positioned to develop a comprehensive data structure and visualization program because of how much contact labs have with the rest of the organization.
It should be fairly obvious that you can’t make visualizations with data you don’t know you have. Providing a method for structuring your data so you can 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:
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. FAIR data governance and structured metadata not only makes your organization more likely to pass an audit from a regulatory body, but also ensures data integrity. Undertaking this data management work will enable you to make meaningful data visualizations from which your organization can derive real value.
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.) What do you need to know about your current data system to obtain the analytics you need to produce meaningful data visualizations? Look at where there are gaps in your current data management system. Which of your systems talk to one another now, and which ones don’t? Knowing what data you can access is the next logical step in developing a data visualization strategy for your organization.
There are, of course, pros and cons to cross-domain data sharing within and outside an organization. You may run into resistance to data sharing across lines of business. Intellectual property is more strictly controlled in some businesses than in others, so this is a legitimate concern. Internal data sharing 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 important point is that all of 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.
At this point, it’s important to continue being strategic. 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. Machine Learning Solutions/Tools, AI Solutions/Tools, Dashboard, Digital Transformation, Data Science, Optimization if we can do cross-platform visualization and opportunity visualization by showing clients opportunities they may not have seen, result patterns, and data clusters. Lisa also mentioned that this is particularly relevant with clinical trial work.