How Machine Learning and AI Aid in Proactive Analytics

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    Business Intelligence (BI) solutions have steadily taken the business world by storm. Originating nearly two decades ago, BI has since morphed and evolved numerous times to meet ever-increasing demands for more in-depth analysis, increased speed and easier-to-digest outputs. With each new evolution, new use cases were born, and the resulting business value increased.

    Today, many consider the progression of BI solutions to have developed into three main categories.

    1. Descriptive analytics report on statistical information, i.e., depictions of what has happened in the past.
    2. Predictive analytics, which provide a highly-informed analysis of what is likely to happen in the future based on previous trends discovered by descriptive analytics.
    3. Prescriptive analytics provide not only detail on what you can expect to happen, but also advice on how to handle it.

    Machine learning and AI are largely responsible for the latter two types of analytics.

    Examples of Proactive Analytics

    To gain the most value from a BI solution, businesses should move away from reactive analytics to proactive analytics that offer alerts and real-time insight that aid stakeholders in making better-informed decisions. The following use cases are examples of proactive analytics that are already in effect.

    • Determining which products appeal to a potential customer based on the information they have already been served or what they’re actively searching for online through retail dashboards
    • Predicting repairs and maintenance for machinery
    • Identifying excess product orders that seem unusual for a specific customer
    • Automating merchandising based on a predictive understanding of consumers
    • Creating a maintenance schedule based on historical equipment failures to reduce flight delays
    • Proactively addressing impending equipment malfunctions based on real-time data from IoT sensors
    • Streamlining operations by acknowledging seasonal trends in supply and demand
    • Aiding physicians in making faster and more reliable diagnoses through healthcare dashboards
    • Accelerating the creation and discovery of new medications

    The scenarios above are merely a small example of the seemingly endless ways in which companies can use machine learning enhanced BI to improve the efficiency of business operations and deliver more customized consumer experiences. AI can help businesses make sense of the overwhelming amount of data being delivered.

    As solutions become more complex, so too does the possibility that companies will need to hire more experienced staff that can manage the installation and effectively produce consumable outputs that effectively inform decision-makers. The challenge for BI providers will be to stay at the forefront of cutting-edge AI capabilities while providing new functionality in ways that add value for business users without requiring intervention from highly skilled data analysts or data scientists.

    ClicData invests heavily in the development of its BI tools to ensure that best practices are built into every solution. Our experienced professionals partner with every client to provide advice on how to implement and execute their BI solution to enable the most productive and competitive solution possible. Check out the latest advancements in our business intelligence dashboard features and schedule time with an advisor to learn how your business can benefit.