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Today, it's difficult to find a company that doesn't employ analytics in some way to guide business decisions and monitor success. Businesses utilize analytics to investigate and analyze their data, then turn their discoveries into insights that enable executives, managers, and operational staff make better, more educated business decisions.
Data is becoming increasingly available and present in the day-to-day operations of today's enterprises. With data comes analytics, and firms must explore multiple analytic solutions to determine which will help them to get the most out of their data in order to drive more effective decision-making. Different sorts, types, and stages of data analysis have emerged as a result of the big data revolution. Data analytics is booming in boardrooms around the country, promising enterprise-wide answers for commercial success.
Big data analytics enables organizations to better understand their customers' needs and preferences, allowing them to expand their client base and retain existing customers through personalized and relevant product or service offerings. According to IDC, the big data and analytics market is expected to grow at a 26.4 percent compound annual growth rate (CAGR) to $41.5 billion by the end of 2018.
Many businesses are unsure where to start, what forms of analytics will help them expand, or what these various types of analytics represent. Let's take a look at the many sorts of analytics and the value they add to every business.
These are the types of analytics that businesses use to guide their decision-making:
Descriptive analytics, which tell us what has already happened;
Diagnostic analytics, which explains why something happened in the past;
Predictive analytics, which predicts what might happen; and
finally, Prescriptive analytics, which tells us what should happen in the future.
Descriptive Analytics
Descriptive analytics uses statistics to look at data in order to figure out what happened in the past. This helps stakeholders interpret data and understand a company's performance by offering context. This is the most fundamental sort of data analytics, depending on basic math and statistical tools such as arithmetic, averages, and percent (%) changes rather than the more intricate computations required by predictive and prescriptive analytics. Graphs, charts, reports, and dashboards are examples of data visualizations that can – and should – be easily understood by a broad business audience.
The results that a firm receives from the web server using Google Analytics tools are the best example of descriptive analytics. The results aid in determining what actually occurred in the past and determining if a promotional effort was effective or not based on simple metrics such as page views.
While descriptive data can be valuable for spotting trends and patterns rapidly, the analysis is not without flaws. Descriptive analytics, when viewed in isolation, may not provide the complete picture. You'll need to dig deeper for additional information.
Diagnostic Analytics
Diagnostic analytics goes beyond descriptive data to provide more in-depth analysis in order to answer the question, "Why did this happen?" The term "diagnostic analysis" is frequently interchanged with "root cause analysis." Processes like data discovery, data mining, and drill down and drill through are all examples of this. Businesses utilize this type of analytics to gain a deep understanding of a problem if they have enough data at their disposal. Diagnostic analytics aids in the detection of anomalies and the discovery of haphazard links in data.
For example, eCommerce behemoths like Amazon can break out sales and gross profit per product category, such as Amazon Echo, to determine why they fell short on total profit margins. Diagnostic analytics is also used in healthcare to determine the impact of pharmaceuticals on a specific patient segment when combined with other filters such as diagnosis and prescribed medication. It could, for example, assist you in determining whether all of the patients' symptoms—high fever, dry cough, and fatigue—are caused by the same infectious agent. You now have an explanation for the ER's rapid increase in volume.
Predictive Analytics
Predictive analytics is a more advanced data analysis approach, which use probability to evaluate what might happen in the future. Prescriptive analysis involves data extraction, like descriptive analytics – but uses statistical modeling and machine learning to identify the likelihood of future results based on prior data. In order to predict, machine learning algorithms take known data and try to create the best guesses possible for the missing data.
However, we must remember that no analytics can tell you exactly what WILL will happen in the future. Predictive analytics put forward what MIGHT is, giving the respective probability in view of the variables under consideration. Businesses used predictive analytics to open the conversation into future scenarios that current information may not yet be collected. Analysts can fill in information with predictive analytical information to examine hypotheses using holistic lens rather than merely predicting a single metric or trend.
Predictive analytics uses various statistical and machine learning algorithms to estimate the possibility of a future occurrence, but the accuracy of predictions is not 100 percent because it is reliant on probabilities. Algorithms gather data and fill in the gaps with the best assumptions possible to make predictions.
The results of these types of analytics can subsequently be used to solve problems and find growth possibilities. For example, is being used to avoid fraud by looking for trends in criminal behavior, optimize marketing efforts by recognizing cross-selling opportunities, and reduce risk by predicting which customers are most likely to default on payments based on historical behavior.
Prescriptive Analytics
Predictive analytics offers firms the raw results of their prospective actions, whereas prescriptive analytics shows them the optimum alternative. Prescriptive analytics uses a number of statistical methodologies and draws significantly on mathematics and computer science. Instead of data monitoring, this stresses actionable insights. This goes beyond descriptive analytics' historical insights and predictive analytics' potential future outcomes to provide recommendations for next steps that should be addressed right now.
For example, now that you know the disease is spreading, the prescriptive analytics tool may recommend that you hire more people to handle the influx of patients. Another example is the Aurora Health Care System, which saved $6 million per year by reducing re-admission rates by 10% using prescriptive analytics. In the healthcare industry, prescriptive analytics can be used to improve medication development, find the proper patients for clinical trials, and so on.
Data and several business rules are combined in predictive analytics. External data that may have an impact on a measure being reviewed and impact the business at a departmental or organizational level might be included in the data, in addition to data from the organization's own sources. Preferences, best practices, boundaries, and other constraints are examples of business rules. Natural language processing, machine learning, statistics, operations research, and other mathematical models are examples.
In Summary
Businesses are increasingly turning to data for insights that can help them develop business strategies, make better decisions, and deliver better products, services, and tailored online experiences. While business analytics is a broad field, the potential utility of these many approaches — descriptive, diagnostic, predictive, and prescriptive – is obvious. These many approaches of analysis, when applied together, are tremendously complementary and valuable to a company's success and survival.
"I'm sure I don't have all of the answers or information about types of analytics here." I'm hoping you'll share your thoughts with analytics in the comments area. In the comments, I'd love to hear your thoughts on this.” You can follow to this blog to receive notifications of new posts.
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