What Are Predictive Analytics?
Nearly every modern business on the market takes advantage of the insights that analytics provide. As data science software technology continues to advance rapidly, it can be challenging to keep up. Terms like “advanced predictive analytics” and “actionable insights” may sound foreign to those with no prior knowledge of the advancements in data-driven business intelligence processes. Thankfully, once you’ve grasped the basics of analytical models, business-applicable analytics becomes much easier to understand.
What are analytics?
Analytics use mathematics and big data to make sense of numbers. They help us identify meaningful relationships between sets of data and can even assist us in predicting the outcome of particular scenarios or future events. In reference to business processes, analytics can be highly beneficial in a number of ways.
Business analytics provide the context behind data and statistics that help employees understand which strategies are working well and which need improvement. When applied appropriately, business analytics can even guide executive-level employees through their decision-making processes.
Descriptive analytics interpret historical data. This type of analytics can help reveal the reasoning behind big data changes over time, like dips in revenue or spikes in online traffic. After data mining, this step in the analytical process comes first. Suppose you own a flower shop that experiences a sharp increase in bouquet sales every September. There are no major holidays in September like Valentine’s Day or Mother’s Day, so it may be challenging to determine why this spike occurs and how to prepare for the next one.
Descriptive analytics will comb through historical data that your business has collected over time and aims to explain the driving factors behind changes in bouquet sales. In this particular case, descriptive analytics helps pinpoint the reason behind the increase in sales so that your company can adjust its inventory appropriately to accommodate it.
Predictive analytics solutions use the latest techniques in modeling and statistics to predict future events. Descriptive analytics provides the foundation of historical data interpretations for predictive analytics to build scenarios upon. In other words, predictive analytics can determine how likely data patterns are to emerge again in the future.
Insurance companies utilize this popular method for risk analyses. Many different aspects come into play when you are being evaluated for insurance coverage, like your driving history and demographic. This complex data is run through a predictive analytical model to determine how much risk the insurance company may be taking on by insuring you.
Prescriptive analytics is known for using machine learning to provide businesses with actionable insights. Just like descriptive analytics sets predictive analytics up for success, predictive analytics provide the data that prescriptive analytics uses to draw conclusions. This type of analytics takes data exploration a step further by recommending which course of action may be best for the company to take in a given scenario. Industries that have little room for human mistakes and errors, like the automobile industry or healthcare industry benefit greatly from these analytics techniques.
Every day, your business is generating tons and tons of unstructured data. Ignoring the practical applications of this data can be a big business mistake. Examining your marketing strategies, communicating business needs to shareholders, and gaining deeper insights into your company’s performance all require the help of business analytics.
Overall, analytics can help you streamline business processes and make better business decisions. To learn more about analytics and the impact they may have on your company goals, consider consulting with an expert. Leaders in data science technology like TIBCO provide businesses with the informational resources, products, and services they need to thrive.