Google Cloud Services for Big Data Projects

Google Cloud Platform provides various services for data analysis and Big Data applications. All those services are integrable with other Google Cloud products, and all of them have their pros and cons. 

This article will review what services Google Cloud Platform can offer for data and Big Data applications and what those services do. We’ll also check out what benefits and limitations they have, the pricing strategy of each service, and their alternatives.

Cloud PubSub

Cloud PubSub is a message queue broker that allows applications to exchange messages reliably, quickly, and asynchronously. Based on the publish-subscription pattern.

Visualization of PubSub workflow

[Visualization of PubSub workflow]

The diagram above describes the basic flow of the PubSub. First, publisher applications publish messages to a PubSub topic. Then the topic sends messages to PubSub subscriptions; the subscriptions store messages; subscriber applications read messages from the subscriptions.


  • A highly reliable communication layer
  • High capacity


  • 10 MB is the maximum size for one message
  • 10 MB is the maximum size for one request, which means if we need to send ten messages per request, the average total length for each notification will be 1 MB.
  • The maximum attribute value size is 1 MB

Pricing strategy

You pay for transferred data per GB.

Analogs & alternatives

  • Apache Kafka
  • RabbitMQ
  • Amazon SQS
  • Azure Service Bus
  • Other Open Source Message Brokers

Google Cloud IoT Core

The architecture of Cloud IoT Core

[The architecture of Cloud IoT Core]

Cloud IoT Core is an IoT devices registry. This service allows devices to connect to the Google Cloud Platform, receive messages from other devices, and send messages to those devices. To receive messages from devices, IoT Core uses Google PubSub.


  • MQTT and HTTPS transfer protocols
  • Secure device connection and management

Pricing Strategy

You pay for the data volume that you transfer across this service.

Analogs & alternatives

  • AWS IoT Core
  • Azure IoT

Cloud Dataproc

Cloud Dataproc for Apache Spark and Apache Hadoop

Cloud Dataproc is a faster, easier, and more cost-effective way to run Apache Spark and Apache Hadoop in Google Cloud. Cloud Dataproc is a cloud-native solution covering all operations related to deploying and managing Spark or Hadoop clusters. 

In simple terms, with Dataproc, you can create a cluster of instances on Google Cloud Platform, dynamically change the size of the cluster, configure it, and run MapReduce jobs.


  • Fast deployment
  • Fully managed service means you need just the right code, no operation work
  • Dynamically resize the cluster
  • Auto-Scaling feature


  • No choice of selecting a specific version of the used framework
  • You cannot pause/stop Data Proc Cluster to save money. Only delete the cluster. It’s possible to do via Cloud Composer
  • You cannot choose a cluster manager, only YARN

Pricing strategy

You pay for each used instance with some extra payment—Google Cloud Platform bills for each minute when the cluster works.

Analogs & alternatives

  • Set-up cluster on virtual machines
  • Amazon EMR
  • Azure HDInsight

Cloud Dataflow

The place of Cloud Dataflow in a Big Data application on Google Cloud Platform

[The place of Cloud Dataflow in a Big Data application on Google Cloud Platform]

Cloud Dataflow is a managed service for developing and executing a wide range of data processing patterns, including ETL, batch, streaming processing, etc. In addition, Dataflow is used for building data pipelines. This service is based on Apache Beam and supports Python and Java jobs.


  • Combines batch and streaming with a single API
  • Speedy deployment
  • A fully managed service, no operation work
  • Dynamic work rebalancing
  • Autoscaling


  • Based on a single solution, therefore, inherits all limitations of Apache Beam
  • The maximum size for a single element value in Streaming Engine is 100 Mb

Pricing strategy

Cloud Dataflow jobs are billed per second, based on the actual use of Cloud Dataflow.

Analogs & alternatives

  • Set-up cluster on virtual machines and run Apache Beam via in-built runner
  • As far as I know, other cloud providers don’t have analogs.

Google Cloud Dataprep

The interface of Dataprep

[The interface of Dataprep]

Dataprep is a tool for visualizing, exploring, and preparing data you work with. You can build pipelines to ETL your data for different storage. And do it on a simple and intelligible web interface.

For example, you can use Dataprep to build the ETL pipeline to extract raw data from GCS, clean up this data, transform it to the needed view, and load it into BigQuery. Also, you can schedule a daily/weekly/etc job that will run this pipeline for new raw data.


  • Simplify building of ETL pipelines
  • Provide a clear and helpful web interface
  • Automate a lot of manual jobs for data engineers
  • Built-in scheduler
  • To perform ETL jobs, Dataprep uses Google Dataflow


  • Works only with BigQuery and GCS

Pricing Strategy

For data storing, you pay for data storage. For executing ETL jobs, you pay for Google Dataflow.

Cloud Composer

Cloud Composer is a workflow orchestration service

Cloud Composer is a workflow orchestration service to manage data processing. Cloud Composer is a cloud interface for Apache Airflow. Composer automates the ETL jobs. One example is to create a Dataproc cluster, perform transformations on extracted data (via a Dataproc PySpark job), upload the results to BigQuery, and then shut down the Dataproc collection.


  • Fills the gaps of other Google Cloud Platform solutions, like Dataproc
  • Inherits all advantages of Apache Airflow


  • Provides the Airflow web UI on a public IP address
  • Inherits all rules of Apache Airflow

Pricing Strategy

You pay only for resources on which Composer is deployed. But the Composer will be deployed to 3 instances.

Analogs & alternatives

  • Custom deployed Apache Airflow
  • Other orchestration open source solution


BigQuery is a data warehouse

[Example of integration BigQuery into a data processing solution with different front-end integrations] 

BigQuery is a data warehouse. BigQuery allows us to store and query massive datasets of up to hundreds of Petabytes. BigQuery is very familiar to relational databases by their structure. It has a table structure, uses SQL, supports batch and streaming writing into the database, and is integrated with all Google Cloud Platform services, including Dataflow, Apache Spark, Apache Hadoop, etc. It’s best for use in interactive queuing and offline analytics.


  • Huge capacity, up to hundreds of Petabytes
  • SQL
  • Batch and streaming writing
  • Support complex queries
  • Built-in ML
  • Serverless
  • Shared datasets — you can share datasets between different projects
  • Global locations
  • All popular data processing tools have interfaces to BigQuery


  • It doesn’t support transactions, but those who need transitions in the OLAP solution
  • The maximum size of the row is 10Mb

Pricing strategy

You pay separately for stored information(for each Gb) and executed queries.

You can choose one of two payment models concerning executed queries, either paying for each processed Terabyte or a stable monthly cost depending on your preferences.

Analogs & alternatives

  • Amazon Redshift
  • Azure Cosmos DB

Cloud BigTable

Google Cloud BigTable is Google's NoSQL Big Data database service

Google Cloud BigTable is Google’s NoSQL Big Data database service. The same database powers many core Google services, including Search, Analytics, Maps, and Gmail. Bigtable is designed to handle massive workloads at consistent low latency and high throughput, so it’s an excellent choice for operational and analytical applications, including IoT, user analytics, and financial data analysis.

Cloud Bigtable is based on Apache HBase. This database has an enormous capacity and is suggested for use more than Terabyte data. One example, BigTable is the best for time-series data and IoT data.


  • Has good performance on 1Tb or more data
  • Cluster resizing without downtime
  • Incredible scalability
  • Support API of Apache HBase


  • Has bad performance on less than 300 Gb data
  • It doesn’t suit real-time
  • It doesn’t support ACID operations
  • The maximum size of a single value is 100 Mb
  • The maximum size of all values in a row is 256 Mb
  • The maximum size of the hard disk is 8 Tb per node
  • A minimum of three nodes in the cluster

Pricing Strategy

BigTable is very expensive. You pay for nodes (minimum $0.65 per hour per node) and storage capacity(minimum 26$ per Terabyte per month)

Analogs & alternatives

  • Custom deployed Apache HBase

Cloud Storage

GCS is blob storage for files

GCS is blob storage for files. You can store any amount of any size files there.


  • Good API for all popular programming languages and operating systems
  • Immutable files
  • Versions of files
  • Suitable for any size files
  • Suitable for any amount of files
  • Etc

Pricing Strategy

GCS has a couple of pricing plans. In a standard plan, you pay for 1Gb of saved data.

Analogs & alternatives

  • Amazon S3
  • Azure Blob Storage

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Other Google Cloud Services

There are a few more services that I should mention.

Google Cloud Compute Engine provides virtual machines with any performance capacity.

Google CloudSQL is a cloud-native solution to host MySQL and PostgreSQL databases. Has built-in vertical and horizontal scaling, firewall, encrypting, backups, and other benefits of using Cloud solutions. Has a terabyte capacity. Supports complex queries and transactions

Google Cloud Spanner is a fully managed, scalable, relational database service. Supports SQL queries, auto replication, transactions. It has a one-petabyte capacity and suits best for large-scale database applications which store more than a couple of terabytes of data.

Google StackDriver monitors Google services and infrastructure, and your application is hosted in a Google Cloud Platform.

Cloud Datalab is a way to visualize and explore your data. This service provides a cloud-native way to host Python Jupyter notebooks.

Google Cloud AutoML and Google AI Platform allow training and hosting of high-quality custom machine learning models with minimal effort.


Now you are familiar with the primary data services that Google Cloud Platform provides. This knowledge can help you to build a good data solution. But, of course, Clouds are not a silver bullet, and in case you use Clouds in the wrong way, it can significantly affect your monthly infrastructure billing.

Thus, carefully build your proposal’s architecture and choose the necessary services for your needs to reach your needed business goals. Explore all benefits and limitations for each particular case. Care about costs. And, of course, remember about the scalability, reliability, and maintainability of your solution.

Useful links:

How to leverage Big Data and Machine Learning for business insights

Big data and Machine Learning are hot topics of articles all over tech blogs. The reason is that businesses can receive handy insights from the data generated. The main tools for that are machine learning algorithms for Big data analytics.  But how to leverage Machine Learning with Big data to analyze user-generated data? Let’s start with the basics.

What is Big data?

Big data means significant amounts of information gathered, analyzed, and implemented into the business. The “Big data” concept emerged as a culmination of the data science developments of the past 60 years.

How to understand what data could be useful for business insights and what data isn’t? To find this out, you need to consider the following data types: 

  • Data submitted. When the User creates an account on the website, subscribes to an email newsletter, or performs payments, for example.
  • Data is a result of other activities. Web behavior in general and interact with ad content in particular. 

Data Mining and further Data Analytics are the heart of Big data solutions. Data Mining stands for collecting data from various sources, while Data Analytics is making sense of it. Sorted and analyzed data can uncover hidden patterns and insights for every industry. How do you make sense of the data? It takes more than to set up a DMP (Data Management Platform) and program a couple of filters to make the incoming information useful. Here’s where Machine Learning comes in.

What is Machine Learning (ML)?

Machine Learning processes data by decision-making algorithms to improve operations. 

Usually, machine learning algorithms label the incoming data and recognize patterns in it. Then, the ML model translates patterns into insights for business operations. ML algorithms are also used to automate certain aspects of the decision-making process.

What is Machine Learning in Big data?

ML algorithms are useful for data collection, analysis, and integration. Small businesses with small incoming information do not need machine learning. 

But, ML algorithms are a must for large organizations that generate tons of data. 

Machine learning algorithms can be applied to every element of Big data operation, including:

  • Data Labeling and Segmentation
  • Data Analytics
  • Scenario Simulation

Let’s look at how businesses use Machine Learning for Big Data analytics.

Machine Learning and Big data use cases

To give you an idea of how businesses combine both technologies, we gathered examples of big data and machine learning projects below. 

Market Research & Target Audience Segmentation

Knowing your audience is one of the critical elements of a successful business. But to make a market & audience research, one needs more than surface observations and wild guesses. Machine learning algorithms study the market and help you to understand your target audience. 

By using a combination of supervised and unsupervised machine learning algorithms you can find out:

  • A portrait of your target audience 
  • Patterns of their behavior
  • Their preferences

This technique is popular in Media & Entertainment, Advertising, eCommerce, and other industries.

To find out more about ML and Big data, watch the video. 

Source: Columbia Business School

User Modeling

User Modeling is a continuation and elaboration on Target Audience Segmentation. It takes a deep dive inside the user behavior and forms a detailed portrait of a particular segment. By using machine learning for big data analytics, you can predict the behavior of users and make intelligent business decisions. 

Facebook has one of the most sophisticated user modeling systems. The system constructs a detailed portrait of the User to suggest new contacts, pages, ads, communities, and also ad content.

facebook big data


Recommendation engines

Ever wondered how Netflix makes on-point suggestions or Amazon shows relevant products from the get-go? That’s because of recommender systems. A recommendation engine is one of the best Big data Machine Learning examples. Such systems can provide a handy suggestion on what types of products are “bought together.” Moreover, they point out the content that might also be interesting to the User who read a particular article.

Netflix recommendations


Based on a combination of context and user behavior prediction, the recommendation engine can:

  • Play on the engagement of the User
  • Shape his experience according to his expressed preferences and behavior on-site.

Recommendation engines apply extensive content-based data filtering to extract insights. As a result, the system learns from the User’s preferences and tendencies.

Predictive Analytics 

Knowing what the customer needs is one of the foundational elements of retail. That’s market basket analysis in action. Big data allows calculating the probabilities of various outcomes and decisions with a small margin of error. 

predictive analytics


Predictive Analytics is useful for:

  • Suggesting extra products on eCommerce platforms
  • Assessing the possibility of fraudulent activity in ad tech projects
  • Calculating the probabilities of treatment efficiency for specific patients in healthcare

One example is eBay’s system that reminds about abandoned purchases, hot deals, or incoming auctions.

Ad Fraud, eCommerce Fraud 

Ad Fraud is one of the biggest problems of the Ad Tech industry. The statistics claim that from 10% to 30% of activity in advertising is fraudulent.

Machine Learning algorithms help to fight that by:

  • Recognizing the patterns in Big data
  • Assessing their credibility
  • Blocking them out of the system before the bots or insincere users take over and trash the place

Machine learning algorithms watch ad track activity and block the sources of fraud.

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Conversational User Interfaces or chatbots are the most use case of Big data & machine learning. By leveraging machine learning algorithms, a chatbot can adapt to a particular customer’s preferences after many interactions 

The most well-known AI Assistants are Amazon’s Alexa and Apple’s Siri.

To find out, how does Alexa uses ML algorithms, watch the video. 

[Source: Data Science Foundation]

In Conclusion

Big data is an exciting technology with the potential to uncover hidden patterns for more effective solutions. The way it transforms various industries is fascinating. Big data has a positive impact on business operations. Machine learning eliminates routine operations with minimum supervision from humans. 

Both Big data and Machine Learning have many use cases in business, from analyzing and predicting user behaviors to learning their preferences. If you have selected the use case of  Big data Machine Learning for your business, do not hesitate to hire us for ML development services. 

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 Pattern Recognition and Why It Matters? Definitive Guide

Because of big data and machine learning technologies’ emergence, a lot of data became available that was previously either deduced or speculated. This data, rooted in more credible sources, provided the means to use more complex methods of data analysis to gain value-added benefits for the business. 

In other words, now that we “knew more,” we moved from the goal of getting information itself to analyzing and understanding the data that was already coming to us.  

Of all the tools used in Big Data, pattern recognition is in the center. It comprises the core of big data analytics – it gets the juice out of the data and uncovers the meanings hidden behind it.

Pattern recognition gives a strategic advantage for the company which makes it capable of continuous improvement and evolution in the ever-changing market.

What is Pattern Identification?

Pattern Recognition is the process of distinguishing and segmenting data according to set criteria or by common elements, which is performed by special algorithms.

Since pattern recognition enables learning per se and room for further improvement, it is one of the integral elements of machine learning algorithm.

Christopher Bishop in his seminal work “Pattern Recognition and Machine Learning” describes the concept like pattern recognition deals with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories.

In other words, pattern recognition is identifying patterns in data. These patterns tell the training data stories through ebbs and flows, spikes, and flat lines.

The data itself can be anything:

  • Text
  • Images
  • Sounds
  • Sentiments, and others.

Any information on the sequential nature can be processed by pattern recognition algorithms, making the sequences comprehensible and enabling their practical use.

Pattern Recognition Techniques

There are three main models of pattern recognition:

  • Statistical Pattern Recognition: to identify where the specific piece belongs (for example, whether it is a cake or not). This model uses supervised machine learning;
  • Syntactic/Structural: to define a more complex relationship between elements (for example, parts of speech). This model uses semi-supervised machine learning;
  • Template Matching: to match the object’s features with the predefined template and identify the object by proxy. One of the uses of such a model is plagiarism checking.

Introduction to Pattern Recognition

While the majority of pattern recognition in artificial intelligence operations is self-descriptive, there is a lot going on underneath.

Overall, there are two major parts of pattern recognition algorithms:

  • explorative – used to recognize commonalities in the data;
  • descriptive  – used to categorize the commonalities in a certain manner;

The combination of these two elements is used to extract insights out of the data, including the use in big data analytics. The analysis of the common factors and their correlation uncovers details in the subject matter that may be critical in understanding it.

Pattern Recognition Process Steps

The process itself looks like this:

  1. Data is gathered from its sources (via tracking or input)
  2. Data is cleaned up from the noise
  3. Information is examined for relevant features or common elements
  4. These elements are subsequently grouped in specific segments;
  5. The segments are analyzed for insights into data sets;
  6. The extracted insights are implemented into the business operation.

Use Cases for Pattern Recognition

Stock market forecasting, audience research, data analytics

Stock Market Forecasting, Audience Research – Data Analytics

Pattern Recognition technology and Data Analytics are interconnected to the point of confusion between the two. An excellent example of this issue is stock market pattern recognition software, which is actually an analytics tool.

In the context of data analytics, pattern recognition is used to describe data, show its distinct features (i.e., the patterns themselves), and put it into a broader context.(Read more in our article about Data Analytics and Data Mining.)

Let’s look at two prominent use cases:

  • Stock market forecasting – pattern recognition is used for comparative analysis of the stock exchanges and predictions of the possible outcomes. YardCharts use this pattern recognition analysis.
  • Audience research – pattern recognition refers to analyzing available user data and segmenting it by selected features. Google Analytics provides these features.
Text Generation, Text Analysis, Text Translation, Chatbots - Natural Language Processing

Text Generation, Text Analysis, Text Translation, Chatbots – Natural Language Processing

Natural Language Processing (aka NLP) is a field of Machine Learning focused on teaching machines to comprehend human language and generate its messages. While it sounds like hard sci-fi, in reality, it doesn’t deal with the substance of communication (i.e., reading between the lines) – it only deals with what is directly expressed in the message.

NLP breaks the text to pieces, finds the connections, and then constructs its variation. The process starts with differentiating the sentences; then it sorts out the words and parts of the speech where they belong and finally defines the ways these words can be used in a sentence.

To do that, NLP uses a combination of techniques that includes parsing, segmentation, and tagging to construct a model upon which the proceedings are handled. Supervised and unsupervised machine learning algorithms are involved in this process at various stages.

NLP is used in such fields as:

  • Text analysis – for content categorization, topic discovery, and modeling (content marketing tools like Buzzsumo use this technique);
  • Plagiarism detection – a variation of text analysis focused on a comparative study of the text with the assistance of the web crawler. The words are broken down into tokens that are checked for matches elsewhere. The exemplary tool for this is Copyscape.
  • Text summarization and contextual extraction – finding the meaning of the text. There are many online tools for this task, for example, Text Summarizer;
  • Text generation – for chatbots and AI Assistants or automated content generation (for example, auto-generated emails, Twitterbot updates, etc.);
  • Text translation – in addition to text analysis and word substitution, the engine also uses a combination of context and sentiment analysis to make closer matching recreation of the message in the other language. The most prominent example is Google Translate;
  • Text correction and adaptation – in addition to correcting grammar and formal mistakes, this technique can be used for the simplification of the text – from the structure to the choice of words. Grammarly, a startup founded by two Ukrainians in Kyiv, Ukraine, is one of the most prominent examples of such NLP pattern recognition uses.
Document Classification and Signature Verification - Optical Character Recognition

Document Classification and Signature Verification – Optical Character Recognition

Optical Character Recognition (aka OCR) refers to the analysis and subsequent conversion of the images considered as alphanumeric text into the machine-encoded text.

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The most common source of optical characters are scanned documents or photographs, but the thing can also be used on computer-generated unlabeled images. Either way, the OCR algorithm applies a library of patterns and compares them with the available input document to mark up the text and construct these. These matches are then assessed with the assistance of language corpus and thus perform the “recognition” itself.

In the heart of OCR is a combination of pattern recognition system and comparative algorithms attached to the reference database.

The most common uses of OCR include:

  • Text Transcription is the most basic process. The text is presented in recognizable characters is recognized and transposed into the digital space. This technology is well-presented on the market. A good example might be ABBYY Fine Reader.
  • Handwriting Recognition is a variation of text transcription with a more significant emphasis on the visual element. However, this time, the OCR algorithm uses a comparative engine to process the handwriting sample. A good example of this is Google Handwriting Input. While this technique’s primary goal is to the transcript, it is also used to verify signature and other handwriting samples (for example, for signing contracts or handwritten will);
  • Document Classification involves deeper processing of the document with a bigger focus on its structure and format. This technique is used for digitization of the paper documents and also for the reconstruction of the scattered elements of the damaged documents (for example, if the thing is shredded or the ink is partially blurred). Parascript is a product that provides such services for document classification. 
Visual Search, Face Recognition - Image Recognition

Visual Search, Face Recognition – Image Pattern Recognition

Image Recognition is a variation of OCR aimed at understanding what is on the picture. In contrast with OCR, image recognition to recognize what is depicted on the input images during image processing. Basically, instead of “recognizing” is “describes” the picture so that it would be searchable and comparable with the other images.

The main algorithms at work in image recognition are a combination of unsupervised and supervised machine learning algorithms.

The first supervised algorithm is used to train the model on the labeled datasets, i.e., examples of the depiction of the objects. Then the unsupervised algorithm is used to explore an input image. After this, a supervised algorithm kicks in and classifies the patterns as related to the particular category of objects (for example, an ink pen).  

There are two main use cases for Image Recognition:

  • Visual Search features are widely used in Search Engines and eCommerce marketplaces. It works the same way as an alphanumeric search query only with images. In both cases, image recognition constitutes a part of the equation. The other part is image metadata and also additional textual input. This information is used to increase the efficiency of the results and to filter the selection of options according to the context. For example, such technologies are widely applied by Google Search and Amazon.
  • Face Detection is widely used in social network services, such as Facebook and Instagram. The same technology is used by law enforcement to find a person of interest or criminals on the run. The technical process behind face detection is more intricate than simple object recognition. To recognize the appearance of a certain person, the algorithm needs to have a specialized labeled sample set. However, due to privacy limitations, these features are usually optional and require user consent. One of the better-known examples of this technology is VeriLook SDK.
AI Assistants, Speech-To-Text, Automatic Subtitling - Voice Recognition

AI Assistants, Speech-To-Text, Automatic Subtitling – Voice Recognition

The sound is an equally important source of information as any other. With the rapid development of machine learning algorithms, it became possible to use it in providing basic services.

In essence, voice recognition works on the same principles as OCR. The only difference is the source of information.

Voice and sound recognition are used for the following purposes:  

  • AI Assistants / Personal Assistant apps use natural language processing to compose the message and an additional database of sound samples to perform the message. For example, Google Assistant;
  • Sound-based Diagnosis – uses the comparative database of sounds to detect anomalies and suggest a possible cause and ways of fixing it. Commonly used in the automobile industry to inspect the state of the engine or the parts of the vehicle.
  • Speech-to-text and text-to-speech transformation use a comparative database of samples, OCR engine, and speech generation engine. Outside of AI assistants, it is also used to narrate written text (for example, this feature is available on Medium);
  • Automatic Caption addition involves speech-to-text recognition and subsequent image overlay to present the text on the screen (for example YouTube or Facebook automatic subtitling features).
Audience Research, Customer Service, Prescription, Recommendation - Sentiment Analysis

Audience Research, Customer Service, Prescription, Machine Learning, Recommendation – Sentiment Analysis

Sentiment Analysis is a subset of pattern recognition that takes an extra step to define its nature and what it can mean. In other words, it tries to understand what is behind the words – the mood, opinion, and, most importantly, an intent. It is one of the more sophisticated types of pattern recognition.

Sentiment analysis for business solutions can be used to explore the variety of reactions from the interactions with different kinds of platforms. To do that, the system uses unsupervised machine learning on top of the basic recognition procedure.

The assumptions of the sentiment analysis are usually grounded incredible sources such as dictionaries, but it can also include more customized databases depending on the context of the operation.

The use cases for sentiment analysis include:

  • Audience Research, content optimization, customer relationship platforms – used for the further definition of the audience segments, their interaction with the content, and analysis of the sentiments regarding it. It also contributes to the further optimization of the content. Such features are now tried out by Salesforce’s Einstein platform services.
  • Service Support – provides assistance in defining the nature of the query (whether it is positive or negative, combative, or poorly defined). This feature is commonly used in AI assistants like Alexa, Siri, and Cortana;
  • Prescription/Recommendation – used to predict the content of interest for the particular user. The suggestion may be augmented by the queries and past history of service use. The best examples are Netflix with their “you might also like” and Amazon with “people also buy”;

In Conclusion: Pattern Recognition Systems

Pattern recognition is the key to the further evolution of computational technology. With its help, big data analytics can progress further and we can all benefit from the machine learning algorithms getting smarter and smarter. 

As you can see, pattern recognition can be implemented in any kind of industry because where there is data, there are similarities in the data. Therefore, it’s wise to consider the possibility of implementing this technology into your business operations to make them more efficient.

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.

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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? 

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Conversational Interfaces – The Future of UI

The emergence of conversational interfaces and the broad adoption of virtual assistants was long overdue. They make things a little bit simpler in our increasingly chaotic everyday lives.

At the moment, we are witnessing how conversational UI is slowly but surely becoming commonplace in customer service, which is made possible by a significant breakthrough in machine learning and natural language processing.

However, there is still not enough understanding of what the concept of “Conversational Interface” really means. Because of that, let’s sort things out.

What is Conversational UI?

A conversational user interface is a type of user experience where the input is not strictly structured (i.e., more informal and more like, well, a conversation.)

It can be verbal or voice-controlled (like Siri or Alexa) or written, and it is more casual as if you’re talking to another human instead of typing phrases, like “outsourcing project development adtech Ukraine.”

While the name is slightly misleading (interface versus experience), many platforms already have UI that you have to fit into (for example, Facebook Messenger) therefore it’s the experience that users get.

Conversational interfaces are a natural continuation of the good old command lines. The significant step up from them is that the conversational interface goes far beyond just doing what it is told to do. It is a more comfortable tool, which also generates numerous valuable insights as it works with users.

How Conversational Interface Works

Conversational UI is built around the call and response approach. Natural language processing and machine learning algorithms are parts of conversational UI design. They shape their input-output features and improve their efficiency on the go.

Overall, the operation requires:

  • Natural Language Processing (NLP) to interpret the text input
  • Image Recognition for images and text-filled images
  • Natural Language Generation features to provide coherent responses
  • Text-to-speech and speech-to-speech output features

There are three ways users can “talk”:

  • Using text input – typing the questions. This option requires Natural Language Processing and Generation.
  • It is using image input – using pictures or written text to communicate the idea. This is a less common, usually a secondary, option. It needs optical character and image recognition.
  • Using speech input – literally talking out loud. In addition to NLP, this would require speech recognition and text-to-speech tools.

Natural Language Processing algorithms interpret the message and do the following actions:

  • Define the type of query aka intent (i.e., do something or find something, etc.);
  • Extract the essential elements of the query (i.e., understanding what action to do to which object);
  • Answer the question, which is represented either by performing requested activities or providing a response.

Conversational interface feeds on massive amounts of user data to provide the most efficient services. There are several sources of data:

  • User Account Data
  • Direct input
  • Past use data
  • Supplementary data used to interpret the context

The gathered information is used for:

  • the conversational platform operations
  • the natural language processing platform
  • machine-learning algorithms
  • speech recognition and generation platforms

As a result, the user gets relevant results or suggestions to their queries and streamlines his or her working process by saying or typing commands.

What are Virtual Assistants?

Virtual Assistants are also known as Chatbots and they are the products that use the conversational UI to communicate with the user.

Do you want to know more about chatbot benefits? or chatbot challenges?

The standard definition of “Virtual Assistant” (also known as “Virtual Intelligent Assistant”  and “AI Assistant”) is a “verbally-operated program designed to perform certain actions routinely or upon request.” However, as we mentioned above, the requests can also be typed. 

Types of Conversational Interfaces

A “conversational interface” is an umbrella term that covers almost every kind of conversation-based interaction service.

Some consider conversational UI to be just a flashier word for “chatbot.” While there is a direct connection, chatbots represent only a particular type of conversational interface that involves conversational elements for enabling its operation but is not defined by it.

From the use case point of view, there are several distinct types of conversational interfaces:

  • Q&A web chat interface is the most basic form. It doesn’t require Natural Language Processing or Machine Learning. It is an algorithm that delivers information upon request straight from its database. Often used to navigate content and give extracts from FAQ.
  • Customer Support interface is the most common type. It’s based on a call and response, template-based system. Able to provide general information. Includes another layer of interaction that assesses the ability of the bot to satisfy the incoming request. In case that is impossible, the system redirects the user to the human operator.
  • User Engagement interface is quickly becoming commonplace amongst the companies. This bot helps the user to navigate through the website, answer basic questions, and exchange information. Can be used for content or product suggestion. As an extension of its features, it can also provide initial lead generation activities. A good example of this approach can be Nuance’s recently unveiled project Pathfinder.
  • The organizer interface is more of a PDA organizer type. This type of interface is designed to keep the user in check with his schedule, manage to-do lists, remind them of different things, and perform simple actions without jumping around the windows/applications. It is a reverse bot that integrates with other services.
  • Multi-purpose Intelligent Virtual Assistant (aka AI Assistant) are big tech Internet of Things solutions like Amazon Alexa, Apple Siri, Microsoft Cortana.  

Now let’s look at some of the tools that are used to build your conversational interface.

Conversational Interface Tools


  • Chatfuel – a platform for simple Q&A / customer support chatbots with the website and social media integration. Easy to use due to the visual interface.
  • Botsify – this platform got many different feature templates, and you can construct your assistant out of building blocks.
  • MobileMonkey – a platform that helps to create a Facebook Messenger chatbot, which is quite convenient for businesses that have a Facebook page (which is, by now, pretty much a requirement for trust.)
  • Sequel – this one is easy to use for informational services, such as providing excerpts and redirects.
  • – one of the more diverse platforms. Highly compatible with social media platforms. You test the bot on the go. Can be used for full-on lead generation and user engagement.

Natural Language Processing

  • (Dialogflow) – this one is a streamlined, no-nonsense tool where you can program the framework and hone it over time. With you can quickly train multiple scenarios of reaction and diverse interpretations of intent and content. It is free and is a great starting point for many.
  • Microsoft Language Understanding Intelligent Service (LUIS) – this is a go-to tool to program conversational intelligence tailor-made for your cause. You can use the existing templates from Bing and Cortana.
  • & – these platforms take a more narrative-based approach which works if your goal is user engagement and lead generation. You can build multiple story points for every turn of events and automatically proceed the initial stage of making contact with a potential client.
  • IBM Watson Assistant – the Swiss army knife of NLP platforms. With Watson, you can build an entire neural network around the bot and gather much more information than usual. It works well if you want to get handy customer insights without breaking a sweat.

Conversational Interface Use Cases

Basic Customer Support

To provide simple customer support, the UI takes the requested information straight from the source material or reinterprets it by natural language processing features to fit the context of the conversation.

In more sophisticated cases, a customer support assistant can also handle notifications, invoices, reports, and follow-up information.

The system can also redirect to the human operator in case of queries beyond the bot’s reach.

Conversational Navigation / Service Guidance

Streamlining the user journey is a vital element for improving customer experience. A natural language user interface is one of the ways it can be achieved.

Here are the types of assistance:

  • Guiding through the checkout process (for example, for money transactions);
  • Filling web forms, subscriptions, sign-ups, etc.
  • Offering operating options (download, sign-up, etc.);
  • Suggesting content

The primary purpose of an assistant is to gather correct data and use it for the benefit of the customer experience.

An excellent example of this is a CRM assistant. Depending on the configuration, it can:

  • answer questions,
  • suggest options (depending on the context of the situation)
  • perform actions (sort contacts, make a report, etc.).

Lead Generation

Lead Generation is the next step from simple customer support. Instead of operating upon request, it engages with the user – the conversational interface is used to extract as much valuable information as possible via more convenient conversational user experiences.

The reason why it works is simple – a conversation is an excellent way to engage the user and turn him into a customer.

The nature of the questions may vary, but the goal is usually to get contact information and business details about the user, such as:

  • Name
  • Job title
  • Company name
  • Contact information (email or phone)

This information then goes straight to the customer relationship management platform and is used to nurture the leads and turn them into legitimate business opportunities.


Conversational UI is applied mainly to enhance productivity. It’s no wonder – there are just many routine things to keep track of.

Such an assistant is a command line that can understand simple, more natural-sounding questions, and be connected to the applications on the computer or mobile device.

Productivity conversational interface is designed to streamline the working process, make it less messy, and avoid the dubious points of routine where possible.

For instance, productivity assistants can handle basic task management duties such as:

  • Task management – creation, assigning, and status updates
  • Retrieving reports, facilitating communication
  • Time management – keeping the schedule intact, booking rooms, making appointments, setting reminders
  • Research – delivering search results in a processed form, most commonly as a summary or digest

Also, such an interface can be used to provide metrics regarding performance based on the task management framework.

Content Suggestion

There is way too much content to get through these days. To get to the most valuable content, users need some extra tools that can sort the content and deliver only the relevant stuff.

The content recommendation is one of the main use cases for of conversational interface. Via machine learning, the bot can adapt content selection according to the user’s preference and/or expressed behavior.

The results can be presented in a conversational manner (such as reading out loud the headlines) or in a  more formal packaging with highlighted or summarized content. For example, The New York Times offers bots that display articles in a conversational format.

Conversational Marketing

Chatbots can be a weapon of mass engagement in the hands of the right marketing team. Just as email marketing makes a case for the brand presentation, chatbots can do the same on multiple platforms.

Such an approach is not limited to your website – it is also relevant for social networks. The features of this kind of interface may vary. Generally, they are:

  • Basic information exchange
  • Content delivery / content suggestion
  • News digests
  • Follow-ups

The biggest benefit from this kind of conversational UI is maintaining a presence throughout multiple platforms and facilitating customer engagement through a less formal approach.

Conversational Interfaces Challenges

Defining Relevant Use Cases

The implementation of a conversational interface revolves around one thing – the purpose of its use.

As mentioned before in the Types section, the use cases may vary from basic a Q&A to a hands-on organizer to a powerful lead generation and marketing tool.

It is essential to understand what you want to do with the conversational interface before embarking on its development. Also, you need to think about the budget you have for such a tool – creating a customized assistant is not the cheapest of endeavors (although there are exceptions).

Different types of interfaces require different features and can’t be tweaked to do something else with the flick of the wrist.

The key to success is to decide:

  • What kind of actions can be beneficial for users?
  • How can certain features contribute to the increase in engagement?
  • What level of conversational UI accessibility is appropriate for the target audience?

Here are some things that can help decide what’s best for you:

  • Event monitoring. Study the analytics of your website: what kind of actions are users usually performing on your platform? What are the weak points? Where does the drop off occur? What type of content is preferred? These questions will help you to round up the fields you can cover with the conversational interface.
  • A/B testing routines will help to figure out the most fitting presentation.

Machine Learning Model Training

The other big stumbling block for conversational interfaces is machine learning model training. While ML is not required for every type of conversational UI, if your goal is to provide personalized experience and lead generation it is important to set the right pattern.

The challenge is twofold:

  • You need to teach the bot to interpret the input text and deliver relevant responses.
  • You need to hone the algorithms that will help the bot adapt to a particular user profile to increase personalization and relevance of output.

It should be noted that this challenge is more of a question of time than effort. It takes some time to optimize the systems, but once you have passed that stage – it’s all good.

To configure a well-oiled conversational UI, you need a combination of descriptive and predictive machine learning algorithms. The models depend on the use case.

Natural Language Processing Configuration

NLP is at the front and center of conversational interfaces. When this is missing in the system, your users might end up getting the frustrating “Sorry, I don’t understand that” and leave.

To avoid such occurrences, you need to set a coherent system of processing input and delivering output.

In this example of the most basic conversational UI framework, here is the sequence:

  • Cleanup of the input information. This includes:
    • Punctuation removal
    • Stopwords removal
    • Word tokenization
  • Words stemming, lemmatizing, vectorizing to interpret the message
  • “Decision-making component,” an integration with outside services to commit requested actions;
  • Output generation for responses.

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Privacy concerns

With the growing concerns over the safety of user data, maintaining the privacy and security of personal data becomes one of the major challenges of conversational interfaces on the business side of things.

Conversational interfaces use data from user’s devices, emails, contacts, search history, and other sources to provide adequate services.

However, given the fact that all these operations are often performed through third-party applications – the question of privacy is left hanging. There is always a danger that conversational UI is doing some extra work that is not required and there is no way to control it.

How to solve this issue? The only viable solution is to add an explanation of:

  • How your conversational interface assistant operates
  • The kind of user data that is gathered

Other important details to specify are:

  • How is user data handled?
  • Is it pseudonymized?
  • Is the data disposed of after it served its cause and in what timeframe?

To understand the underlying legal challenges regarding personal information, check out the EU’s General Data Protection Regulation.

What’s next?

The emergence of Conversational interfaces has been long-awaited. Now, after decades of being something from science fiction, it has become just another part of everyday life.

This technology can be very effective in numerous operations and can provide a significant business advantage when used well.

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Guide to Supervised Machine Learning

The competitive advantage of any company is built upon insights. Understanding what the information holds for you is one of the most vital requirements for business success.

Supervised Machine Learning paves the way for understanding uneven, hidden patterns in data by transforming raw data into the menagerie of insights that show you how to move forward and accomplish your goals.

The secret of the successful use of machine learning lies in knowing what exactly you want it to do. In this article, we will take a closer look at business applications of supervised learning algorithms.

What Is Supervised Machine Learning?

Supervised learning is a type of machine learning algorithm that looks for the unknown in the known. 

For example, you have known input (x) and output (Y). A simplified supervised machine learning algorithm would look like an equation:

Y = f(x)

Where your goal is to train your model in such a way that you would be able to tell what kind of Y would you get if you change x. In less technical terms, it is an algorithm designed to sort through the data and squeeze the gist of it in the process so that you could understand what the future holds for you.

Supervised machine learning applications are all about:

  • Scaling the scope of data;
  • Uncovering the hidden patterns in the data;
  • Extracting the most relevant insights;
  • Discovering relationships between entities;
  • Enabling predictions of the future outcomes based on available data;

How does it work?

The supervised learning algorithm is trained on a labeled dataset, i.e., the one where input and output are clearly defined.

Data Labeling means:

  • Defining an input – the types of information in the dataset that the algorithm is trained on. It shows what types of data are there and what are their defining features;
  • Defining an output – labeling sets the desired results for the algorithm. It determines the articulation of the algorithm with the data (for example, matching data on “yes/no” or “true/false” criteria).

The labeled dataset contains everything the algorithm needs to operate while setting the ground rules. The training process consists of 80% of training data and 20% of testing data.

With clearly determined values, the “learning” process is enabled, and the algorithm can “understand” what it is supposed to be looking for. From the algorithm’s perspective, the whole process turns into something akin to a “connect the dots” exercise.

Now let’s look at two fundamental processes of supervised machine learning – classification and regression.

Classification – Sorting out the Data

Classification is the process of differentiating and categorizing the types of information presented in the dataset into discrete values. In other words, it is the “sorting out” part of the operation.

Here’s how it works:

  1. The algorithm labels the data according to the input samples on which the algorithm was trained.
  2. It recognizes certain types of entities, looks for similar elements, and couples them into relevant categories.
  3. The algorithm is also capable of detecting anomalies in the data.

The classification process covers optical character or image recognition, and also binary recognition (whether a particular bit of data is compliant or non-compliant to certain requirements in a manner of “yes” or “no”).

Regression – Calculating the Possibilities

Regression is the part of supervised learning that is responsible for calculating the possibilities out of the available data. It is a method of forming the target value based on specific predictors that point out cause and effect relations between the variables.

The process of regression can be described as finding a model for distinguishing the data into continuous real values. In addition to that, a regression can identify the distribution movement derived from the part data.

The purpose of regression is:

  • To understand the values in the data
  • To identify the relations or patterns between them.
  • To calculate predictions of certain outcomes based on past data.

Examples of supervised machine learning


Decision Trees – Sentiment Analysis & Lead Classification

Decision trees are a primary form of organizing the operation in machine learning, which can be used both for classification and regression models. The decision tree breaks down the dataset into exponentially smaller subsets with a deeper definition of an entity. It provides the algorithm with the decision framework.

Structure-wise, decision trees are comprised of branches with different options (nodes) going from general to specific. Each branch constitutes a sequence based on compliance with the node requirements.

Usually, the requirements of the nodes are formulated as simple as “yes” and “no”. The former enables further proceeding while the latter signifies the conclusion of the operation with the desirable result.

The depth of the decision tree depends on the requirements of the particular operation. For example, the algorithm should recognize the images of apples out of the dataset. One of the primary nodes is based on the color “red,” and it asks whether the color on the image is red. If “yes” the sequence moves on. If not – the image is passed on.

Overall, decision trees use cases include:

  • Customer’s Sentiment Analysis
  • Sales Funnel Analysis

See also: Why Business Applies Sentiment Analysis

Linear Regression – Predictive Analytics

Linear Regression is the type of machine learning model that is commonly used to get insight out of available information.

It involves determining the linear relationship between multiple input variables and a single output variable. The output value is calculated out of a linear combination of the input variables.

There are two types of linear regression:

  1. Simple linear regression – with a single independent variable used to predict the value of a dependent variable
  2. Multiple linear regression – with numerous independent variables used to predict the output of a dependent variable.

It is a nice and simple way of extracting an insight into data.

Examples of linear regression include:

  • Predictive Analytics
  • Price Optimization (Marketing and sales)
  • Analyzing sales drivers (pricing, volume, distribution, etc.)


Logistic Regression – Audience Segmentation and Lead Classification

Logistic regression is similar to linear regression, but instead of a numeral dependent variable, it uses a different type of variables, most commonly binary “yes/no” / “true/false” variations.

Its primary use case is for binary prediction. For example, it is used by insurance companies to determine whether to give a credit card to the customer or decline.

Logistic Regression also involves certain elements of classification in the process as it classifies the dependent variable into one of the available classes.

Business examples of logistic regression include:

  • Classifying the contacts, leads, customers into specific categories
  • Segmenting target audience based on relevant criteria
  • Predicting various outcomes out of input data

Random Forest Classifier – Recommender Engine, Image Classification, Feature Selection

Random Forest Classifier is one of the supervised machine learning use cases that apply the decision trees.

It creates a sequence of decision trees based on a randomly organized selection from the training dataset. Then it gathers the information from the other decision trees so that it could decide on the final class of the test object.

The difference from the traditional decision trees is that random forest applies an element of randomness to a bigger extent than usual. Instead of simply looking for the most important feature upon the node split, it tries to find the best feature in the random selection of features.

This brings a large degree of diversity to the model and can seriously affect the quality of its work.

Deep decision trees may suffer from overfitting, but random forests avoid overfitting by making trees on random subsets. It takes the average of all the predictions, which cancels out the biases.

Random Forest Classifier use cases include:

  • Content Customization according to the User Behavior and Preferences
  • Image recognition and classification
  • Feature selection of the datasets (general data analysis)

Gradient Boosting Classifier – Predictive Analysis

Gradient Boosting Classifier is another method of making predictions. The process of boosting can be described as a combination of weaker (less accurate) learners into a stronger whole.

Instead of creating a pool of predictors, as in bagging, boosting produces a cascade of them, where each output is the input for the following learner.  It is used to minimize prediction bias.

Gradient boosting takes a sequential approach to obtain predictions. In gradient boosting, each decision tree predicts the error of the previous decision tree — thereby boosting (improving) the error (gradient).

Gradient Boosting is widely used in sales, especially in the retail and eCommerce sectors. The use cases include:

  • Inventory Management
  • Demand Forecasting
  • Price Prediction.

Support Vector Machines (SVM) – Data Classification

Support Vector Machines (aka SVM) is a type of algorithm that can be used for both Regression and Classification purposes.

At its core – it is a sequence of decision planes that define the boundaries of the decision. Different planes signify different classes of entities.

The algorithm performs classification by finding the hyperplane (a unifying plane between two or more planes) that maximizes the margin between the two classes with the help of support vectors. This shows the features of the data and what they might mean in a specific context.

Support Vector Machines algorithms are widely used in ad tech and other industries for:

  • Segmenting audience
  • Managing ad inventory
  • Providing a framework for understanding the possibilities of conversions in the specific audience segments of the particular types of ads.
  • Text Classification

Naive Bayes – Sentiment Analysis

Naive Bayes classifier is based on Bayes’ theorem with the independence assumptions between predictors, i.e., it assumes the presence of a feature in a class is unrelated to any other function. Even if these features depend on each other or upon the existence of the other elements, all of these properties work independently. Thus, the name Naive Bayes.

It is used for classification based on the normal distribution of data.

The Naive Bayes model is easy to build, with no complicated iterative parameter estimation which makes it particularly useful for very large datasets.

Naive Bayes use cases include:

  • Data Classification (such as spam detection)
  • Lead Classification
  • Sentiment Analysis (based on input texts, such as reviews or comments)


The vast majority of business cases for machine learning use supervised machine learning algorithms to enhance the quality of work and understand what decision would help to reach the intended goal.

As we have seen in this article, numerous business areas can benefit from the implementation of ML – sales and marketing, CEOs, and business owners, the list goes on. 

You’ve got business data, so make the most of it with machine learning.