Predictive Analytics and Data Mining: Know the Difference

Today, there is so much information in the world that no human brain can process it. Just imagine, when surfing your favorite social network, you see, not relevant content from friends and interest groups in the feed, but in general, everything that anyone has ever added there. In a word, chaos and confusion! Few people would like this.

To avoid this, companies working with big data use various methods to analyze the video/audio and text content they have so that every consumer of goods or services remains satisfied and active on the site for as long as possible.


These methods include predictive analytics and data mining. They are often confused, considering that they are about the same thing. However, there is a difference, although it can’t be denied that the goal is the same – to lure as many consumers as possible under your commercial umbrella. One comes out of the other.

To explain the difference between data mining and predictive analytics, let’s first talk about each method.


What is Data Mining?

Data Mining is the process of simplifying and generalizing a colossal amount of data in a humanly-understandable way using machine learning technologies. During this process, various clusters of information are discovered, analyzed, sorted, and classified.

Thus, patterns are revealed based on which it is possible to draw certain conclusions and decide what to do next with the results obtained.


Depending on the subtlety of the customization, you can get hyper-precise results that will work for almost every client in a personalized way. According to a Microstrategy report, 92% of respondents plan to roll out advanced analytics capabilities in their organizations.

Data mining is also used in risk management, cybersecurity, and software optimization in addition to forecasting the demand for goods/services and predicting behavioral factors.


What is Predictive Analytics?

Predictive analytics is the process of extracting valuable data from an existing system and then identifying specific trends and tendencies, based on which you can plan further business steps. Then, based on previous experience, future results are modeled by using artificial intelligence and machine learning.

This does not mean a 100% likelihood of events. Still, a high proportion of predictions helps marketers and business analysts navigate which course to lead the company in the near or distant future.


What is the Difference between Data Mining and Predictive Analytics?

Data mining helps organizations build a background and understand the current situation. In addition, predictive analytics is taking on a more proactive role, allowing users to anticipate results and develop preemptive strategies for a wide range of future scenarios while avoiding crises.

Simply put, these are interconnected high-tech processes. Without data mining, predictive analytics could not have appeared in principle since there would be no place to get information for further predictions. And without predictive analytics, data mining would not make much sense either, since the mere presence of structured information, without a further action plan, is not a very useful tool. Data mining illustrates today’s picture, while predictive analytics tells you what to do with it tomorrow.

Thus, data mining turns out to be a stepping stone for predictive analytics. Apart from this, data mining is passive, while predictive analytics is active and can offer a clear picture.


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How Data Mining Works

Imagine that you have gathered three friends and decided which pizza to buy – vegetarian, meat, or fish? You just poll everyone and conclude what exactly needs to be ordered in your favorite pizzeria. But what if, for example, you have three million friends and several hundred varieties of pizza from several dozen establishments? It’s not so easy to deal with an order, is it? Nevertheless, it is what data mining specialists do.



According to this principle, when you go to an online store to buy earrings, you will immediately be offered a bracelet, pendant, and rings to match. And to the swimsuit – a straw hat, sunglasses, and sandals. 

It is precisely the ideally structured array of specific information that make it possible to identify a suspicious declaration of income among millions of others of the same kind.

Data mining is conventionally divided into three stages:

  • Exploration, in which the data is sorted into essential and non-essential (cleaning, data transformation, selection of subsets)
  • A model building or hidden pattern identification, the same datasets are applied to different models, allowing better choices. It is called competitive pricing of models
  • Deployment – the selected data model is used to predict the results


Data mining is handled by highly qualified mathematicians and engineers as well as AI/ML experts.


How Predictive Analytics Works

According to a report by Zion Market Research, the global predictive analytics market was valued at approximately $3.49 billion in 2016 and is expected to reach approximately $10.95 billion by 2022, with a CAGR between 2016 and 2022 at about 21%.

Predictive analytics works with behavioral factors, making it possible to predict customer behavior in the future – how many will come, how many will go, how to change the product, and what promotions to offer to prevent consumer churn.

predictive analysis for big data

You can make predictions based on one person’s behavior or a group united by a specific criterion (gender, age, place of residence, etc.) Predictive analytics uses not only statistics, but ML, teaching itself.

Business analysts interpret forecasts from inferred patterns. If you don’t predict how your regular and hypothetical customers will behave, you will lose the battle with your competitors.


Data Mining and Predictive Analytics in Healthcare

The healthcare system was one of the first to adopt AI technologies, including data mining and predictive analytics. It includes detecting fraud, managing customer relationships, and measuring the effectiveness of specific treatments. And, of course, there is such a massive layer of developments as predictive medicine based on predictive analytics.

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Using the example of the latter, we will explain how it works. Let’s say you have a cancer patient like thousands of other patients in your hospital. Based on their treatment, you decide which regimen to choose for this particular patient, taking into account all of the characteristics. The more patients you add to the database, the more relevant solution will be given by the self-learning application for future patients.

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Another example: you can adjust the number of medical personnel in a hospital depending on the reasons for the visit. If most of the patients who come to you are kids, it’s time to expand the pediatric ward. AI will help the HR department see an impending problem before it becomes urgent. Also, such a system can predict peak loads in hours/days/months of hospital operation, which will make it possible to intelligently plan the shifts of doctors and nurses.


Clustering patients into groups will help assign a patient to a risk group for a particular disease before getting sick. For example, those prone to diabetes or disseminated sclerosis need to stick to diets  so as not to worsen their health. If the patient prepares in advance, the course of the disease will be far less intense and more effectively treated.

But data analysis tools can be helpful not only for doctors. So, a special application can remind the patient that it is time to replenish the supply of prescription drugs, and if necessary, automatically pay for them at the nearest pharmacy and order home delivery.



According to spending data reported by the Centers for Medicare and Medicaid Services, the United States’ national healthcare expenditure reached $ 3.5 trillion in 2017. Applying a 12-17% savings to that number, the estimated cost reduction from system-wide data analytics efforts could earn between $ 420 billion and $ 595 billion.

It would be a crime to ignore such a lucrative market, where supply will not soon outstrip demand. Try trading with The APP Solutions now. Our company has excellent experience in developing health apps.

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Predictive Analytics vs. Machine Learning: What is the Difference

Artificial Intelligence is a compound, highly complex technology with almost unlimited possibilities, including many structural elements and subsets. Each of them is necessary to perform specific tasks, independently or in combination with others. In this article, we will talk about such subsets as predictive analytics and machine learning. We will analyze what they have in common, or different, where they are used and why one does not replace but complements the other.


Predictive Analytics Definition

Predictive analytics forecast the future based on data gathered in the past to find likely patterns and behaviors. It reduces errors by removing the notorious human factor and bringing out important ideas and trends. The term “predictive analytics” refers to an approach, not a specific technology.

How Does it Work?

Techniques used in predictive analytics include descriptive analytics, advanced statistical modeling and mathematics, high-volume data mining, and AI algorithms. For this large volume to be quickly and efficiently analyzed, machine learning is needed.

Predictive analytics is based on prognostication modeling. It is more a scientific niche than a process. Predictive analytics and machine learning go hand in hand since predictive models usually include a machine learning algorithm. These models can be trained over time to respond to new data or values ​​to provide the results your business needs. Predictive modeling has a lot in common with machine learning but is not an identical phenomenon.


Machine Learning Definition

Machine learning is an AI tool that makes it possible to improve forecasting accuracy without additional coding. The machine exercises this by detecting specific patterns in the data clusters. The tool automates predictive modeling by creating training algorithms to find consistency and behavior in data without clearly specifying the search meaning.

How Does it Work?

Machine learning includes drilling algorithms, neural networks, or processing computers to analyze data and automatically output results at the desired scale. Machine learning usually works by combining large amounts of data through iteration and intelligent algorithms, allowing software to automatically learn from patterns or data functions.

Machine learning’s ability to learn from previous datasets and remain flexible allows for various applications, not just predictive modeling.


Predictive Analytics vs. Machine Learning: Similarities

The main similarity between predictive analytics and machine learning can be called a reference to the past to unravel the future. But the significance, approaches, and functions in this process are somewhat different.

Other common criteria include: 

  • the use of an extensive array of data that a person cannot cope with
  • analysis of patterns (albeit in a different way) to determine future results
  • application in the same business sectors: security, finance, retail, medicine, etc.

Even though we present machine learning and predictive analytics as related areas of AI, there are still significantly more differences.

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Predictive Analytics vs. Machine Learning: Difference

Let’s immediately define that predictive analytics and machine learning are different categories of a very generalized concept of AI. Machine learning is a technology that works with complex algorithms and vast amounts of data. At the same time, predictive analysis is research, not a specific technology that existed long before the advent of machine learning; it just made it much more efficient and accurate. 

Simply put, machine learning is a method that has catalyzed progress in the predictive analytics field, while predictive analytics is one of the machine learning applications. There is no problem that predictive analytics can solve, but machine learning cannot.


Benefits and challenges of predictive analytics and machine learning in business

Any AI methods used in business, sooner or later, give tangible results. Therefore, it is only important to understand to what extent these methods and technologies “come to the court” in your case. In some cases, the use of AI pays off relatively quickly; in others, its use is redundant, and the company is neither technically nor “humanly” ready for such a transition to a new level. 

Let’s talk about the pros and cons of machine learning and predictive analytics and some use cases to understand how valuable this tool will be for you and what it has to offer.

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Predictive Analytics and Machine Learning Advantages 


  • Automation of processes and, as a result, saving time and money
  • Improving economic performance through a well-thought-out financial strategy and logistics
  • Getting into the vanguard of a niche due to the ability to foresee the global business trend and understand behavioral factors
  • Technology consolidation, simplifying processes for end-users


Predictive Analytics Disadvantages


  • The need to collect an impressive amount of data to get a relevant forecast
  • You need to keep all trends and patterns that were derived earlier
  • Is guided only by the historical data set, not taking into account current information
  • The unpredictability of human behavior in some aspects can give an inaccurate forecast (for example, if, as a result of an image scandal, the company’s indicators sagged at the moment)


Machine Learning Disadvantages


  • The problem must be very descriptive to find the correct algorithm to apply the solution
  • Big data requirements and training data, such as deep learning data, must be created before this algorithm is actually used
  • resource costs for technology are not always economically feasible


Although there are more disadvantages, the weight of the seemingly small advantages is much higher. We will prove this by briefly describing how and in which business areas both phenomena are used.


Predictive Analytics: when used

Predictive analytics is used to detect trends in behavioral factors across various sites to personalize email advertising messages. Impressive sets of information are collected in a variety of ways, not just online. These can be sensors in retail outlets or store applications, completed questionnaires indicating email, and social networks. All this adds up to the idea of sales forecasting, logistics, and customer experience management.

Predictive analytics works with both people and mechanisms. For example, with its help, you can predict buyer behavior or the growth of a specific disease among certain groups of the population, identify the employees of your company who are thinking about dismissal, or calculate the bank’s clients who are facing bankruptcy soon.

You can predict the wear and tear of equipment or the percentage increase in fraudulent transactions among a series of such bank operations.  

Using machine learning, predictive analysts can work with not only historical data, but also current data.

Data Mining Vs. Predictive Analytics: Know The Difference


Machine Learning: when used

Machine learning is less about reporting than about modeling itself. It is not required to answer human questions.

Examples of using machine learning: 

  • Identifying patterns in marketing research
  • Flagging errors in transactions or data entry
  • Automatic subtitles in videos
  • Personalized shopping experience based on browsing history
  • Signaling anomalies in medical research


Machine learning is a tool, and predictive analytics is a role equipped with tools, one of which is machine learning. These are interacting concepts.

Machine learning algorithms can produce more accurate predictions, generate cleaner data, and enable predictive analytics to run faster and provide deeper insights with less control. Having a solid predictive analysis model and clean data fosters the development of machine learning applications.

To get the most out of predictive analytics and machine learning, organizations need to make sure they have an architecture that supports these solutions and high-quality data to help them learn. They should be centralized, unified, and in a consistent format. In addition, organizations need to know what problems they want to solve as this will help them determine the best and most applicable model to use. This will increase efficiency at all stages of the business. Providing the best practice, The APP Solutions can help with this!

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How Predictive Analytics is Changing Healthcare Industry

The Healthcare industry is experiencing a significant leap forward due to the growing adoption of big data and machine learning algorithms. The tools are becoming more powerful, and the results are becoming more informative. One of the most useful machine learning tools is predictive analytics algorithms.

The steady supply of information feeds the healthcare system. The patient shares his or her well being with the doctor; the doctor gets more data from the machines and equipment; the researchers receive the compiled data from various hospitals, and in turn, can work on creating a treatment that would help the initial patient (and all the others). As such, this is a perfect playground for analytics technology like predictive analytics. 

In this article, we will talk about how predictive analytics can bring healthcare to a new level.

What is Predictive Analytics?

The term “Predictive analytics” describes a methodology of getting an insight into the possible future favorable and adverse events based on the available data and statistical analysis, answering the question “What might happen?”

The purpose of predictive algorithms in healthcare is:

  • To find the correlations in the patient’s data
  • To find associations of the symptoms
  • To find familiar antecedents of the symptoms
  • To explore the impact of different factors (genome structure, clinical variable, et al.) on the course of treatment
  • To examine the possible influence of medical care to past and current diseases


How Predictive Analytics helps in Healthcare

The researchers, as well as doctors, can benefit from predictive analytics to see what can happen. Here is a simplified process: 

Descriptive analytics algorithms are the first to the scene. They take the incoming data from electronic health records and present it in an understandable format. The information includes clinical documentation, claims data, patient surveys, lab tests, and so on – everything that already happened.

The processed information is sorted into various datasets by various criteria (for example, drug reaction dataset and genomics dataset.)

Predictive analytics algorithms start their work. Depending on the goal of the analysis, a predictive algorithm can produce assumptions based either on available data directly from a given patient or general medical data from the public health datasets.

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The assumptions are usually grouped by their probability – from the most likely to the least likely to happen. It’s important to remember that predictions are, in fact, nothing more than assumptions and probabilities. The more data you have, the more accurate and detailed result you will get (like a trend line or high risk score.)

All these insights give a foundation for prescriptive analytics in healthcare, which also calculates probabilities. The difference is that predictive advanced analytics answers the question “What can happen?” and prescriptive analytics answers “What can we do about it?” 


Healthcare Predictive Analytics Examples

Precise Treatment & Personalized Healthcare – Make Better Decisions

Predictive analytics’ most significant contribution to healthcare is personalized and accurate treatment options. 

Getting the treatment strategy right requires going through a lot of data and taking a lot of factors into consideration. In addition to that, the process is time-consuming, which can be detrimental to the treatment as the patient’s condition may worsen in-between the tests and results.

The predictive analytics algorithms can:

  • calculate what can happen (predictive modeling)
  • say what to expect in certain turns of events
  • tell how to map out the treatment of the disease outbreaks.

Even without implementing the prescriptive algorithms, the doctors can use the results from predictive analytics to treat the patient right (especially in cases of rare chronic diseases that the docs did not have enough experience with before.)

Analytics streamline the process – all you technically need is input data and a clear understanding of what are you looking for. 


Efficient Treatment Testing – Reduce Risks

Predictive analytics aren’t directly involved in the treatment testing process, but it is used to cut out the apparent dead ends and streamline the other tasks that will contribute to the treatment. Considering the amount of information to sift through, any functions that can be done automatically simplify the trial runs and reduce potential risks.

Structured patient data is a treasure trove of information. Based on this information, the predictive algorithm can assess how various types of treatments might affect the organism.

The results of the analysis are processed with the assistance of public health datasets and then interpreted as risk factors for the specific scenarios. The criteria are usually symptom-based, time-based, and treatment type-based.

Risk Factor intelligence is a set of filters, which is utilized during treatment testing and scenario simulation. 

When the time comes to select the proper treatment, the elements that don’t fit the Risk Factor filters are eliminated.  

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Disease Control and Management – Avoid Sepsis

Sepsis is when the body starts to attack its own organs and tissues in attempt to fight off the bacteria or other causes. It is one of the most dangerous threats during any course of treatment. According to the recent Sepsis Alliance study, harmful bacterias and toxins in the tissues kill one person every two minutes.

In the case of a septic shock, healthcare workers need to act quickly and understand the patient’s needs and reactions. Predictive algorithms can help to avoid fatal outcomes.

  • Real-time analytics provide medical professionals with a big picture of what is going on with the patient.
  • The incoming information is analyzed to detect any kind of anomalies.
  • In case of any suspicious symptoms, an early warning health systems informs the doctors and they can prevent the condition from harming the patient.

Such applications as DNA Nanopore sequencers can detect pathogens and toxins in the DNA samples and calculate possible courses of action that avoid the mere possibility of sepsis.

Our developers from the APP Solutions have worked alongside medical research and the Google team on a proof of concept for genomics researchers, data scientists, and bioinformatics developers to fight such sepsis danger, using the breadth and depth of Google Cloud. Read the case study on Google Blog

Workflow Optimization – Predict Patient Utilization Patterns

Besides treating patients, predictive analytics can also help to manage the hospital and other medical institutions’ workflows. 

Managing healthcare institutions, especially on the day-to-day operation level, is a significant undertaking. The predictive algorithm can streamline some of its elements and boost the health services’ efficiency by avoiding operational downtime and stalling.

Predictive algorithms can be used to analyze Patient Utilization Patterns. For instance, it can detect the peak highs and lows as well as the weak points of the workflow. Predictive algorithms can also provide a big picture of the working process and its effectiveness. 

On the other hand, predictions can be used to optimize the workflow of various departments:

  • Build an effective schedule that will avoid extreme workload and avoid needless downtime
  • Manage personnel allocation
  • Predict supply chain demands and refill/maintenance schedule

All this can help to flatten the bell curve and even out the workflow of each department (unless we’re talking about ER, where the flow is pretty much unpredictable.)

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Supply Chain Management

Supply chain management is an important integral part of the healthcare workflow. Predictive analytics algorithms in hospital analytics can solve a few issues here:

  • Provide a more in-depth view into the state of the market and its possibilities
  • Give hospital administrative operations management team an opportunity to cut healthcare costs and use supply chain budget more effectively
  • Can help to better utilize the supply chain according to the demands of the healthcare operation

In other words, Predictive Analytics puts things into perspective. A combination of the current trends and history can show what the optimal decision can be in the current situation. 

It is a variation of e-commerce market basket analysis with additional inventory management tools. Predictions are based on associations between the items and their consumption and the results can streamline the workflow. As a result, you get a much more cost-effective operation and much less headache.

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InConclusion – Actionable Insights

The Healthcare industry is bound by the need for the right decision making and the key to this is understanding what the future holds.

Predictive analytics with its handy sets of predictions and estimates provide a competitive advantage and lets you think through the course of action a couple of steps ahead. Predictive analytics help to act instead of reacting. 

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