Data Analytics or Data Visualizations? Why You Need Both

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    While it’s now touted as one of an organization’s most valuable assets, data alone is worthless without supporting technologies to mine it, process it, organize it, and analyze it. Said another way, the value of business data isn’t in each bit, byte, or field, but in the results and insights that can be gleaned from the relationships between data points. The “bigger” the data, the more true this comes.

    Recognizing this, more and more business intelligence (BI) solutions are emerging that collect all varieties of data, reach across numerous departments, and serve a multitude of purposes. For reasons of practicality and efficiency, it is best for all of these solutions to be tied into a single BI tool to deliver the most accurate, complete, and broad picture of the performance of the business. But, once obtained, what tools will give decision-makers the best insights, conclusions, and results to steer with? What should a business do to reap the greatest value from the masses of data they’re collecting?

    Data Analytics or Data Visualization?

    Data analytics has proven its worth time and again by helping businesses examine structured and unstructured datasets and extract useful information so key stakeholders can make more-informed and more effective decisions. Analytics can be prescriptive, predictive, diagnostic, and/or descriptive to produce insights, observe trends, compare metrics, and more.

    But it can only do so much. Endless columns and rows of alphanumeric data can be difficult to digest at scale. Depending upon the level of detail that stakeholders need to draw actionable conclusions, as well as the need to interact with or drill-down into the data, traditional data analytics might not be sufficient for businesses to excel in today’s competitive marketplace. Additional tools are needed to help extract more timely, more nuanced, and more interactive insights than data analysis alone can provide.

    Those tools are data visualization tools.

    The reason data analytics is limited might be simple enough. Data analytics helps businesses understand the data they have collected. More precisely, it helps them become cognizant of the performance metrics within the collected data that are most impactful to the business. And it can provide a clearer picture of the business conditions that are of greatest concern to decision-makers.

    But analytics does not do what data visualization can do: help to communicate and explain that picture with precision and brevity while in a format that the brain consumes exceedingly quickly. The data itself isn’t changed by data viz; further analysis isn’t done. But two-dimensional tables of data are not very amenable to learning; the mind tends to gloss over a large amount of it, scan for highest and lowest values, and miss the details in between. Data visualization doesn’t have that problem. Quite the opposite, the visuals are often compelling as they literally draw the picture of the metrics in question.

    Make your data human-friendly
    Data visualization takes the results of the queries and computations of data analysis and puts them into a more dynamic and human-friendly format. It summarizes and delivers complex ideas, correlations of intricate relationships, and the results of multiple tiers of variables to those that need them.

    In many cases, data visualization allows real-time interaction, allowing users to drill down into the minute details of a chart right on their computers and mobile devices. This vertical interaction allows stakeholders to select different data sets, view the results of different filters, and otherwise fine-tune their view of the data to answer specific questions they have in the moment.

    Visually compelling metaphors of data visualization, like charts, graphs, gauges, and maps, help business understand the story of trends and stats much more easily. They can often reveal patterns, trends, and correlations that would easily go undetected otherwise. Through comparative metaphors and the like, decision-makers can more immediately assess the significance of certain metrics over others.

    7 Reasons To Employ Data Visualization Tools

    There are seven primary reasons to use data visualization tools to communicate the results of data analytics:

    • Absorb large amounts of data at scale
    • Compare and contrast metrics
    • Make explicit business trends, patterns, and insights
    • Monitor trends and patterns
    • Reveal questions that would otherwise be missed
    • Simplify reporting
    • Experiment with different scenarios

    Additionally, data visualizations often allow users to interact with the image for a deeper level of insight or to see what data is feeding what results. Sometimes users can even change variables to see the effects. This type of user engagement typically sparks additional curiosity which leads to more in-depth analysis, further improving decision-making.

    Principles of Effective Data Visualization

    The most productive BI tools cause people to think about the meaning of the data they’re looking at and not focus on the tool, mechanics, images, or anything other than the information at hand. Effective use of data visualization tools like dashboards recognize that certain types of information are better communicated with certain metaphors. Implementing an effective data visualization tool begins with identifying the audience and clarifying the objectives of the visualization. It also includes using side-by-side comparisons and drawing the viewer’s attention to the most important, most relevant data.

    Working Hand in Hand
    Both data analysis and data visualization are critical BI tools to mine the power within an organization’s vast collection of data. Working hand in hand, they can deliver the most impactful and actionable insights for key stakeholders to run with. But data visualization is only as good as the analytics that supports it. Like the decades-old adage, “garbage in, garbage out,” the insights it depicts depend on the integrity of the analytics provided. Poor data models, or unclean or incomplete data—no matter how well presented visually—do not serve anyone. On the other hand, clean, sophisticated, and thorough data analysis can provide the raw materials to build influential and valuable data visualization tools like dashboards to give decision-makers the insights they need to drive their business.

    Neither data analytics nor data visualizations alone can serve as the sole component of a powerful, dynamic solution for data processing in today’s competitive marketplace; they should work hand in hand to leverage the power of your data. To reap the greatest value from your BI tools, use a solution that combines both a backend data analytics platform to manage the data analytics tasks you need and an intuitive frontend dashboard tool that delivers data visualizations to convey information powerfully and intuitively. Take a look at a few practical examples of data visualization dashboards from ClicData.