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Business Management Review | Thursday, March 17, 2022
Businesses use three types of analytics to drive their decision-making; descriptive analytics, which tells us what has already happened; predictive analytics, which shows us what could happen.
FREMONT, CA: Most executives say their organization has achieved successful outcomes from Big Data and AI. Still, data can also impact your bottom line, with businesses that utilize big data increasing their profits by an average of 8-10%.
So, what data analysis methods are businesses utilizing to generate these impressive results?
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Descriptive, predictive, and prescriptive analytics
Business Analytics is the procedure by which businesses use statistical methods and technologies to analyze data to gain insights and enhance their strategic decision-making.
Businesses use three types of analytics to drive their decision-making; descriptive analytics, which tells us what has already happened; predictive analytics, which shows us what could happen. And finally, prescriptive analytics informs us what should happen in the future.
While each method is useful when used individually, they become especially powerful when used together.
Descriptive analytics
Descriptive analytics analyzes historical data using two key methods – data aggregation and mining- to uncover trends and patterns. Descriptive analytics is not utilized to draw inferences or make predictions from its findings; rather, it is involved with representing what has happened in the past.
Descriptive analytics is often displayed using visual data representations like lines, bars, and pie charts. Although they give useful insights, they often form a foundation for future analysis. In addition, because descriptive analytics uses fairly simple analysis techniques, any findings should be easy for the wider business audience to understand.
Hence, descriptive analytics form the core of everyday reporting in many businesses.
While descriptive data can be useful for spotting trends and patterns quickly, the analysis has limitations. Viewed in isolation, descriptive analytics may not provide the full picture.
Predictive analytics
Predictive analytics is a more modern method of data analysis that utilizes probabilities to assess what could happen in the future. Like descriptive analytics, prescriptive analytics employ data mining – however, it also uses statistical modeling and machine learning techniques to identify the possibility of future outcomes as per historical data. To make predictions, machine learning algorithms take present data and try to fill in the missing data with the best possible guesses.
These predictions can then be utilized to solve problems and identify growth opportunities. For example, Organizations are utilizing predictive analytics to prevent fraud by searching for patterns in criminal behavior, optimizing their marketing campaigns by detecting opportunities for cross-selling, and decreasing risk by using past behaviors to predict which customers are most certainly to default on payments.
Another predictive analytics division is deep learning, which mimics human decision-making processes to make even more sophisticated predictions. For instance, through multiple social and environmental analysis levels, deep learning is being utilized to predict credit scores more precisely in the medical sector. In addition, it is being utilized to sort digital medical images like MRI scans and X-rays to provide an automated prediction for doctors to diagnose patients.
Prescriptive analytics
While predictive analytics shows companies the raw results of their potential actions, prescriptive analytics gives companies which option is the best.
The field of prescriptive analytics adopts heavily from mathematics and computer science, using various statistical methods.
Although closely related to descriptive and predictive analytics, prescriptive analytics emphasizes actionable insights instead of data monitoring. This is achieved by gathering data from various descriptive and predictive sources and applying them to decision-making.
Algorithms then create and re-create likely decision patterns that could affect an organization differently.
What makes prescriptive analytics particularly valuable is their ability to measure a decision's repercussions based on different scenarios and then recommend the best action to take to attain a company's goals.
The business benefit of utilizing prescriptive analytics is huge. It lets teams view the best course of action before making decisions, saving time and money while achieving optimal results.
Businesses that can exploit the power of prescriptive analytics use them in various ways. For example, prescriptive analytics enable healthcare decision-makers to optimize business outcomes by recommending the best course of action for patients and providers. They also allow financial companies to know how much to decrease the cost of a product to draw new customers while keeping profits high.
A data-led future
Despite the clear benefits of data analytics in decision-making, many organizations still lack the skills to optimize them.
Data analytics is a complicated discipline. Less than a quarter of businesses present describe themselves as data have driven. However, Forbes reports that nearly all businesses cite the need to manage unstructured data as a problem for their organization.
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