Improving Predictive Analytics with Data Visualization
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Improving Predictive Analytics with Data Visualization

Fadi Elawar, Technical Consultants Team Lead, iDashboards
Fadi Elawar, Technical Consultants Team Lead, iDashboards

Fadi Elawar, Technical Consultants Team Lead, iDashboards

Today, business intelligence solutions continue to grow more powerful and sophisticated, tracking variables with greater precision while gathering enormous amounts of information in real time.

But with more data and variables comes more challenges. Sifting through the details and untangling the complexities and interrelationships between different pieces of information has become increasingly difficult. In today’s data-rich environment, what differentiates successful organizations from the rest what separates winners from losers is the ability to understand, interpret and leverage that data to separate the signal from the noise and turn raw information into meaningful insights. Data without context and connection is meaningless.

Perhaps the most promising and productive way to do just that is through the fast growing and rapidly evolving practice of predictive analytics. Predictive analytics is a branch of business intelligence that goes beyond merely interpreting or contextualizing data. Instead, it uses historical information and transactional data to predict the future with remarkable accuracy.

While predictive analytics has been around for some time, new tools and technologies have come together in recent years with higher volumes of increased quality data to yield impressive results.

  ​With predictive analytics and dashboard technology, businesses can turn information into knowledge   

Predictive power

While the use of predictive analytics is particularly helpful to make practices such as credit risk assessment and fraud detection more effective, these techniques are hardly limited to banking and financial services. In fact, predictive analytics can be (and is) used in almost any industry where organizations with accumulated historical data are looking to use technology to boost performance, reduce costs and minimize risk. It is especially prevalent in government, healthcare, manufacturing, banking, education and retail.

Predictive analytics encompasses a remarkably flexible set of tools that can be used by organizations in many different ways. For instance, companies use past sales to help predict what the future of their sales will be over the course of a defined term. Businesses can predict progress on key performance indicators (KPI), and predictive analytics can be used in operations to plan ahead and improve strategic decision-making with regard to things like inventory, staffing and warehousing. It is also used in marketing to analyze web traffic and predict future trends, and can even be utilized to predict equipment failure for preventative maintenance, as well as budgeting for future needs.

Imagine a company like Walmart, which literally processes and tracks billions of transactions annually. Walmart can apply predictive analytics to that data not only to predict future sales, but also how often different products need to be restocked and reordered. They can even provide insight into where products should be stocked based on what predictive analytics suggest about what products are likely to be sold in tandem with other products or in different seasonal quantities. For example, the amount of hot chocolate you need to have on hand in August will likely differ dramatically from what you need in December. That kind of information has a profound impact on day-to-day operational decisions, and can even impact store layout and design. Overstocking and inefficient inventory practices can be enormously expensive, and companies like Walmart can and do save staggering amounts of money every year using predictive analytics to improve efficiency and make smarter, more informed decisions.

Unmatched clarity

For all of the power and analytical rigor of predictive analytics, the missing piece of the puzzle is a way to comprehend and convey the information being processed. Analytical models can be enormously complicated, and finding a way to visualize the results in report form can be extremely challenging.

Enter data visualization dashboards

Dashboards are highly visual business intelligence tools that are dramatically easier to read and under-stand than Excel reports. As a result, users spend less time trying to figure out what the data is telling them and more time doing something about what the data is telling them. Dashboards are specifically designed to coordinate and display large amounts of data in a visual way that is clear, concise and comprehensible. The best dashboards are intuitive and user-friendly, providing a single screen that displays everything users need to know in one glance.

High quality dashboard solutions provide meaningful insights into even the most complex operations, utilizing a visually dynamic presentation and the ability to bring together multiple sources of data into a single resource. Users can not only view key metrics and measurables, they can actually interact with the data, drilling down to reveal important connections, trends and relationships that may not have been evident at first. Dashboards support decision-making by moving away from compartmentalized data and presenting the whole picture.

On one level, a dashboard functions like an interpretive tool for predictive analytics (informing a manager when it’s time to reorder widgets, for example). But dashboards can also help improve make predictive analytics capabilities. Because those clear visuals can identify relationships between vari-ables that users may not have realized were connected, they can actually help improve and enhance predictive models. This allows companies to adjust and fine-tune predictive modeling as outside variables and circumstances evolve.

That synergy is a big part of the reason why dashboards and predictive analytics are such a good fit. A picture is worth a thousand words, and visual solutions tend to strip the complexity away from the data. In other words, dashboards help interpret what your analysis is telling you. That’s important, because no matter how good the measuring tools are, the human capacity to translate that data is critical. Your speedometer may tell you that you’re driving 55 mph and obeying the speed limit, but it takes a human brain to know that’s not a safe speed if it’s snowing and the roads are slick.

Happily, the growing popularity of dashboards comes at a time when they are more affordable and accessible than ever. Once a luxury reserved for larger corporations, dashboards can now be used by virtually any organization at any level.

More information may lead to more complexity, but with predictive analytics and dashboard technology, businesses can turn that information into knowledge and can leverage those insights to make better decisions.

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