According to an Experian report, 95% of U.S. organizations say that they use data to power business opportunities, and another 84 percent believe data is an integral part of forming a business strategy.

With an exponentially growing volume of data, it may seem decision makers are well off and should prepare to reap generous benefits from their multiplying assets. However, more is not always better. Dealing with large amounts of data can be overwhelming and result in “drowning” if the companies don’t solve particular challenges before stepping into a data-driven era.

Challenges in Working with Big Data

1. Cleaning your data is the first barrier to break before you start thinking of what to do with your data. In fact, the same optimistic report by Experian points out that fewer than half (44%) of decision makers trust their data, and C-level executives in general are skeptical, believing that 33% of their data is inaccurate.Even though the reasons of “dirty” data vary – inconsistency, incompleteness, human errors – cleansing the raw data is step zero for companies that truly rely on their assets and feel confident backing up their decisions with the terabytes of data.

2. Deriving insights from the streams of data, and most importantly, understanding how to extract meaningful dependencies and finding patterns in the data is another challenge. At the end of the day, it’s the insights and relevant correlations that allow companies to solve business problems, drive sales, cut costs and find new revenue streams.Otherwise, businesses may end up with perfectly structured graphics that have the same value as the charts on how per capita consumption of mozzarella cheese correlates with awarded civil engineering doctorates, or the number of people drowned by falling into the pool relates to the films Nicolas Cage appears in.

3. Data strategy is another one on the list. You may have the data cleansed and the insights extracted, but you should also figure out what to do with all the treasure. Data strategy supposes many steps, from analyzing market and industry, choosing and prioritizing data streams – real-time or legacy, internal or external, or both, etc., to selecting tools and sources for data processing and analytics – manual data scientist analysis, cutting edge machine learning algorithms. However, without a clever data strategy, the whole investment and effort put into leveraging data may end up unhelpful, as in the example mentioned before.

How Visualization Helps Data Impact Business

Cleansing

The simplest way to explain the importance of visualization is to look at visualization as the means to making sense of data. Even the most basic, widely-used data visualization tools that combine simple pie charts and bar graphs help people comprehend large amounts of information fast and easily, compared to paper reports and spreadsheets.

This powerful advantage of visualization and, obviously, visual cortex and pattern recognition capabilities of the human brain allow data scientists and decision makers to quickly grasp the meaning, identify trends and even notice inconsistencies and errors.

In other words, visualization is the initial filter for the quality of data streams. Combining data from various sources, visualization tools perform preliminary standardization, shape data in a unified way and create easy-to-verify visual objects. As a result, these tools become indispensable for data cleansing and vetting and help companies prepare quality assets to derive valuable insights.

Extracting

Data visualization instruments have a long history – a simple pie chart is more than 200 years old. However, these tools have never been so advanced as they are today.

Known versatile tools for data visualization and analytics – Elastic Stack, Tableau, Highcharts, and more complex database solutions like Hadoop, Amazon AWS and Teradata, have wide applications in business, from monitoring performance to improving customer experience on mobile tools.

For example, when you are looking at your Fitbit dashboard, it is Highcharts’ graphs and diagrams you see while checking your steps and calories intake.

New generation of data visualization based on AR and VR technology, however, provides formerly infeasible advantages in terms of identifying patterns and drawing insights from various data streams.

Both highly specialized platforms like Virtualitics and grand masters like IBM rely on AR and VR technology to introduce immersive visualization with brand new benefits for business and the whole industries.

Building 3D data visualization spaces, companies can create an intuitive environment that helps data scientists grasp and analyze 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 granulated areas.

Moreover, these tools allow us to expand the capabilities of data visualization by creating collaborative 3D environments for teams. As a result, new technology helps extract more valuable insights from the same volume of data.

Strategizing

As the amount of data grows, it becomes harder to catch up with it. Therefore, data strategy becomes the necessary part of the success in applying data to business.

Here’s what makes data visualization an important tool in your strategic kit:

First, it helps you cleanse your data. Secondly, it allows you to identify and extract meaningful information from it. Finally, data visualization tools enable continuous real-time monitoring of how your strategy and now data-driven decisions influence performance and business outcomes. In other words, these tools visualize not only the data, but also the results, and help correct and optimize strategy on the go.

Let’s take Applixure platform as an example. In a nutshell, Applixure hardware and software monitoring tools collect performance data from a client’s IT infrastructure in real time, visualize this data and provide in-depth system health overview.

Clients use this overview, for example, to schedule timely maintenance, identify and cut hidden IT costs and improve performance. Over time, performance data changes depending on a clients’ actions, illustrates the results of their data-driven decisions and provides the insights that help enrich and optimize their strategies in the future.

Based on updating data views, decision makers determine if they need to expand or reduce their data assets, select new combinations of various data sources to drill deeper into analytics, consider what to do with legacy data and how to get the best of streaming real-time data using modern technology – machine learning and data science solutions.

Data visualization is one of the initial steps made to derive value from data. It’s also one of the most important steps, as it determines how efficiently analysts can work with data assets, what insights they are able to extract and how their data strategy will develop over time.

Therefore, the quality and capabilities of data visualization directly influence how data impacts your business strategy and what benefits data applications can bring to the companies and their industries.

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