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AnswerMiner
February 12, 20194 min read

The Beginner’s Guide to Prescriptive, Descriptive, and Predictive Analytics

Analytics has always been an interesting concept for businesses and organizations across the globe. Businesses are not only able to draw insights through analytics, but it is also possible for them to make informed decisions that are in their best interest. What’s more, using analytics when making decisions for your business is also known to help reduce overhead costs and improve efficiency, maximizing productivity as a whole.

Predictive Analytics

Despite the myriad benefits of analytics, however, it’s unfortunate that not a lot of people truly understand the basics of analytics and how they can actually be used. Additionally, far too many business owners are still in the dark with regards to the different types of analytics and how analytics can be used for the optimization of sales, operation planning, and forecasting.

Interested in learning more about the prescriptive, descriptive, and predictive analytics and how each one of them can be used? Read on to find out as we tell you all about the importance of analytics in business decision making, and how analytics can be used to achieve a variety of objectives.

Descriptive Analytics

Descriptive analytics, as the name suggests, helps businesses and organizations gain visibility of their processes and understand what has really been going on with their data. Descriptive analytics is particularly important for businesses and organizations because it does not only help them understand which efforts have worked in their favor, but also allows them to make amends and tweak their processes to ensure that trajectory remains positive.

Even though descriptive analytics may sound relatively straightforward, there’s a lot that makes this branch of analytics particularly interesting. In most cases, descriptive analytics involves processing raw data in order to draw valuable insights. Since most companies, businesses, and organizations have incredible amounts of raw data available at all times, it is almost always a challenge to decide which of this data is significant and pertinent enough to be processes using descriptive analytics techniques.

And that’s not all.

Since descriptive analytics works on data of the past, this data can also be used to predict behaviors and their effect on outcomes that are not even visible in the near future. With the help of data mining, data aggregation, and other similar techniques, it is also possible for businesses and organizations to gain increased ability to guide decisions.

Regardless of whether or not you need to predict outcomes, behaviors, or trends, descriptive analytics plays a crucial role in summarizing the rate of success or failure of different aspects of your business and provides you with data that is bound to come in handy.

Predictive Analytics

Predictive analytics, as you might have guessed, has more to do with the future. Predictive analytics can be used to predict, with greater accuracy, the likelihood of the occurrence of certain events or outcomes. What’s more, predictive analytics is also known to provide businesses and organizations with insights that are far more actionable than those provided by descriptive analytics, accounting for more efficient and effective forecasting.

Satisfaction level prediction tree

With that said, however, it is imperative for one to remember that there is always going to be a risk associated with predictive analytics. This is primarily because this branch of analytics is based on probability, and 100% certainty cannot be guaranteed even if all factors are taken into consideration at any given point.

The most interesting part about predictive analytics is that it can also be used to fill in gaps of information that you might not be able to access otherwise. Needless to say, when combined with descriptive analytics, predictive analytics can help with extremely efficient forecasting and estimations that will allow the business or organization in question make better decisions. Owing to its nature, predictive analytics can not only be used to identify consumer purchasing trends and sales activities, but also estimate changes in the inventory and supply chain with increased accuracy.

Prescriptive Analytics

Often confused with predictive analytics, prescriptive analytics is a type of analytics that focuses on “prescribing” possible routes and solutions based on certain factors, metrics, and insights. Unlike predictive analytics that deals with a very limited set of information at any given point, prescriptive analytics focuses more on the quantification of future decisions and their effects by laying out all of the possible outcomes to help users get a more clear picture of the situation at hand.

Predictive Analytics

Unlike predictive analytics that just states what will happen in one particular scenario, prescriptive analytics focuses on the different paths and the outcomes that each of these paths will lead to so that users are able to make informed decisions. Being a lot more comprehensive and making use of far more metrics and data than all other types of analytics, prescriptive analytics uses machine learning, algorithms, and computational modeling procedures in order to paint a complete picture. In addition, prescriptive analytics techniques do not only make use of real time data, but also uses big data, and historical feeds for accurate results.

What Are The Benefits Of Using Analytics?

Analytics do not only allow businesses to explore relationships and build patterns, but it is also possible for them to make more data-driven decisions that are in their best interest. What’s more, when using the different types of analytics, it will also be possible for businesses and organizations to forecast more effectively and incorporate automation for optimized results.

Now that you know how the different types of analytics can be use, you can start using analytics to improve business decisions and maximize output.

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