From Data to Business Value: How Data Visualization Helps

Decision-makers have access to big data and the challenge for these decision-makers and organizations is to extract patterns, trends and correlations to do have a well-informed decision-making process. This is where the whole domain of data visualization starts, it are innovative tools that help decision-makers to effectively and efficiently gain value from all the available data.

From data to value

The first step is to understand the phases from data to actual business value, the three phases are:

  • Data: these are the actual facts from databases
  • Information: this is ‘data’ and a contextual layer upon it to give it sense. Without context data in itself is less meaningful
  • Actionable insights: these are an upgrade from ‘information’ with added understanding and behavior that is taken upon data. Only then, when effective action is taken upon data, it proves it business value to a professional, team or organization. If not, it’s just a lot of expensive information.

A slightly different process van data to insights is the flow below from this blog:

Data visualization as cost-effective enabler

Data visualization tools are enablers to cost-effective decision making, ‘extracting’ value from raw big data. If you take a look at the three phases, data visualization helps in all phases but particulary the first two. It enables to visualize quickly lots of data and combining several data sources it could support the process in making sense out of it faster.

The role within the actionable insights phase is more a persuading one, showing the team or other professionals what the status is from where action can be taken.

But more importantly I think in this phase it’s the tacit knowledge, the experience, the expertise of professionals and business understanding that is the decisive factor in getting true business value from data.

Data visualisation secrets

Nate Agrin and Nick Rabinowitz wrote a great article on the seven dirty secrets of data visualization. In their article they offer an inside look at the process of visualisation development, along with practical tools and approaches for dealing with its inevitable challenges and frustrations:

Secret #1: Real data is ugly

When dealing with most real-world data sets, expect to spend up to 80 per cent of your time finding, acquiring, loading, cleaning and transforming your data. Some of this process can be done with automated tools, but almost any data cleaning involving two or more data sets will require some level of manual work.

Secret #2: A bar chart is usually better

Compared to bar charts, bubble charts support more data points in less space, doughnut charts clearly indicate part-whole relationships, and treemaps support hierarchical categories – but none match simple bars for fine-grained comparison. 

One of the first questions to ask when considering a potential visualisation design is “Why is this better than a bar chart?” If you’re visualising a single quantitative measure over a single categorical dimension, there is rarely a better option.

Secret #3: There’s no substitute for real data

Unfortunately, there is no substitute for real data. Demo data tends to have a normal distribution and a manageable number of records; it’s designed to show visualisations in their best light. A bar chart doesn’t just have the prerequisite bars, it looks like an ideal bar chart. It doesn’t help you plan for data discrepancies, null values, outliers, or other real-world problems. If you rely too much on demo data, when you plug in real data you may find that your visualisation isn’t the best one suited for your data to begin with.

Secret #4: The devil is in the details

Laying out labels horizontally can quickly lead to crowding and illegible text (top). Rotating labels 90 degrees improves legibility, but takes away significant space from the visualisation. Finding a truncated or abbreviated label format is one approach, but won’t work for every data set. 

Designing the labels, legends and axes for your visualisation is often an afterthought to the initial visualisation. But these elements are crucially important to the visualisation, and can be difficult and time-consuming to get right, especially when you can’t predict the data ahead of time.

Secret #5: Animate only when appropriate

Animations are a powerful way of connecting data to changes in state and trends. However, animations can also lead to confusing or misleading interpretations of your data. You should carefully plan for how it will affect your entire output and not simply add it at the end of your work. Animations work best when they can reveal data relationships showing how data groups together between different states, how the data changes over time, or how data points are directly related.

In general, make your animations simple, predictable and re-playable. Allow users to view the animation multiple times so they can track where objects start and end. Avoid occluding objects in a transition with other objects, which makes tracking more difficult and do not transition objects along unpredictable paths

Secret #6: Visualisation is not analysis

It’s a central tenet of the field that data visualisation can yield meaningful insight. While there’s a great deal of truth to this, it’s important to remember that visualisation is a tool to aid analysis, not a substitute for analytical skill. It’s also not a substitute for statistics: your chart may highlight differences or correlations between data points, but to reliably draw conclusions from these insights often requires a more rigorous statistical approach. (The reverse can also be true – as Anscombe’s Quartet demonstrates, visualisations can reveal differences statistics hide.) Really understanding your data generally requires a combination of analytical skills, domain expertise, and effort. Don’t expect your visualisations to do this work for you, and make sure you manage the expectations of your clients and your CEO when creating or commissioning visualisations.

Secret #7: Data visualisation takes more than code

The range of libraries and tutorials now available make it easier than ever to produce production-quality web-based visualisations without specialised expertise. But creating visualisations that offer real insight or tell a compelling story still requires a particularly wide range of real skills in addition to coding, including graphic design, data analysis, and an understanding of interaction design and human perception. No library or technology can substitute for knowing what you’re doing.

Read the complete elaboration ánd tools and strategies on all seven secrets here.

The last two secrets fit with the aforementioned, that actionable insights is an other additional step in the complete process.

As elaborated in this article, I think a team of people with different skill-sets and competencies are able in most effectively extracting insights from data.

Do you make use of data visualization to fasten insights discovery?. What other dirty secret do you know about data visualization?.

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