A picture, as has often been said, is worth 1000 words. The same is true of data. The importance and usefulness of data visualization has long been known.
Anyone who has ever watched a slide presentation knows firsthand how much easier it is to digest and remember information formatted in colorful, well-labeled charts than it is to take in and retain information written out in paragraph form.
However, data visualization capabilities in the analytics world have progressed far beyond the ability to convert data tables into charts.
The most effective visualization models are now interactive — giving users additional depth and insight beyond what meets the eye.
In addition to optimizing the presentation of data findings — useful during presentations to stakeholders and group meetings — data visualization models help employees “detect outliers and unusual groups, identify trends and clusters and spot local patterns,” as Harvard Data Science Review notes.
These functions make visualization tools a must-have for companies aiming to get the most from their collected data.
Keep these four interactive data visualization best practices in mind when your enterprise is evaluating its approach to analytics with the goal of driving successful decision-making.
The first best practice is, well, going beyond static charts — which tell part of the story, but fail to provide additional context by empowering the user to keep digging for all the relevant information available ahead of making a decision.
Interactive data visualizations allow users to drill into data beyond the surface level by clicking on different features to explore and asking follow-up questions on the fly as they occur. The ability to zoom in and out to understand what the data is conveying, why, where it came from and how it relates to other data insights tends to fuel more informed, nuanced decision-making.
Moreover, the ability to drill into all of the data behind the chart, in an infinitely granular fashion — as well as to other datasets,as opposed to a limited predefined dataset — is exceptionally valuable.
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Think about the wide range of users inside and outside your company who can benefit from access to interactive data visualizations. Different stakeholders, on the individual and team level, will have different needs and uses for data analytics tools.
Thus, personalization is key to helping everyone get the most from these models. Although advanced data analytics platforms typically automate the creation of best-fit charts, the ability to further personalize standalone models and configure them into customized dashboards and pinboards gives users the flexibility to apply the insights most relevant to their own roles.
Personalization is a key factor when it comes to avoiding information overload, in which users are bombarded with more generic data insights than they can possibly use. This can sometimes be true to the point it impedes decision-making and renders users averse to harnessing data due to fatigue.
Another way to closely connect data visualization to decision-making is to embed charts and pinboards in context — wherever they are most closely aligned with user workflows and thought processes.
As Gartner writes, embedding interactive data visualizations into “a user’s natural workflow” removes “the need to toggle to another application.” This also aligns insights with whatever processes they affect; be they sales, marketing, supply chain management, customer service, finance or etc.
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It’s only natural for users to wonder not only about what the data is telling them but from where the data originated.
Making the lineage of data insights traceable back to their sources instills trust in users and helps them understand the numbers better — and ensure they are interpreting insights accurately and fully before they apply them to decision-making.
When data visualizations are interactive, customizable, embedded in context and connected to source data, users get the most from the insights they provide.
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