You Need a Data Visualization Strategy, Here's Why

Find out what makes data visualization a vital asset in your data strategy toolkit.

You Need a Data Visualization Strategy, Here's Why

95% of U.S. businesses say they use data to power business prospects, and another 84% agree that data is critical to developing a successful company strategy, according to a survey by Experian.

People think that since the amount of data available is growing at an exponential rate, decision-makers are in a good position to reap the benefits. However, this is not always the case; more data is not always better. Dealing with huge amounts of data can be overwhelming and cause companies to "drown" if they don't deal with certain problems before moving into a data-driven future.

The Big Issue with Big Data

The first hurdle to overcome when considering what to do with your data is cleaning it. Actually, the same optimistic Experian research shows that only 44% of decision makers trust their data, and most C-level executives are cautious because they think that 33% of their data is wrong. Cleansing the raw data is the first step for businesses that depend a lot on their assets and want to be sure that their decisions are supported by the terabytes of data they have, no matter where the "dirty" data came from or why it was dirty.

It's not easy to draw conclusions from data streams, and it's even more difficult to do so without first learning how to extract significant dependencies and patterns. Lastly, it is the discovery of insights and useful correlations that helps businesses increase sales, cut costs, and find new ways to make money. Otherwise, companies risk having wonderfully organised graphics with very little value.

Data strategy is another one on the list. Even if you've successfully scrubbed your data and mined for insights, you still need a plan for applying all that gold. Market and industry research, the selection and prioritisation of data streams (real-time or legacy, internal or external, or both), and the selection of tools and sources for data processing and analytics (e.g., human data scientist analysis, cutting-edge machine learning algorithms) are just a few of the many steps that make up a data strategy. But without a smart data strategy, the time and money spent on figuring out how to use data could be wasted.

The Power of Data Visualization to Drive Business Decisions

Data Cleansing

The simplest way to understand the relevance of visualization is to look at it as a great method of making sense of data. Data visualization tools, even the most basic ones that mix pie charts and bar graphs, make it far easier to digest enormous volumes of data than it would be to do so with traditional methods like paper reports and spreadsheets.

Data scientists and decision makers can greatly benefit from visualization since the visual cortex and pattern recognition capabilities of the human brain help them quickly grasp the meaning, recognise patterns, and spot inconsistencies and errors.

Thus, visual analysis serves as a first line of defence in ensuring the integrity of data streams. Visualization tools provide preliminary standardisation, shape data in a uniform way, and produce visual objects that are easy to verify by combining data from multiple sources. So, these technologies are important for cleaning and checking data, and they help businesses get ready to get insights from high-quality assets.


Data extraction tools have been around for quite some time; the humble pie chart may trace its roots back more than two centuries. However, these tools have never been as advanced as they are today.

From monitoring performance to bettering the customer experience on mobile tools, businesses can benefit from using well-known, flexible tools for data visualization and analytics, such as Tableau, Power BI, or Qlik Sense, and more complex database solutions like Hadoop, Amazon Web Services, and Teradata.

However, the new generation of data visualization based on AR and VR technology delivers advantages that were previously impossible, especially when it comes to seeing patterns and deducing insights from diverse data streams.

By building 3D data visualization spaces, companies can create an intuitive environment that helps data scientists grasp and analyse more data streams at the same time, observe data points from multiple dimensions, identify previously unavailable dependencies, and manipulate data by naturally moving objects, zooming, and focusing on more granular areas.

Moreover, these tools allow us to increase the potential of data visualization by constructing collaborative 3D environments for teams. As a result, new technologies help extract more relevant insights from the same volume of data.


As the amount of data expands, it gets difficult to catch up with it. Therefore, data strategy becomes a vital aspect of the success of applying data to a company.

Here are some of the reasons why data visualization is so useful for strategic planning:

First, it helps you purify your data. Secondly, it helps you detect and extract relevant information from it. Data visualization tools allow you to keep tabs on the impact of your strategy and new data-driven decisions on performance and business outcomes in near-real time. In other words, these tools help adjust and optimise strategy in real time by visualising not only the data but also the outcomes.

Clients use this information to do things like schedule maintenance at the right time, find and get rid of hidden IT costs, and improve performance. Performance data evolves over time in response to customers' actions; it depicts the outcomes of their data-driven decisions; and it yields insights that help enrich and optimise future strategies.

Decision-makers use changing data views to decide whether to increase or decrease data holdings, choose new combinations of data sources to dig deeper into analytics, decide what to do with legacy data, and plan how to use cutting-edge technology like machine learning and data science solutions to get the most out of real-time streaming data.

Data visualization is one of the initial steps taken to derive value from data. This is an important step because it affects how much analysts can use their data assets, how good the insights they can get from that data, and how their data strategy will move forward.

To conclude, the value of data to your business strategy and the advantages data applications can bring to your sector are directly related to the quality and capabilities of the data visualization you employ.