In our world today, each one of us creates data on a daily basis, whether we know it or not. From the things we buy on Amazon, to the shows and movies we watch on Netflix, we are creating data that companies can collect and analyze in order to recognize trends, make predictions, and examine relationships between variables. We’ve all seen the “Recommended” category on Netflix and the “Frequently Bought Together” category on Amazon, but how did those companies figure out what should be in those categories? It’s all thanks to big data! For example, by analyzing the data from customers who bought hand sanitizer on Amazon, the company was able to find a trend. People who bought hand sanitizer also bought other disinfecting products like wipes and sprays. Another example is Netflix. Netflix gathered tons of data from all over the world and analyzed it to make business decisions. This helped them decide on what programs to buy and create in order to appeal to the largest amount of viewers. Instead of creating a product and then finding an audience, Netflix found their largest audience and is creating a product specifically for them. Read the full Netflix article here.
The Three Types of Analytics : Descriptive, Prescriptive, & Predictive
Every business collects data, the problem is figuring out what to do with it. That’s where the three types of Data Analytics come into play. The first type of analytics that can be done on data is descriptive analytics. This is generally done on historical data and can be used to explain why things happened. This is the most common type of analysis that businesses do and it is helpful to see the past by turning the data into a useful visual. A case study done on companies in the cosmetic market showed how these companies can look at years of historical data to determine how quickly they need to get new products on the market and what trends are starting to occur. By analyzing their historical data, they found that the demand for natural and/or organic cosmetics has been growing. They also found that in order for these new products to do well, they need to be unique and have some sort of competitive advantage over other products.
The second type of analytics is prescriptive analytics. Prescriptive analytics is used for discovering relationships in the data and using that information to make smarter and more informed decisions for the future. This type of analytics is used hand in hand with Artificial Intelligence (AI). By learning from large amounts of data, AI is able to recognize what has happened in the past and use that information to make decisions on new data that it is given. When that additional data becomes available, Prescriptive Analytics can use AI and machine learning to show potential outcomes of certain business decisions and help business owners choose what decision is best for their intended goal. This type of analytics is highly useful in the medical field and our team has extensive experience with it. This article here talks about how prescriptive analytics is the perfect fit for healthcare. It allows for multiple variables in decision making, such as patient groups, treatments, and payments. This data can be used to optimize how the hospital runs and improve the care they provide!
The last type of analytics is Predictive Analytics. Predictive Analytics works with Prescriptive analytics by using historical and new data to predict future outcomes. Insurance companies use Predictive Analytics to determine the likelihood of having to pay for future claims based on past payouts and also the risk of policyholders that are similar. Another use for predictive analytics, similar to prescriptive analytics, is in healthcare. Our team here at Aptus worked with Phoenix Children's Hospital, Neff Power,University of Arizona - College of Medicine , and Ira A. Fulton School of Engineering to develop an algorithm that used pre and post admission factors to produce categorized trauma levels. The problem is that many trauma patients are either over triaged or under triaged. This allows for the proper allocation of resources for each patient by predicting what that patient would need based on past data. To read more about this project, click here.
Another great example comes from a case study with The Journal News. This study talks about how the company was struggling to switch over to a technology based news strategy and how data analytics help them understand their viewers better. While experimenting with live-streaming video, the company collected real time data which showed that viewers were leaving their homepage in waves. In order to understand why, The Journal News was able to analyze their real-time data and find out that the live streaming video was set to autoplay. When their viewers went to their homepage, the video starting playing instantly and basically scared their viewers away.
How Aptus Can Help
Our team here at Aptus can help with all three types of analytics. Our goal is to help companies solve problems that they didn’t even know they had. We can analyze your data and present it to you in a way that makes sense. With so much data being collected, analyzing it can be a daunting task and most people don’t know where to start. By using AI and machine learning, we can help you gain insight on your clients, business data, and sales information. The Industries that we've worked range from defense to healthcare, and aerospace to bio-technology. Interested in learning about companies we've worked with in the past? Take a look at our client testimonial page. Feel free to contact us to learn more about how we can help your company!