The terms “Big Data” and “Machine Learning” are currently among the most persistent buzzwords in the various tech articles all over the web. It is easy to see why — we live in the time when the amount of information produced by every active user is exploding exponentially with each coming day.
More and more information is constantly created as a result of the rapid development of web technologies, social media, and mobile devices. At the end of 2017, there were 2.5 quintillion bytes of data created per day. As we approach the end of 2018, we can see that number quickly rising still.
So with all that information available, how can big data and machine learning be used for particular business processes?
Let’s start with the basics.
The technical definition of “Big Data” is relatively simple — it refers to extra large amounts of data that require specialized solutions in order to be gathered, analyzed and implemented into the business operation. Machine learning algorithms are applied to increase efficiency and insightfulness of this data (but we'll expand on ML a bit later.)
The “Big Data” concept emerged as a culmination of the data science developments of the past 60 years. It is often referred to as revolutionary due to the potential of changing the ways we work, live and even think. This kind of data requires a lot of power and specific mechanisms to handle it.
- 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.
Everything is data in one way or another, but you have to understand what data is useful for your particular business operation or purpose and what can be disregarded (at least for a time being.)
Overall, there are two types of gathered data.
- Data submitted intentionally. This type of data includes cases when the user creates an account on the website, subscribes to an email newsletter, or performs payments, for example.
- Data as a byproduct of other activity. This can be a lot of things — clicking on the link is the most obvious example. The other byproducts include demonstrated web behavior in general and interaction with ad content in particular. All that can be processed and put in a certain context.
- Collected straight from your own resources (first party);
- Brought in by your partners or affiliates (second party);
- Bought from the third-party.
Data Mining and subsequent Data Analytics are right in the very heart of the 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 that can be useful in a variety of fields. As such, big data is a source of incredible business value for every industry.
“Big data really is about having insights and making an impact on your business. If you aren’t taking advantage of the data you’re collecting, then you just have a pile of data, you don’t have big data” says Jay Parikh, VP of Engineering at Facebook.
How do you make sense of the data? It takes more than just to set up a DMP (Data Management Platform) and program a couple of filters in order to make the incoming information useful. Here's where Machine Learning comes in.
The term “Machine Learning” can be defined as systems of automated data processing and decision-making algorithms designed to improve their operation according to the results of their work.
Basically, it means “learning on the go.”
In the realm of Big Data, Machine Learning is used to keep up with the ever-growing and ever-changing stream of data and deliver continuously evolving and valuable insights.
Usually, machine learning algorithms are used to label (define what it is) the incoming data and recognize patterns in it, which are subsequently translated into valuable insights that can be further implemented into the business operation. After that, the algorithms also used to automate certain aspects of the decision-making process.
For this purposes, machine learning (ML) algorithms can use a variety of techniques like decision trees or neural networks.
ML algorithms provide effective automated tools for data collection, analysis, and integration. When your business is small and there's not too much incoming information, it might not require machine learning because everything can be done manually (or using simple tools).
However, when you have a business that deals with big data, ML algorithms save the day. Combined with cloud computing power, machine learning enables fast and thorough processing and integration of large amounts of various information, be it user behavior or purchases or DNA sequencing or the effectiveness of your ads.
Machine learning algorithms can be applied to every element of Big Data operation including:
- Data Labeling / Segmentation;
- Data Analytics:
- Scenario Simulation.
All these elements combined create the big picture constructed out of Big Data — with insights, patterns and everything else of interest sorted out, categorized, and packaged into a digestible form.
It is important to note that applying Machine Learning in Big Data solutions is basically an infinite loop. The algorithms created for certain purposes are monitored and perfected over time as the information is coming into the system and out of the system.
Let’s look at the use cases of machine learning in Big Data.
Business benefits: instead of countless questionnaires and Q&A sessions with people on the street (or online), machine learning algorithms study the market and help you understand what your target audience is like.
Knowing your audience is one of the key elements of the successful business. But in order to make an effective market & audience research, one needs more than surface observations and wild guesses. Enter Big Data and Machine Learning.
A combination of supervised / unsupervised machine learning algorithms can give:
- an understanding of what one’s real target audience looks like;
- what are their major patterns of behavior;
- what are their preferences?
Subsequently, this allows to keep up with the trends in the target audience behavior and adapt the campaigns accordingly.
This technique is widely used in Media & Entertainment, Advertising, eCommerce and other industries and many large-scale campaigns rely on such algorithms.
Business benefits: based on the information you know about your customer, you can predict the behavior of this type of users and make your business decisions based on your audience.
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.
The primary purpose of user models is to provide a frame of reference for an ad tech system or recommendation engine.
User models are also used to predict how particular segments are acting and adapt the system accordingly. For example, it can be suggesting a particular set of content or showing relevant products or ads.
Facebook has one of the most sophisticated user modeling systems. It is able to construct a detailed portrait of the user that will be used to suggest new contacts, pages, communities and also ad content.
Business benefits: raise the users' engagement with your product by offering them goods or services they might like. Clients are happy because they get what they want without too much hassle, you get happy clients.
Ever wondered how Netflix makes on-point suggestions or Amazon shows relevant products from the get-go? That’s because of recommender systems.
Recommendation / Filtering / Personalization engines are the most common uses of machine learning. Such systems can provide handy suggestion what types of products are “frequently bought together” or point out at the content that might be also interesting to the user who read a particular article.
For example, you can implement such a solution in ecommerce, social media, digital advertising, and other content-oriented fields.
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 in order to extract insights. As a result, the system learns from the user's preferences and tendencies, and in turn, it also shapes the perception and preferences of the user by offering similar information. In other words, it learns from the user and it also teaches the user.
As it was mentioned previously, Amazon and Netflix got great recommender systems. In addition to that, Spotify is using a very efficient system that analyzes the user’s listening history, search queries, listening stats, and other expressed behavior to construct personalized playlists.
Business benefits: once you know what your customers are like, you can use machine learning to analyze their behavior and be able to predict the future... or at least what the clients might like.
Knowing what the customer needs is one of the foundational elements of retail commerce and it is a reasonable way of getting the customer into considering buying other products. That’s market basket analysis in action.
Big data, since it's big, gives the opportunity to calculate the probabilities of various outcomes and decisions with a small margin of error. This, in turn, helps to work out operation procedures in order to be prepared as much as possible ahead of time.
Predictive Analytics can be used in a variety of ways, for example:
- Suggest additional products on eCommerce platforms;
- Develop content for inbound marketing;
- Predict the most fitting/effective time for publishing articles and multi-channel spreading;
- Assess the possibility of fraudulent activity in ad tech projects;
- Calculate the probabilities of outbreaks or courses of treatment for specific patients for healthcare systems.
One of the most prominent examples of Market Basket Analysis can be seen on eBay. Their system is aimed at keeping the user on point with the types of products he is interested in and also remind about abandoned purchases, hot deals, or incoming auctions.
Business benefits: stop losing so much money to bots and fraudulent activity in your ad tech project.
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.
Here’s how it looks in action: machine learning algorithms are used to monitor ad content-related activity and assess its quality. If there are any suspicious actions or sudden spikes of activity, it notifies the system and blocks the detected sources of fraudulent activity.
On the other hand, big data and machine learning can help to figure out fraudulent activities on eCommerce marketplaces. In this case, there are more intricate systems that monitor user activity and assess their payment-related inputs.
Overall, this process combines anomaly and credential analysis:
- Anomaly analysis assesses the nature of the user activity. For example, if the user is hoarding products in the cart or then suddenly deletes everything.
- Credential analysis checks the credibility of the payment-related input information, such as credit card number, balance, account history and so on. The combination of these two types of data allows to identify potential fraud and cut it off before the damage is done.
Business benefits: spend less time on sorting through your inbox or incoming requests from the website.
Have you ever wondered how your Gmail account sorts out spam mail?
Spam mail or comments may be not as harmful as other types of fraud, but it is definitely irritating and time-consuming to deal with. Sometimes, spam can even be weaponized by the competition and this may be the big problem for the CRM. That’s where big data machine learning kicks in.
Overall, there are two filters at play:
- The content filter uses databases with examples of the typical word combinations and sentences previously used in spam messages to compare with the selected letters or messages. If there is a match, the letter or message is automatically discarded.
- The sender filter checks the credibility of the sender. If a particular email address has been often reported in a bad way, it wins the one-way ticket to the blacklist.
Spammers aren't fools so they try their best to bypass such filters, for example by using blatantly erroneous spellings. But machine learning algorithms are ahead of the game and add new examples of spam to the database to be in the know next time around.
Businesses: Marketing, eCommerce
Use Case: Conversational User Interfaces
Business benefits: people want to spend less time performing a task and one of the ways is using a bot to order something or find out information. Therefore use that opportunity to become even more available to the general public via Siri or Alexa or usual messenger chatbots.
Conversational User Interfaces are amongst the most exciting developments in the field of big data & machine learning. The way they managed to establish themselves on the market is nothing short of staggering. AI assistants can be a viable option for eCommerce Marketplaces and service-oriented companies.
While its primary function is to interact with the customers and provide them with the relevant information, the interaction itself presents a rather useful sleigh of insights into how customers are formulating their queries and what are they looking for.
Because of machine learning algorithms, the whole system can upgrade itself and adapt to the particular customer with more and more examples to lean upon. This information can be further used to adjust your business model and targeting of the marketing campaigns.
The most well-known AI Assistants are Amazon’s Alexa and Apple’s Siri.
Simply collecting data is one thing. Making it a value-added for your business development is a whole another level. To do this, you need to understand what kind of data you need and how to use it properly.
The thing is — it takes little effort to set up a tracker and gather every single one and zero going through the system. However, with this approach, you will be forced to go through a lot of information you don’t really need, which is rather time-consuming.
Besides defining the data you need, there are two additional sub-challenges:
- Unlabeled data: dealing with this is simple - you need to sort and label it.
- Data noise: this is a more complicated issue because you need to decide what "data noise" means specifically for your business. What might be useful for some businesses might be useless for yours and vice versa, so take time to clear this issue.
Big Data is one of the reasons why Cybersecurity concerns are an all-time high. Cambridge Analytica scandal, Equifax leaks — all that is about data collecting and its illicit use.
Given the fact that the majority of the gathered information is related to the users in one or another, the whole thing automatically becomes sensitive. In May of 2018, European Union had adopted the General Data Protection Regulation (GDPR) and this is already a turning point for Big Data industry due to clearly defined consequences of carefree and reckless data gathering.
What does it mean? The entire process of data collection and use should be:
- Strictly regulated (who has an access, why, which level)
- Informed (data is gathered and processed with the official consent agreement)
- Monitored (when happens what, why)
- Transparent (no concealed or undocumented processes in the system)
Simple data leak can cause big trouble, Big Data leak is a disaster. Just think about all the bad stuff - identity theft, malicious manipulations, fraudulent actions - these are the things you'd probably want to avoid in your business.
The solution for protecting the data and providing security is two-fold: monitoring and controlled access.
- Modern systems of real-time monitoring are extremely efficient in spotting anything outside of the norm (AKA anomalies) in your system. Sure, since it is an automated process there is a fair share of false alarms, but given the scope of monitored information, it just something you'd have to adjust to. Better to be safe than sorry.
- Security measures can extend into full-on audits where the entire system is assessed and analyzed for strong and weak points.
- The other important element is keeping logs for database operators so that every entry and action would be documented.
As with any other system that contains sensitive information, the best way to keep it safe is to control who has direct and indirect access to it.
The whole process should include:
- Multiple levels of access and permissions for different types of users;
- Full encryption of the proceedings;
- Two-factor authentication in order to minimize the slightest possibility of unlawful entry.
Data storage options are not so hard to create from a technical standpoint, but it's more of a challenge in terms of logistics.
Storing data requires a well-defined sequencing of transmitting data and its dynamic categorizing. Categories can depend on different factors: the level of protection, the speed of access, the frequency of use. All this involves automated transferring of information to fit the requirements.
In addition to that, every piece of data has a status, which also affects its position. For example, there is:
- collected information that is waiting to be processed
- already processed but not integrated information
- integrated information.
This process can be handled through Automated Storage Tiering algorithms. In addition to handling the information itself, AST can operate with its metadata in order to increase the overall efficiency of an operation.
While storing the data does not require much creativity in terms of technology, data analytics is a more complex thing.
Data Analytics systems are a major element of big data operations and it often suffers most from technological progress. It requires constant upgrades, fixes, and tweaks to make sure it remains effective in handling ever-growing amounts of data.
At the same time, technological progress also brings open-source technologies and microservices that are more flexible than monolith software, which helps to solve this challenge.
Basic big data operation involves collecting information from multiple sources and different formats and making it cohesive and comprehensive. The challenging part is to validate the credibility of this data.
The catch is that there is too much stuff to go through. This process requires resources to sort through, segment the information, and leave the data noise behind. Therefore, you need to have a system with foolproof mechanisms in place that checks the sources and nature of gathered information and keeps the intentionally or unintentionally malicious data away.
Machine learning algorithms help with automating these processes of data labeling. Semi-supervised ML algorithm is capable of going through the unlabeled data and comparing it with the available examples of labeled data. This process allows adjusting the operation accordingly thus retaining a high level of efficiency.
Machine learning models are designed to perform specific tasks and there are several types of algorithms. Each of them applies different methods to conduct various operations.
- Supervised algorithms work with labeled data and can be used for various prediction and prescription operations (price predictions, product suggesting).
- Unsupervised algorithms work with unlabeled data and used to label and segment it.
- Semi-supervised learning algorithm combines two aforementioned approaches in order to perform content classification and other filtering activities.
- Reinforcement learning algorithms are used for more operations of more routine nature (web content crawling, system checks, etc);
The thing with these types of algorithms is that they can’t do what they were not designed to.
Because of that, it is important upon considering the implementation of machine learning algorithms into the system to clearly define what kinds of operations are required.
This will give a clear understanding which algorithms to develop.
It takes a lot of data to construct efficient machine learning model. Otherwise, the model will be unable to identify things it should. The challenge comes with acquiring said data.
There are several ways of getting data. You can:
- Set-up your data gathering via tracking and surveys
- Create sample data sets (so-called faux data)
- Buy data from the third party
Here are the requirements for machine learning data:
- Data needs to be validated as credible;
- Data must be relevant to operation the algorithm will be applied at;
- Data must be labeled (in cases of supervised machine learning) & cleaned up (more on that below).
With all that in mind, you have to remember that your data should be acquired legally.
As it was mentioned before, you need to know what you are looking for before you start collecting data.
The process of data labeling is a strategic challenge in building Machine Learning Models. You need to label the data to create a working model. This challenge is especially time-consuming when you initially have a supervised machine learning model.
There are two ways to deal with this issue. One is to buy third-party labeled data. It costs a penny, but you will have the package ready to go.
The other way is handling the data with unsupervised machine learning algorithms that can operate on unlabeled data and identify patterns to group it in a convenient form. Granted, you will still need to sort out the most relevant pieces to train the model, but you'll get a good headstart.
While data labeling is a strategic challenge, sorting out the noise in data is a tactical one.
According to IBM, about 80% of data scientists time is dedicated to cleaning data.
Data noise is basically anything of no particular use for the given process. It can include:
- Incomplete or damaged bits
- Inconsequential bits of data
- Anomalous bits (related to fraudulent activity)
- Unidentifiable information
The influence of the data noise on ML is immense — it can affect the efficiency of the machine learning algorithm and dilute its performance. Therefore, you need to clean up and format the information before you feed it to the algorithms.
Sorting out the noisy data depends on the type of data itself and its sources' quality. In terms of technological solutions, Apache Spark and Flink help with this challenge.
Model training is the most crucial moment in setting up the machine learning algorithm. An untrained algorithm is like a toddler who doesn't know any rules (and therefore, while being cute, isn't helpful at all.)
Here are the main challenges with machine learning model training:
- Limited data sample
- Insufficient algorithm structure
- Vague goals of the algorithm
If the algorithm is trained on insufficient or limited data samples — chances are it will not be as effective as it needs to be.
The source of this problem often lies in the lack of understanding of the algorithm's purpose. Therefore, you need to know what kind of data you expect to have.
There is a particular set of tools that can make it all tick like clockwork. Such solutions as TensorFlow, PyTorch, Apache Spark, and Apache MXNet can handle different aspects of this issue.
Big data is an exciting technology with the potential to uncover hidden patterns and find better and more effective solutions to many problems. The way it transforms various industries is fascinating and the positive impact it has on the business operation is undeniable.
Machine learning is the technology that finally makes many tangled processes that previously required a lot of time and effort routine operations that require mere supervision and not a manual action.
Do you have a Big Data project that needs the benefits of a machine learning technology?
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