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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Data Mining: The Definitive Guide to Techniques, Examples, and Challenges

We live in the age of massive data production. If you think about it – pretty much every gadget or service we are using creates a lot of information (for example, Facebook processes around 500+ terabytes of data each day). All this data goes straight back to the product owners, which they can use to make a better product. This process of gathering data and making sense of it is called Data Mining.

However, this process is not as simple as it seems. It is essential to understand the hows, whats, and whys of data mining to use it to its maximum effect.

What is Data Mining?

Data mining is the process of sorting out the data to find something worthwhile. If being exact, mining is what kick-starts the principle “work smarter not harder.”

At a smaller scale, mining is any activity that involves gathering data in one place in some structure. For example, putting together an Excel Spreadsheet or summarizing the main points of some text.

Data mining is all about:

  • processing data;
  • extracting valuable and relevant insights out of it.

Purpose of Data Mining

There are many purposes data mining can be used for. The data can be used for:

  • detecting trends;
  • predicting various outcomes;
  • modeling target audience;
  • gathering information about the product/service use;

Data mining helps to understand certain aspects of customer behavior. This knowledge allows companies to adapt accordingly and offer the best possible services.

 Big Data vs. Data Mining

Difference between Data Mining and Big Data

Let’s put this thing straight:

  • Big Data is the big picture, the “what?” of it all.
  • Data Mining is a close-up on the incoming information – can be summarized as “how?” or “why?”

Now let’s look at the ins and outs of Data Mining operations.

How Does Data Mining Work?

Stage-wise, data mining operation consists of the following elements:

  • Building target datasets by selecting what kind of data you need;
  • Preprocessing is the groundwork for the subsequent operations. This process is also known as data exploration.
  • Preparing the data – a creation of the segmenting rules, cleaning data from noise, handling missing values, performing anomaly checks, and other operations. This stage may also include further data exploration.
  • Actual data mining starts when a combination of machine learning algorithms gets to work.

Data Mining Machine Learning Algorithms

Overall, there are the following types of machine learning algorithms at play:

  • Supervised machine learning algorithms are used for sorting out structured data:
    • Classification is used to generalize known patterns. This is then applied to the new information (for example, to classify email letter as spam);
    • Regression is used to predict certain values (usually prices, temperatures, or rates);
    • Normalization is used to flatten the independent variables of data sets and restructure data into a more cohesive form.
  • Unsupervised machine learning algorithms are used for the exploration of unlabeled data:
    • Clustering is used to detect distinct patterns (AKA groups AKA structures
    • Association rule learning is used to identify the relationship between the variables of the data set. For example, what kind of actions are performed most frequently;
    • Summarization is used for visualization and reporting purposes;
  • Semi-supervised ML algorithms are a combination of the aforementioned methodologies;
  • Neural Networks – these are complex systems used for more intricate operations.

Now let’s take a look at the industries where mining is applied.

Examples of Data Mining

Examples of Data Mining in business

Marketing, eCommerce, Financial Services – Customer Relationship Management

All industries can benefit from CRM systems that are widely used in a variety of industries – from marketing to eCommerce to healthcare and leisure.

The role of data mining in CRM is simple:

  • To get insights that will provide a solid ground for attaining and retaining customers
  • To adapt services according to the ebbs and flows of the user behavior patterns.

Usually, data mining algorithms are used for two purposes:

  • To extract patterns out of data;
  • To prepare predictions regarding certain processes;

Customer Relationship Management relies on processing large quantities of data in order to deliver the best service based on solid facts. Such CRMs as Salesforce and Hubspot are built around it.

The features include:

  • Basket Analysis (tendencies and habits of users);
  • Predictive Analytics
  • Sales forecasting;
  • Audience segmentation;
  • Fraud detection;

eCommerce, Marketing, Banking, Healthcare – Fraud Detection

As it was explained in our Ad Fraud piece, fraud is one of the biggest problems of the Internet. Ad Tech suffers from it, eCommerce is heavily affected, banking is terrorized by it.

However, the implementation of data mining can help to deal with fraudulent activity more efficiently. Some patterns can be spotted and subsequently blocked before causing mayhem, and the application of machine learning algorithms helps this process of detection.

Overall, there are two options:

  • Supervised learning – when the dataset is labeled either “fraud” or “non-fraud” and algorithm trains to identify one and another. In order to make this approach effective, you need a library of fraud patterns specific to your type of system.
  • Unsupervised learning is used to assess actions (ad clicks, payments), which are then compared with the typical scenarios and identified as either fraudulent or not.

Here’s how it works in different industries:

  • In Ad Tech, data mining-based fraud detection is centered around unusual and suspicious behavior patterns. This approach is effective against click and traffic fraud.
  • In Finance, data mining can help expose reporting manipulations via association rules. Also – predictive models can help handle credit card fraud.
  • In Healthcare, data mining can tackle manipulations related to medical insurance fraud.

Marketing, eCommerce – Customer Segmentation

Knowing your target audience is at the center of any business operation. Data mining brings customer segmentation to a completely new level of accuracy and efficiency. Ever wondered how Amazon knows what are you looking for? This is how.

Customer segmentation is equally important for ad tech operation and for eCommerce marketers. Customer’s use of a product or interaction with ad content provides a lot of data. These bits and pieces of data show customers:

  • Interests
  • Tendencies and preferences
  • Needs
  • Habits
  • General behavior patterns

This allows constructing more precise audience segments based on practical aspects instead of relying on demographic elements. Better segmentation leads to better targeting, and this leads to more conversions which is always a good thing.

You can learn more about it in our article about User Modelling.

Healthcare – Research Analysis

The research analysis is probably the most direct use of data mining operations. Overall, this term covers a wide variety of different processes that are related to the exploration of data and identifying its features.

The research analysis is used to develop solutions and construct narratives out of available data. For example, to build a timeline and progression of a disease outbreak.

The role of data mining in this process is simple:

  1. Cleaning the volumes of data;
  2. Processing the datasets;
  3. Adding the results to the big picture.

The critical technique, in this case, is pattern recognition.

The other use of data mining in research analysis is for visualization purposes. In this case, the tools are used to reiterate the available data into more digesting and presentable forms.

eCommerce – Market Basket Analysis

Modern eCommerce marketing is built around studying the behavior of the users. It is used to improve customer experience and make the most out of every customer. In other words, it uses user experience to perpetuate customer experience via extensive data mining.

Market basket analysis is used:

  • To group certain items in specific groups;
  • To target them to the users who happened to be purchasing something out of a particular group.

The other element of the equation is differential analysis. It performs a comparison of specific data segments and defines the most effective option — for example, the lowest price in comparison with other marketplaces.

The result gives an insight into customers’ needs and preferences and allows them to adapt the surrounding service to fit it accordingly.

Business Analytics, Marketing – Forecasting / Predictive Analytics

Understanding what the future holds for your business operation is critical for effective management. It is the key to making the right decisions from a long-term perspective.

That’s what Predictive Analytics are for. Viable forecasts of possible outcomes can be realized through combinations of the supervised and unsupervised algorithm. The methods applied are:

  • Regression analysis;
  • Classification;
  • Clustering;
  • Association rules.

Here’s how it works: there is a selection of factors critical to your operation. Usually, it includes user-related segmentation data plus performance metrics.

These factors are connected with an ad campaign budget and also goal-related metrics. This allows us to calculate a variety of possible outcomes and plan out the campaign in the most effective way.

Business Analytics, HR analytics – Risk Management

The Decision-making process depends on a clear understanding of possible outcomes. Data mining is often used to perform a risk assessment and predict possible outcomes in various scenarios.

In the case of Business Analytics, this provides an additional layer for understanding the possibilities of different options.

In the case of HR Analytics, risk management is used to assess the suitability of the candidates. Usually, this process is built around specific criteria and grading (soft skills, technical skills, etc.)

This operation is carried out by composing decision trees that include various sequences of actions. In addition, there is a selection of outcomes that may occur upon taking them. Combined they present a comprehensive list of pros and cons for every choice.

Decision tree analysis is also used to assess the cost-benefit ratio.

Big Data and Data Mining Statistics 2018

Source: Statista

Data Mining Challenges

The scope of Data Sets

While it might seem obvious for big data, but the fact remains – there is too much data. Databases are getting bigger and it is getting harder to get around them in any kind of comprehensive manner.

There is a critical challenge in handling all this data effectively and the challenge itself is threefold:

  1. Segmenting data – recognizing important elements;
  2. Filtering the noise – leaving out the noise;
  3. Activating data – integrating gathered information into the business operation;

Every aspect of this challenge requires the implementation of different machine learning algorithms.

Privacy & Security

Data Mining operation directly deals with personally identifiable information. Because of that, it is fair to say that privacy and security concerns are a big challenge for Data Mining.

It is easy to understand why. Given the history of recent data breaches – there is certain distrust in any data gathering.

In addition to that, there are strict regulations regarding the use of data in the European Union due to GDPR. They turn the data collection operation on its head. Because of that, it is still unclear how to keep the balance between lawfulness and effectiveness in the data-mining operation.

If you think about it, data mining can be considered a form of surveillance. It deals with information about user behavior, consuming habits, interactions with ad content, and so on. This information can be used both for good and bad things. The difference between mining and surveillance lies in the purpose. The ultimate goal of data mining is to make a better customer experience.

Because of that, it is important to keep all the gathered information safe:

  • from being stolen;
  • from being altered or modified;
  • from being accessed without permission.

In order to do that, the following methods are recommended:

  • Encryption mechanisms;
  • Different levels of access;
  • Consistent network security audits;
  • Personal responsibility and clearly defined consequences of the perpetration.

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Data Training Set

To provide a desirable level of efficiency of the algorithm – a training data set must be adequate for the cause. However, that is easier said than done.

There are several reasons for that:

  • Dataset is not representative. A good example of this can be rules for diagnosing patients. There must be a wide selection of use cases with different combinations in order to provide the required flexibility. If the rules are based on diagnosing children, the algorithm’s application to adults will be ineffective.
  • Boundary cases are lacking. Boundary case means detailed distinction of what is one thing and what is the other. For example, the difference between a table and a chair. In order to differentiate them, the system needs to have a set of properties for both. In addition to that, there must be a list of exceptions.
  • Not enough information. In order to attain efficiency, a data mining algorithm needs clearly defined and detailed classes and conditions of objects. Vague descriptions or generalized classification can lead to a significant mess in the data. For example, a definitive set of features that differentiate a dog from a cat. If the attributes are too vague – both will simply end up in the “mammal” category.

Data Accuracy

The other big challenge of data mining is the accuracy of the data itself. In order to be considered worthwhile, gathered data needs to be:

  • complete;
  • accurate;
  • reliable.

These factors contribute to the decision making process.

There are algorithms designed to keep it intact. In the end, the whole thing depends on your understanding of what kind of information you for which kind of operations. This will keep the focus on the essentials.

Data Noise

One of the biggest challenges that come while dealing with Big Data and Data Mining, in particular, is noise.

Data Noise is all the stuff that provides no value for the business operation. As such it must be filtered out so that the primary effort would be concentrated on the valuable data.

To understand what is noise in your case – you need to define what kind of information you need clearly, which forms a basis for the filtering algorithms.

In addition to that, there are two more things to deal with:

  • Corrupted attribute values
  • Missing attribute values

The thing with both is that these factors affect the quality of the results. Whether it is a prediction or segmenting – the abundance of noise can throw a wrench into an operation.

In case of corrupted values – it all depends on the accuracy of the established rules and the training set. The corrupted values come from inaccuracies in the training set that subsequently cause errors in the actual mining operation. At the same time, values that are worthwhile may be considered as noise and filtered out.

There are times when the attribute values can be missing from the training set and, while the information is there, it might get ignored by the mining algorithm due to being unrecognized. 

Both of these issues are handled by unsupervised machine learning algorithms that perform routine checks and reclassifications of the datasets.

What’s Next?

Data Mining is one of the pieces for the bigger picture that can be attained by working with Big Data. It is one of the fundamental techniques of modern business operation. It provides the material that makes possible productive work.

As such, its approaches are continually evolving and getting more efficient in digging out the insights. It is fascinating to see where technology is going.

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What is Data Wrangling? Steps, Solutions, and Tools

These days almost anything can be a valuable source of information. The primary challenge lies in extracting the insights from the said information and make sense of it, which is the point of Big Data. However, you also need to prep the data at first, and that is Data Wrangling in a nutshell.

The nature of the information is that it requires a certain kind of organization to be adequately assessed. This process requires a crystal clear understanding of which operations need what sort of data.

Let’s look closer at wrangled data and explain why it is so important.

What Is Data Wrangling? Data Wrangling Definition

Data Wrangling process (also known as Data Munging) is the process of transforming data from its original “raw” form into a more digestible format and organizing sets from various sources into a singular coherent whole for further processing.

What is “raw data”? It is any repository data (texts, images, database records) that is documented but yet to be processed and fully integrated into the system.

The process of wrangling can be described as “digesting” data (often referred to as “munging” thus the alternative term “data wrangling techniques”) and making it useful (aka usable) for the system. It can be described as a preparation stage for every other data-related operation.

Wrangling the data is usually accompanied by Mapping. The term “Data Mapping” refers to the element of the wrangling process that involves identifying source data fields to their respective target data fields. While Wrangling is dedicated to transforming data, Mapping is about connecting the dots between different elements.

What is the Purpose of Data Wrangling?

The primary purpose of data wrangling can be described as getting data in coherent shape. In other words, it is making raw data usable. It provides the substance for further proceedings.

As such, Data Wrangling acts as a preparation stage for the data-mining process. Process-wise these two operations are coupled together as you can’t do one without another.

Overall, data wrangling covers the following processes:

  • Getting data from the various source into one place
  • Piecing the data together according to the determined setting
  • Cleaning the data from the noise or erroneous, missing elements

It should be noted that Data Wrangling is a somewhat demanding and time-consuming operation both from computational capacities and human resources. Data wrangling takes over half of what data scientist does.

On the upside, the direct result of this profound — data wrangling that’s done right makes a solid foundation for further data processing.

Data Wrangling Steps

Data Wrangling is one of those technical terms that are more or less self-descriptive. The term “wrangling” refers to rounding up information in a certain way.

This operation includes a sequence of the following processes:

  1. Preprocessing — the initial state that occurs right after the acquiring of data.
  2. Standardizing data into an understandable format. For example, you have a user profile events record, and you need to sort it by types of events and time stamps;
  3. Cleaning data from noise, missing, or erroneous elements.
  4. Consolidating data from various sources or data sets into a coherent whole. For example, you have an affiliate advertising network, and you need to gather performance statistics for the current stage of the marketing campaign;
  5. Matching data with the existing data sets. For example, you already have user data for a certain period and unite these sets into a more expansive one;
  6. Filtering data through determined settings for the processing.

Data Wrangling Machine Learning Algorithms

Overall, there are the following types of machine learning algorithms at play:

  • Supervised ML algorithms are used for standardizing and consolidating disparate data sources:
    • Classification is used to identify known patterns;
    • Normalization is used to flatten the independent variables of data sets and restructure data into a more cohesive form.
  • Unsupervised ML algorithms are used for the exploration of unlabeled data:


How Data Wrangling solves major Big Data / Machine Learning challenges?

Data Exploration

The most fundamental result of data mapping in the data processing operation is exploratory. It allows you to understand what kind of data you have and what you can do with it.

While it seems rather apparent — more often than not this stage is skewed for the sake of seemingly more efficient manual approaches.

Unfortunately, these approaches often leave out and miss a lot of valuable insights into the nature and the structure of data. In the end, you will be forced to redo the thing properly to make possible further data processing operations.

Automated Data Wrangling goes through data in more ways and presents many more insights that can be worthwhile for business operation.


Unified and Structured Data

It is fair to say that data always comes in as a glorious mess in different shapes and forms. While you may have a semblance of comprehension of “what it is” and “what it is for” — data, as it is in its original form, raw data is mostly useless if it is not organized correctly beforehand.

Data Wrangling and subsequent Mapping segments and frames data sets in a way that would best serve its purpose of use. This makes datasets freely available for extracting any insights for any emerging task.

On the other hand, clearly structured data allows combining multiple data sets and gradually evolve the system into more effective.

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Data Clean-up from Noise / Errors / Missing Information

Noise, errors, and missing values are common things in any data set. There are numerous reasons for that:

  • Human error (so-called soapy eye);
  • Accidental Mislabeling;
  • Technical glitches;

Its impact on the quality of the data processing operation is well-known — it leads to poorer quality of results and subsequently less effective business operation. For the machine learning algorithms noisy, inconsistent data is even worse. If the algorithm is trained in such datasets — it can be rendered useless for its purposes.

This is why data wrangling is there to right the wrongs and make everything the way it was supposed to be.

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In the context of data cleaning, wrangling is doing the following operations:

  • Data audit — anomaly and error/contradiction detection through statistical and database approaches.
  • Workflow specification and execution — the causes of anomalies and errors are analyzed. After specifying their origin and effect in the context of the specific workflow — the element is then corrected or removed from the data set.
  • Post-processing control — after implementing the clean-up — the results of the cleaned workflow are reassessed. In case if there are further complications — a new cycle of cleaning may occur.

Minimized Data Leakage

Data Leakage is often considered one of the biggest challenges of Machine Learning. And since ML algorithms are used for data processing — the threat grows exponentially. The thing is — prediction relies on the accurateness of data. And if the calculated prediction is based on uncertain data — this prediction is as good as a wild guesstimation.

What is Data Leakage? The term refers to instances when the training of the predictive model uses data outside of the training data set. So-called “outside data” can be anything unverified or unlabeled for the model training.

The direct result of this is an inaccurate algorithm that provides you with incorrect predictions that can seriously affect your business operation.

Why does it happen? The usual cause is a messy structure of the data with no clear border signifiers where is what and what is for what. The most common type of data leakage is when data from the test set bleeds into the training data set.  

Extended Data Wrangling and Data Mapping practices can help to minimize its possibility and subsequently neuter its impact.

Data Wrangling Tools

Basic Data Munging Tools

Data Wrangling in Python

  1. Numpy (aka Numerical Python) — the most basic package. Lots of features for operations on n-arrays and matrices in Python. The library provides vectorization of mathematical operations on the NumPy array type, which improves performance and accordingly speeds up the execution.
  2. Pandas — designed for fast and easy data analysis operations. Useful for data structures with labeled axes. Explicit data alignment prevents common errors that result from misaligned data coming in from different sources.
  3. Matplotlib — Python visualization module. Good for line graphs, pie charts, histograms, and other professional grade figures.
  4. Plotly — for interactive, publication-quality graphs. Excellent for line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axis, polar graphs, and bubble charts.
  5. Theano — library for numerical computation similar to Numpy. This library is designed to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Data Wrangling in R

  1. Dplyr – essential data-munging R package. Supreme data framing tool. Especially useful for data management operating by categories.
  2. Purrr – good for list function operations and error-checking.
  3. Splitstackshape – an oldie but goldie. Good for shaping complex data sets and simplifying the visualization.
  4. JSOnline – nice and easy parsing tool.
  5. Magrittr – good for wrangling scattered sets and putting them into a more coherent form.


Staying on your path in the forest of information requires a lot of concentration and effort. However, with the help of machine learning algorithms, the process becomes a lot simpler and manageable. 

When you gain insights and make your business decisions based on them, you gain a competitive advantage over other businesses in your industry. Yet, it doesn’t work without doing the homework first and that’s why you need data wrangling processes in place. 

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Benefits and Challenges of Big Data in Customer Analytics

“The world is now awash in data, and we can see consumers in a lot clearer ways,” said Max Levchin, PayPal co-founder.

Simply gather data, however, doesn’t bring any benefits, it’s the decision-making and analytics skills that help to survive in the modern business landscape. It’s not something new, but we need to know how to construct engaging customer service using the information we have at hand. Here’s where Big Data analytics becomes a solution. 

These days, the term Big Data is thrown around so much it seems like it is a “one-size-fits-all” solution. The reality is a bit different, but the fact remains the same — to provide well-oiled and effective customer service, adding a data analytics solution to the mix can be a decisive factor.

What is Big Data and how big is Big Data?

Big Data is extra-large amounts of information that require specialized solutions to gather, process, analyze, and store it to use in business operations. 

Machine learning algorithms help to increase the efficiency and insightfulness of the data that is gathered (but more on that a bit later.)

Four Vs of Big Data describe the components:

  • Volume — the amount of data
  • Velocity — the speed of processing data
  • Variety — kinds of data you can collect and process
  • Veracity — quality, and consistency of data

[Source: IBM Blog]

How big is Big Data? According to the IDC forecast, the Global Datasphere will grow to 175 Zettabytes by 2025 (compared to 33 Zettabytes in 2018.) In case you’re wondering what a zettabyte is, it equals a trillion gigabytes. IDC says that if you store the entire Global Datasphere on DVDs, then you’d be able to get a stack of DVDs that would get you to the Moon 23 times or circle the Earth 222 times. 

Speaking regarding single Big Data projects, the amounts are much smaller. A software product or project passes the threshold of Big Data once they have over a terabyte of data.

Class Size Manage with
Small < 10 Gb Excel, R
Medium 10 GB – 1 TB Indexed files, monolithic databases
Big > 1 TB Hadoop, cloud, distributed databases

Now let’s look at how Big Data fits into Customer Services.

Big Data Solutions for Customer Experience

Data is everything in the context of providing Customer Experience (through CRMs and the likes), and it builds the foundation of the business operations, providing vital resources.

Every bit of information is a piece of a puzzle – the more pieces you have, the better understanding of the current market situation and the target audience you have. As a result, you can make decisions that will bring you better results, and this is the underlying motivation behind transitioning to Big Data Operation.

Let’s look at what Big Data brings to the Customer Experience.

Big Data Customer Analytics — Deeper Understanding of the Customer

The most obvious contribution of Big Data to the business operation is a much broader and more diverse understanding of the target audience and the ways the product or services can be presented to them most effectively.

The contribution is twofold:

  1. First, you get a thorough segmentation of the target audience;
  2. Then you get a sentiment analysis of how the product is perceived and interacted with by different segments.

Essentially, big data provides you with a variety of points of view on how the product is and can be perceived, which opens the door to many possibilities of presenting the product or service to the customer in the most effective manner according to the tendencies of the specific segment.

Here’s how it works. You start by gathering information from the relevant data sources, such as:

  • Your website;
  • Your mobile and web applications (if available);
  • Marketing campaigns;
  • Affiliate sources.

The data gets prepared for the mining process and, once processed, it can offer insights on how people use your product or service and highlight the issues. Based on this information, business owners and decision-makers can decide how to target the product with more relevant messaging and address the areas for improvement. 

The best example of putting customer analytics to use is Amazon. They are using it to manage the entire product inventory around the customer based on the initial data entered and then adapting the recommendations according to the expressed preferences.

Sentiment Analysis — Improved Customer Relationship

The purpose of sentiment analysis in customer service is simple — to give you an understanding of how the product is perceived by different users in the form of patterns. This understanding lays a foundation for the further adjustment of the presentation and subsequently more precise targeting of the marketing effort.

Businesses can apply sentiment analysis in a variety of ways. For example:

  • A study of interaction with the support team. This may involve semantic analysis of the responses or more manual filling-in of the questionnaire regarding an instance of the particular user.
  • An interpretation of the product use via performance statistics. This way, pattern recognition algorithms provide you with hints at which parts of the product are working and which require some improvements.

For example, Twitter shows a lot of information regarding the ways various audience segments interact and discuss certain brands. Based on this information, the company can seriously adjust their targeting and strike right in the center.

All in all, sentiment analysis can help with predicting user intent and managing the targeting around it.

Read our article: Why Business Applies Sentiment Analysis

Unified User Models – Single Customer Relationship Across the Platforms – Cross-Platform Marketing

Another good thing about collecting a lot of data is that you can merge different sets from various platforms into the unified whole and get a more in-depth picture of how a given user interacts with your product via multiple platforms.

One of the ways to unify user modeling is through matching credentials. Every user gets the spot in the database and when the new information from the new platform comes in is added to the mix thus you are can adjust targeting accordingly.

This is especially important in the case of eCommerce and content-oriented ventures. The majority of modern CRM’s got this feature in their bags. 

Superior Decision-Making

Knowing what are you doing and understanding when is the best time to take action are integral elements of the decision-making process. These things depend on the accurateness of the available information and its flexibility regarding the application.

In the context of customer relationship management (via platforms like Salesforce or Hubspot), the decision-making process is based on available information. The role of Big Data, in this case, is to augment the foundation and strengthen the process from multiple standpoints.

Here’s what big data brings to the table:

  1. Diverse data from many sources (first-party & third-party)
  2. Real-time streaming statistics
  3. Ability to predict possible outcomes
  4. Ability to calculate the most fitting courses of actions

All this combined gives the company a significant strategic advantage over the competition and allows standing more firmly even in the shake market environment. It enhances the reliability, maintenance, and productivity of the business operation.

Performance Monitoring

With the market and the audience continually evolving, it is essential to keep an eye on what is going on and understand what it means for your business operation. When you have Big Data, the process becomes more natural and more efficient:

  • Modern CRM infrastructure can provide you with real-time analytics from multiple sources merged into one big picture.
  • Using this big picture, you can explore each element of the operation in detail, keeping the interconnectedness in mind. 
  • Based on the available data, you can predict possible outcome scenarios. You can also calculate the best courses of action based on performance and accessible content.

As a direct result, your business profits from adjusted targeting on the go without experiencing excessive losses due to miscalculations. Not all experiments will lead to revenue (because there are people involved, who are unpredictable at times), but you can learn from your wins as well as from your mistakes. 

Diverse Data Analytics

Varied and multi-layered data analytics are another significant contribution to decision-making.

Besides traditional descriptive analytics that shows you what you’ve got, businesses can pay closer attention to the patterns in the data and get:

  • Predictive Analytics, which calculates the probabilities of individual turns of events based on available data.
  • Prescriptive Analytics, which suggests which possible course of action is the best according to available data and possible outcomes.

With these two elements in your mix, you get a powerful tool that gives multiple options and certainty in the decision-making process.


Cost-effectiveness is one of the most biting factors in configuring your customer service. It is a balancing act that is always a challenge to manage. Big Data solutions make the case of making the most out of the existing system and making every bit coming into count.

There are several ways it happens. Let’s look at the most potent:

  1. Reducing operational costs — keeping an operation intact is hard. Process automation and diverse data analytics make it less of a headache and more of an opportunity. This is especially the case for Enterprise Resource Planning systems. Big data solutions allow processing more information more efficiently with less messing around and wasting opportunities.
  2. Reducing marketing costs — automated studies of customer behavior and performance monitoring make the entire marketing operation more efficient in its effort thus minimizing wasted resources.
These benefits don’t mean that big data analytics will be cheap from the start. You need proper architecture, cloud solutions, and many other resources. However, in the long-term, it will pay off. 

Customer Data Analysis Challenges

While the benefits of implementing Big Data Solutions are apparent, there are also a couple of things you need to know before you start doing it.

Let’s look at them one by one.

Viable Use Cases

First and foremost, there is no point in implementing a solution without having a clue why you need it. The thing with Big Data solutions is that they are laser-focused on specific processes. The tools are developed explicitly for certain operations and require accurate adjustment to the system. These are not Swiss army knives — visualizing tools can’t perform a mining operation and vice versa.

To understand how to apply big data to your business, you need to:

  • Define the types of information you need (user data, performance data, sentiment data, etc.)
  • Define what you plan to do with this data (store for operational purposes, implement into marketing operation, adjust the product use)
  • Define tools you would need to do those processes? (Wrangling, mining, visualizing tools, machine learning algorithms, etc.)
  • Define how you will integrate the processed data into your business to make sure you’re not just collecting information, but it is useful.
Without putting the work into the beginning stages, you risk ending up with a solution that would be costly and utterly useless for your business. 

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Because big data is enormous, scalability is one of the primary challenges with this type of solution. If the system runs too slow or unable to go under heavy pressure — you know it’s trouble.

However, this is one of the simpler challenges to solve due to one technology — cloud computing. With the system configured correctly and operating in the cloud, you don’t need to worry about scalability. It is handled by internal autoscaling features and thus uses as much computational capacity as required.

Data Sources

While big data is a technologically complex thing, the main issue is the data itself. The validity and credibility of the data sources are as important as the data coming from them. 

It is one thing when you have your sources and know for sure from where the data is coming. The same thing can be said about well-mannered affiliate sources. However, when it comes to third-party data — you need to be cautious about the possibility of not getting what you need.

In practice, it means that you need to know and trust those who sell you information by checking the background, the credibility of the source, and its data before setting up the exchange.

Data Storage

Storing data is another biting issue related to Big Data Operation. The question is not as much “Where to store data?” as “How to store data?” and there are many things you need to sort out beforehand.

Data processing operation requires large quantities of data being stored and processed in a short amount of time. The storage itself can be rather costly, but there are several options to choose from and different types of data for each:

  1. Google Cloud Storage — for backup purposes
  2. Google DataStore — for key-value search
  3. BigQuery — for big data analytics
This solution is not the only one available but this is what we use at the APP Solutions, and it works great. 


In many ways, Big Data is a saving grace for customer services. The sheer quantity of available data brims with potentially game-changing insights and more efficient working processes.

Discuss with your marketing department what types of information they would like and think of the ways how to get that user data from your customers to make their journey more pleasurable and customized to their likes. And may big data analytics and processing help you along the way.

Need a team with expertise in Big Data Analytics? 

Contact us

PODCAST #18. AI’s Influence in Virtual Healthcare and How Product Managers Can Help in the Revolution

In this Careminds podcast episode, our conversation with Ran Shaul, Chief Product Officer and Co-Founder of K Health and Hydrogen Health, explores virtual healthcare and the influence of AI on patient experiences.

The discussion extends to data-driven decision-making, entrepreneurship within the healthcare sector, and Ran’s unique perspective on the central role product managers play in health tech.

How to Know When a Career Path Makes Sense

After a late start in his career post a five-year service in the Israeli Army, Ran pursued industrial engineering and computer science in Israel, driven by a passion for data science. Upon graduation, he used his skills to tackle complex problems using data, with a particular fascination for employing mathematics in business contexts.

“That’s really the theme of everything I’m passionate about. I don’t know why I’m attracted to the concept of using mathematics to solve business problems.”

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

This led him to start his first business after only a few years of experience in a company working with data warehouses in the early days, which involved managing large databases and local machines before the advent of the cloud. This step into entrepreneurship was motivated not just by a desire for creative freedom, but also by a conviction that data science was poised to become highly influential. This conviction proved true as Ran navigated the growing fields of data mining and natural language processing.

Ran started three companies in total, with the first one being in the health sector. The other two were either acquired or sold, and his focus eventually settled on a company he had founded 6.5 years prior. This company represented a matured perspective in entrepreneurship and offered the chance to tackle a significant problem.

Driven by personal experiences with healthcare and a desire to contribute to something mission-driven, Ran aimed to use data to empower people to make better decisions, particularly in the field of medicine. Six years prior, accurate online medical information was scant and he saw potential in creating an online system for medical advice that was as easily accessible as booking a flight or finding a restaurant.

When asked about the nature of his company, K Health, Ran explains that it’s an AI company, a virtual company, and a doctor’s clinic all in one. Traditional doctor visits often have negative expectations, including long wait times, short consultations, and unforeseen costs. K Health aims to alleviate these issues by offering a more flexible and comprehensive experience.

Patients can consult a doctor on their own terms, at any hour of the day. This flexibility caters to those with busy schedules who might only find time for a doctor’s appointment late in the evening. The wait time is minimal, and the consultation is more in-depth as patients can discuss their symptoms at length with an AI before meeting a physician. This enables the physician to understand the patient’s condition quickly and thoroughly.

The company offers multiple modes of consultation, including video and text-based conversations. Unlike traditional doctor visits, their service doesn’t necessarily end after a single consultation. Patients have the freedom to return to the app and continue discussing their condition or ask further questions about their treatment. This fosters a long-term relationship with the physician rather than a series of transactional interactions.

What Does It Take to Align Innovation and Market Perception?

In healthcare, you should adopt an approach that is conservative, avoiding the typical tech mindset of “move fast and break things”. This principle is even more important when navigating the intricacies of healthcare regulations, which often contain gray areas. Despite these challenges, it’s vital to always prioritize safety and adhere strictly to regulations.

On the question of balancing innovation with regulation, especially as patients share their information with an AI, Ran believes that their approach in summarizing a patient’s situation to provide efficient and personalized care is an innovative and useful feature. He indicates that users are in full control of their experiences, which makes this combination of virtual primary care and personalized AI a truly innovative healthcare solution.

For instance, while there are companies who have chosen to adopt a more aggressive approach by prescribing potentially addictive medications online, this might not always be the best course of action. Such decisions should be made with the patient’s health and safety in mind. Restrictions to service areas that guarantee high-quality and safe care should be seriously considered.

Now, the medical decision-making process primarily lies in the hands of qualified physicians. As an entrepreneur or a tech professional, one should respect and adhere to these decisions without any judgement or influence. The guiding principle in digital health should always be thinking about the long-term outcome for the patient rather than a fast-paced growth model.

While this approach might not conform to conventional business growth models, in the field of healthcare, patient outcomes should always take precedence. It’s important to steer clear of cases that might jeopardize patient safety or the reputation of digital healthcare. By considering these aspects carefully, one can successfully navigate the complexities of designing user-centric, innovative, and safe healthcare solutions.

What Are the Key Challenges in Creating Unreplicated Workflows?

“It’s fine to be an AI company or a virtual clinic individually, but integrating both presents a significant challenge”. 

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

Envious glances might be cast towards AI companies that develop an algorithm and simply provide an API for use, or services that offer “doctor in a box” solutions via video call. However, without a connection between the two, real change can’t occur.

So how do you apply AI safely for the benefit of physicians and patients within a clinical care environment? It’s not just about building an AI system that’s accurate and continually learning, but also about making it understandable for patients and beneficial for physicians.

Often, questions arise about how such an accurate machine was built, one that knows everything about primary care conditions and can diagnose people. However, the main question isn’t just about how it was built, but also about how it’s explained to patients. How do patients understand what the results actually mean? How are these results handed over to physicians? And how is the experience continued such that when a patient has consulted with the AI, the physician has the ability to seamlessly take over and make the actual medical decision?

These considerations represent the major challenge. In the end, the service needs to be something people enjoy using and are satisfied with. It’s a blend of art and science, requiring a combination of different domains. A meeting at a company like this could involve five different domains in the same room: physicians, engineers, mathematicians, regulatory and operational experts, and product designers.

The second part of the challenge is how to build an accurate algorithm. This is where reinforcement learning comes in. Regardless of how simplistic the initial iteration might be, if the model is trained rapidly enough and consistently given feedback about its performance, it will learn and deliver the desired results over time. This concept of a machine constantly learning from humans, a continuous loop of diagnosis, feedback, and improvement, is at the core of the AI’s development and refinement.

These two aspects – multidisciplinary collaboration and constant machine learning – are instrumental in overcoming the challenges that come with blending AI and healthcare in an effective and meaningful way.

How to Define Product Success in Your Organization

“If you have people using the product and come back for more, that is when you know, you have a good product in the market.”

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

Reflecting on leadership style and how it has evolved over the years, there is a need to balance personal opinions and passion with the success of the company. In the early stages, when the company is small, you might be doing a little bit of everything. However, when the company grows – as it did during the COVID-19 pandemic from a 50-person company to a 300-person company – the need for vision and leadership becomes more pronounced.

Using techniques like providing hints rather than direct instructions and allowing people to discover things themselves can be very effective in larger settings. As the company grows, the leadership role becomes more about providing vision and inspiration rather than direct, hands-on guidance.

The establishment of a strong leadership layer is critical to the impact and success of the company. This strong leadership group, composed of leaders in different domains, has the ability to execute efficiently and effectively. Creating alignment with this group is key. It’s important to maintain the right to go into the details – to look at the code, the algorithms, the design – but to do it in a consultative way rather than authoritative, to avoid disrupting the work of others.

Maintaining a strong leadership team at the top, ensuring they have the capacity and willingness to execute, while occasionally diving into the lower levels to get your hands dirty, is vital. It’s a balance of leading by example and supporting those executing the work.

Tough Jobs, Tougher Candidates: The Ideal Profile for a Product Manager

“You need to have a belief, you need to have a vision. They need to be able to basically say no to the naysayers and say no.”

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

Ultimately, someone needs to connect the dots. There’s a necessity for someone to sit in a room, hear all the arguments from various sides, and then stitch it all together. This task is complicated because product managers may not have a background in medicine, nor might they fully understand all the regulatory aspects of their decisions. Despite this, they suddenly need to merge data science, the accuracy of algorithms, and the provision of high-quality clinical care. This makes the role of a product manager incredibly complex, given that they likely aren’t a data scientist nor a physician.

There are two dimensions that are important here: curiosity and the ability to make decisions. Surprisingly, many people prefer to stick to what they know. If they’ve worked in an e-commerce company, for instance, they might be comfortable with selling a new product using the same basic user funnel principles. However, the role here requires learning new domains, understanding the considerations of a physician, the considerations of an algorithm, and integrating those. This requires an eagerness to learn, to read and to understand beyond what one already knows.

The second dimension is decision-making and trade-offs. There’s rarely a perfect solution or an exact minimum viable product (MVP) in every aspect. So, you have to make decisions and execute them in such a way that you’re making small progress with each step. It’s not about one or two decisions; it’s about thousands of micro-decisions that build the big picture and result in a cohesive product. This combination of curiosity and trade-off handling makes for a very strong product manager or product owner.

How Often Do Product Managers Influence the Company’s Vision?

“A product manager needs to kind of ignore the noise and follow the data and, but that’s the task when you actually have a running product with your own data.”

Ran Shaul – Chief Product Officer and Co-Founder of K Health and Hydrogen Health

It can be challenging to know which feature to implement, and sometimes you have to rely on A/B testing and observing what works. This requires a product manager to cut through the noise and follow the data. However, this mainly applies when you already have a running product with your own data.

The situation changes when you don’t have this data, for instance, when you want to start a completely new feature or even a new company. While surveys can provide some feedback, consumers may not be as good at giving feedback for a product that doesn’t exist yet. It’s difficult for consumers to envision using a product that doesn’t exist.

In these situations, the product manager needs to rely more on gut feeling, belief, and vision. They need to have the courage to say no to the naysayers and to believe that they are innovating something that people will want to use. This is where many interesting things happen and where new features are born.

For instance, with K, we didn’t initially know if people would be interested in a single screen showing them a differential diagnosis. Some suggested that people wouldn’t want this feature and that it would only confuse them. However, we went ahead, implemented that screen, and iterated around it. It turned out to be a moment of success, with users spending four minutes answering questions just to know what K thinks about their condition. This was despite initial feedback that people wouldn’t want to spend that much time providing information.

So, the toughest part of being a product manager is to break through the “nos”, follow your vision, and build something that you believe people will like. Then, you put it in their hands and see how they respond. Despite the rules and guidelines, sometimes you need to see past them, invent new things, and rethink the existing order.


In conclusion, if you have a good idea, just go ahead and do it. While gaining experience in big companies and working in different environments is valuable, there’s something uniquely rewarding about pursuing your own idea. Entrepreneurship and leadership aren’t for everyone, but if you enjoy the excitement and have something you want to pursue, go ahead and do it. Put it out there.

The key points are thus:

  • Passion, persistence and the right skills can create meaningful entrepreneurship ventures, even in complex fields like healthcare.
  • The integration of data science, AI and real-world medical expertise is key to providing a more accessible and efficient healthcare service.
  • Regulatory compliance, safety, and patient-first approach are paramount in navigating the challenges of digital healthcare innovation.
  • Success in health-tech depends on multidisciplinary collaboration and constant machine learning, aiming for a blend of accuracy, transparency, and patient-physician interaction.
  • The role of a product manager in this setting is multifaceted, requiring curiosity, sound decision-making, and the ability to navigate both familiar and unfamiliar terrains.








The APP Solutions launched a podcast, CareMinds, where you can hear from respected experts in healthcare and Health Tech.

Who is a successful product manager in the healthcare domain? Which skills and qualities are crucial? How important is this role in moving a successful business to new achievements? Responsibilities and KPIs?

Please find out about all this and more in our podcast. Stay tuned for updates and subscribe to channels.

Listen to our podcast to get some useful tips on your next startup.

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