Descriptive, predictive, and prescriptive: three types of business analytics (2023)

You'd be hard-pressed to find a company today that doesn't use analytics in some form to inform business decisions and measure performance.

(Video) Types of Business Analytics (Descriptive, Predictive, and Prescriptive)

Global spending on big data analytics solutions is projected to beworth over $274.3 billion by 2022– and it's not just big corporations that are investing. Research shows thatnearly 70% of small businesses spend more than $10,000 a yearin analytics to help them better understand their customers, markets, and business processes.

The overwhelming majority of executives say that their organization hasachieved successful results from Big Data and AI. Data can also have a big impact on your bottom line, as companies take advantage of big data.increasing your profits by an average of 8-10%. netflix supposedlysaves $1 billion every yearusing data analytics to improve their customer retention strategies.

So what data analysis methods are companies using to generate these impressive results?

Descriptive, predictive and prescriptive analytics

Business Analytics is the process by which companies usestatistical methods and technologies for data analysisto obtain information and improve their strategic decision making.

Haythree types of analytics companies use to drive their decision making; descriptive analyses, which tell us what has already happened; predictive analytics, which shows us what could happen, and finally prescriptive analytics, which tells us what should happen in the future.

While each of these methods is useful when used individually, they become especially powerful when used together.

(Video) Descriptive, Predictive, & Prescriptive Analytics

descriptive analysis

Descriptive analysis is thehistorical data analysis using two main methods– data aggregation and data mining, which are used to discover trends and patterns. Descriptive analysis is not used to make inferences or predictions about the future from your findings; instead, he is concerned with depicting what happened in the past.

Descriptive analysis is often shown through visual representations of data, such as line, bar, and pie charts, and while they provide useful information on their own, they often act as the basis for further analysis. Because descriptive analysis uses fairly simple analysis techniques, all findings must be easy to understand for the broadest business audience.

For this reason,descriptive analysis forms the core of daily reportsin many businesses. Annual revenue reports are a classic example of descriptive analytics, along with other reports such as inventory, warehousing, and sales data that can be easily aggregated and provide a clear picture of a company's operations. Another widely used example issocial media tools and Google Analytics, which summarize certain groupings based on simple counts of events like clicks and likes.

While descriptive data can be useful for quickly spotting trends and patterns, the analysis has its limitations. Viewed in isolation, descriptive analysis may not provide a complete picture. To learn more, you need to dig deeper.

predictive analytics

Predictive analytics is amost advanced method of data analysiswhich uses probabilities to make assessments of what might happen in the future. Like descriptive analysis, prescriptive analysis uses data extraction; however, it also uses statistical modeling and machine learning techniques to identify the probability of future results based on historical data. To make predictions,machine learning algorithms take existing dataand try to fill in the missing data with the best possible guesses.

These predictions can be used tosolve problems and identify growth opportunities. For example, organizations use predictive analytics to prevent fraud, look for patterns of criminal behavior, optimize their marketing campaigns by identifying cross-selling opportunities, and mitigate risk by using past behavior to predict which customers are most likely to default

(Video) Descriptive vs Predictive vs Prescriptive Analytics

Another branch of predictive analytics is deep learning, which mimics human decision-making processes to make even more sophisticated predictions. For example, by using multiple levels of social and environmental analysis, deep learning is used to predict credit scores more accurately, and in the medical field, it is used to classify digital medical images such as MRIs and X-rays. - lightning strikes, to provide automated prediction for use by clinicians in diagnosing patients.

prescriptive analytics

While predictive analytics shows companies the raw results of their potential actions,prescriptive analytics shows companies which option is best.

The field of prescriptive analyticstakes a lot of math and computer science, using a variety of statistical methods.

While closely related to descriptive and predictive analytics, prescriptive analyticsemphasizes actionable insights about data monitoring. This is accomplished by collecting data from a variety of descriptive and predictive sources and applying it to the decision-making process. Algorithms create and recreate possible decision patterns that can affect an organization in different ways.

What makes prescriptive analytics especially valuable is its ability tomeasure the repercussions of a decision based on different future scenariosand then recommend the best course of action to take to achieve the company's goals.

The business benefit of using prescriptive analytics is huge. It allows teams to visualize the best course of action before making decisions, saving time and money and achieving the best results.

(Video) The three '..tives' of business analytics; predictive, prescriptive and descriptive

Companies that can harness the power of prescriptive analytics are using it in a variety of ways. For example, prescriptive analytics allows you tohealthcare decision makers to optimize business outcomesrecommend the best course of action for patients and providers. They also let financial companies know how muchreduce the cost of a productto attract new customers and keep profits high.

A data-driven future

Despite the clear benefits of using data analytics in decision making, many organizations still lack the skills to optimize it.

Data analysis is a complex discipline.Less than a quarter of companiescurrently describe themselves as data driven and Forbes reports thatalmost all companies cite the need to manage unstructured dataas a problem for your organization.

There is a growing skills gap for business professionals who can manipulate and interpret data.

“The ideal candidate for companies in 2021 and beyond will be someone who can understand and talk about data, because in a few years, data literacy will be something that employers demand and expect. Those who want to get ahead are acquiring these talents now,” says the CEO of ThoughtSpot.Sudheesh Nair.

studying for aOnline Master of Business Analyticsprovides the data analysis and decision skills businesses need to turn big data into insight.

Administered by the EQUIS-accredited Bath School of Business, the Business Analytics online Master's course offers access to the latest data insights, management principles and industry experience, providing essential skills to lead change in today's data-driven industries. data.

(Video) Introduction to the 3 Types of Analytics: Descriptive, Predictive, and Prescriptive

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