Predictive Analytics

The realms of advanced data analytics continues to grow and one interesting spot where it is taking root is in staffing for hospital emergency rooms

What once was done through experience and rules of thumb - like needing more trauma staff on hand for 4th of July fireworks accidents has given way to a more data-driven approach that lets ERs ensure they have the staff they need. 

Predictive analytics tools recognize that data models can better project probabilities and give more accurate information - and not just for hospital staffing. 

Our data scientists, analysts, and strategist use both traditional statistical techniques and more advanced predictive algorithms for predictive analysis to determine the likelihood of future results.

These tools allow our team to work with large amounts of data to find valuable links between causes and consequences, and to make well-founded predictions about currently unavailable data. 

Predictive analysis is closely linked to a number of other major data technologies, including machine learning, artificial intelligence (AI), data mining and prescriptive analysis. Unlike traditional analysts, when predictive analysis is used, it is not known in advance what data is important. 

This type of open-ended information seeking has led to some of our most impactful insights for clients. In once case, we analyzed the sales data for a restaurant chain. Using an advanced AI platform, we were able to see connections between sales activity at specific times of the day and other data points that would have never come to light otherwise. This allowed our team to focus on some interesting behaviors that customers in certain market segments had specifically when they went to the restaurant at lunch. 

In this way, predictive analysis is based on the same or similar methods used in more traditional data analysis, but it adds various forms of machine learning and statistical modeling to detect potential patterns and hidden causes in the raw information. 

Predictive analysis always begins with a business objective: to use data to reduce waste, save time or reduce costs. When we open up the data and analyze it through the specific lens that each project needs, we drive finding insights that make CX and Brand strategy even more valuable. 

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