If you think about the worst analytics scenario, it’s probably staring at an unfiltered Excel sheet trying to make sense of all the data. If you consider the amount of data that’s being generated and processed daily, it becomes pretty apparent¾this would not be a very productive way of going about it.
Providing a visual context for data is very important for analytics. It gives a visual representation of data trends that couldn’t be spotted otherwise. Why so? The most basic reason is our biology. The data that we process is mostly visual. In fact, 90% of the data that our brain processes is visual, and images are processed 60,000 faster than text. It’s in our nature to digest visual information efficiently. That’s why even the most basic data processing tools, like Excel or the free LibreOffice, contain at least some data visualization capabilities.
In this article, we’ll try to cover some of the essential aspects of big data visualization and its invaluable role within a modern business. We’ll also talk about some of the basic vocabulary and things you need to consider before choosing a BI tool that promises you amazing visualization capabilities.
It’s important to understand that data visualization is a support tool for your analytics efforts. We’ll cover its various capabilities below, but if your business is looking to generate advanced insights and drive strategic decision-making, then visualization is not the whole answer. You might be better off exploring predictive or prescriptive analytics. But even in these domains, data visualization is essential to building the basic understanding of trends and forecasts.
Bill Shander, CEO of Beehive Media, put it very bluntly: “If you’re selling straightforward solutions to simple problems, data visualization is probably not worth the money.”
Data visualization is great for mining data and making sense of highly diverse datasets. But if your business is pretty straightforward, then you probably won’t see any major ROI out of having a tool like that in your arsenal.
Spreading analytical efforts across an organization is a tough challenge. There are many blockers on the path. But one of the biggest ones is the lack of skills required to process and make sense of data.
This is where data visualization becomes increasingly helpful. Anyone can process visual information and identify anomalies and outliers. These basic capabilities can drive insights discovery, which in turn can power analytical hypotheses. These hypotheses can then be formulated and shipped to the appropriate analytical unit for further investigation. A by-product of this process is the overall actualization of business knowledge for all the users. If you analyze datasets on a recurring basis, you’re bound to become more informed about business performance.
For example, DePaul University uses Tableau data visualization tool for this particular purpose¾to discover ‘aha’ moments through data analysis of datasets with millions of rows. There’s no other way to look at such voluminous data.
Images are often capable of telling a much better story than text. Especially when you have a limited amount of time or an audience that’s quite hard to captivate. Even when you’re not that good of a narrator, data visualization can help you tell the success story a lot more efficiently.
And even in this case, there are certain rules of engagement. You have to know what data to transmit through the image. Even companies like Google struggle with data visualization. People like Cole Nussbaumer Knaflic, who worked at the company, built a career out of making better visualizations.
This practice becomes vital for business analytics and data scientists. Often the stakeholders that they’re talking to don’t understand how most of the complex data analysis works. Data visualization helps to bridge that gap.
You can always dive deep into the numbers, but visualization provides the ability to quickly make sense of the trends that are omnipresent throughout these numbers. This kind of information can provide insights into the seasonality of the business, or any other type of recurring patterns that might be obvious when visualized, but hidden when presented in a plain form.
For example, you don’t have to be an analyst to understand the correlation between months and sales in the chart below:
Now, take the same concept and imagine it extrapolated onto big data, where you might have dozens of months and a corresponding number of sales reps. Suddenly, the bigger picture becomes clearer:
This concept can be applied to a multitude of analytical goals, like marketing, operational analytics for manufacturing, software performance, and any other domain that deals with large amounts of data.
It’s one thing when you’re sifting through your data in search of some vital business information. A different thing would be getting alerts for specific actions that your customers or visitors make. But what if you’re looking for something that’s not available through a standard query? What if your business operation team needs time to formulate the solution and execute it to find you the right answers?
Big data visualizations are a great tool for doing just that¾finding relevant information quickly and being able to process it without wasting time and resources. According to the study by the Aberdeen Group, managers that use data visualization and discovery tools are more likely to find relevant and timely information by up to 30%.
That’s a powerful metric that shows how visualization can improve business processes and optimize your company’s business resources.
Depending on the tools that you use for visualization, you’ll be given plenty of opportunities to play around with the data. A good data visualization tool should allow you to interact with the data comfortably and efficiently.
Looking at static dashboards that only change when the query parameter is changed is not enough. You should be able to click data points, see the original data and discover new trends.
These features work for the reasons outlined above—they provide faster access to data that can be processed much more efficiently than via a standard analytical query.
This feature of data visualization tools is related to the previous point of improving interactivity. You’re given two options: a) to scroll through an Excel sheet, or b) to click around some fancy and colorful graphs. Most of the sane folk would probably pick option b.
And this is the power of data visualization. It makes big data far more approachable, easy to digest and make sense of. This alone can improve any data-driven initiatives. People can’t drive them unless they actually discover data and get intimate with it.
You need to carefully consider all of the big data visualization tools that are available out there. You can start with those that provide access for free, like Microsoft Power BI. But keep in mind that there are many other vendors that have invested a lot of time and resources into building data visualization capabilities, for example:
Each of these tools has its advantages. But it’s also important to remember that the tool has to fit into your business process and technical requirements. It’s better to consult your team, starting with people who want to be able to discover and interact with the data. From there, you can build out the basic requirements for the tool and let the analytics or business operation teams handle the technicalities.
A good data visualization tool will combine an intuitive interface with rich visualization pool including all charts, graphs, and plots that you need to view and explore your data efficiently.