Do you have a moment? Think about all the data your enterprise has - customer information data; website, transaction and buying pattern data; social media data. Now, imagine analyzing this data, and all of the other data you have, to identify patterns and trends, and glean meaningful insights for smarter business decision making.
Sound impossible?Fortunately, analytics and data visualization can do this for you, and more efficiently than even the most deft human is capable of. Data visualization can present your data in intelligible ways. You can spot trends from simple pie charts and bar graphs, and dashboards are a particularly useful tool for displaying several metrics in a single view.
So, how do you best incorporate data visualization at your enterprise? Keep reading for the best practices today.
Getting started with your data visualization tool
Even the most powerful tools are of little use without a clear strategy, and the same is true for data visualization. Keep this in mind when you begin.
1. Define your need for data visualization
Why is your enterprise in need of data visualization? Your answer here will dictate everything going forward. For example, monitoring customer satisfaction requires different data sets, metrics and visualization outputs than monitoring sales performance. The relevancy of visualization outputs depend on the end user, which brings me to my next point:
2. Identify who will be using your insights
Consider your companies sales manager and chief financial officer; metrics like average deal size may be important to your sales manager, but less so for your CFO, whose concerns center on sales growth and churn.
Information is prioritized and interpreted differently, depending on the end user. So, make sure the visualization you’re designing is one that best serves them.
3. Determine your visualization type
What goals are you trying to accomplish through visualization? This will help you determine the types of data you’ll want to display on your dashboards. Not all dashboards are the same; rather, they’re classified in the following ways:
- Strategic: High-level metrics that best indicate company strategy and goals (e.g. KPIs). Strategic dashboards require minimal interaction
- Analytical: Interactive metrics to encourage further investigation by end users
- Operational: Dashboards that monitor metrics related to operations and performance. These are often updated in real-time
Working with your data visualization tool
With a set goal and your end user in mind, it’s now time you identify which data is right to input into your tool. These tips will help streamline this process:
1. Create a methodology
Your data visualization methodology should include your processes for gathering, analyzing and presenting your data. A clear process ensures consistent outputs, as all data will have been collected in the same way.
How you’ll use your visualization outputs, and your end goals, will help you decide which methodology approach is best for you. For example, agile methodologies fixate on flexibility and adaptability.
2. Choose the right visual
Different types of data are best represented in different ways; Pie charts best represent parts of a whole, while line charts track trends over time. Consider the following:
- Bar charts compare quantities of different groups
- Line charts show the relationship between two or more variables, and they track trends over time
- Scatter plots and bubble charts show joint variation of two and three data points, respectively
3. Don’t be afraid to fail with your data visualization efforts
Don’t fear failure when you’re first attempting data visualization and instead, leverage an iterative design.
Begin with a prototype and test it with end users, like your employees or customers, to solicit feedback. Then, make real-time adjustments from what they say.
Getting started with best practices in-mind
Your data visualization planning will only get you so far and eventually, the time will come when you have to move forward with a design. Though debates may rage on internally, giving your data visualization product to your end users is the only way to truly gauge its usefulness.
See how Antuit combines data science, domain knowledge and data visualization expertise with the latest reporting technologies to enable insights-driven organizations.
