Understanding the needs of your customers is an integral part of customer satisfaction. And, you need all the information you can get about them to deliver the high-quality products or services they desire.
Generating and analyzing customer data is key. If you run a large-scale business, big data analytics comes in handy for making well-informed decisions to drive your business forward.
Did you know that you can also use big data analytics to prevent cybersecurity threats? In this article, you’ll learn about big data analytics, how it works, and how you can use it to prevent cybersecurity threats.
What Is Big Data Analytics?
Big data is a diverse category of data in high volume, variety, and velocity. The people who interact with your business produce tons of data that are originally raw or unstructured.
The use of advanced technologies driven by Artificial Intelligence (AI) and the Internet of Things (IoT) in modern-day businesses increases the volume of data generated by consumers. Large-scale businesses are left with more data that would be difficult to analyze without big data analytics.
Big data analytics is the process of studying and analyzing behavioral patterns to make well-informed decisions and predictions.
Businesses interpret high-volume consumer data with analytic techniques and software to understand their consumers better, satisfy their needs, and position themselves strategically.
How Does Big Data Analytics Work?
Data in raw form is of little or no importance to your business. Rather than operating blindly, you can leverage big data to get a clearer picture of your business direction.
Here's how big data analytics works:
1. Data Collection
The first step in big data analytics is data collection. It’s the process of getting raw, unstructured, or structured information about your customers.
The various mediums of data collection include cloud storage, artificial intelligence, mobile applications, in-store IoT sensors, etc.
You can store the data that you collect in warehouses for easy access by the intelligence software that you have adopted in your business.
If you are dealing with overly complex raw data, you can store them in a data lake instead of a warehouse for better access and analysis.
2. Data Processing
Most of the data that you collect is raw or unstructured. To enhance the accuracy of your analysis, you have to organize it properly before you proceed.
The high volume of data generated nowadays makes data processing more challenging as you might have an overwhelming number of data in your hands. However, there are two major methods that you can implement to simplify your data processing—batch processing and stream processing.
Batch processing examines large data blocks over a long period. It's very effective when you have enough time on your hands to collect and process your data. Stream processing, on the other hand, examines data in small batches. It’s very effective when you need to make decisions faster, shortening the turnaround time.
3. Data Cleaning
Regardless of the volume of your data, you need to clean it to get rid of the dirt that could alter the accuracy of your results.
The cleaning process also involves proper formatting and removal of irrelevant information.
4. Data Analysis
Having gotten rid of the dirt in your data and formatted it, it’s time to analyze it.
Transforming your data into valuable information is time-consuming, but you can use some processes to fasten the process. The most common big data analyses methods include the following:
Data Mining
Data mining is the process of sorting large databases to identify existing patterns by looking out for anomalies and building clusters.
Deep Learning
Deep learning is the process of imitating human learning with the use of machine learning and artificial intelligence to find similar traits in complex and ambiguous datasets.
Predictive Analysis
Predictive analysis is the process of relying on the historical data of your organization to make future predictions regarding risks and opportunities.
5 Ways Big Data Analytics Prevents Cybersecurity Threats
The increase in data generation creates more opportunities for cyber threats. Cybercriminals are keen on compromising the high volumes of information that big businesses are churning out.
Although there is still a lot more work to do in creating safer cybersecurity, data analytics is putting up good defenses against cyber attacks. Let’s take a look at some of the ways that big data analytics prevents cybersecurity threats.
1. Predictive Models
Having observed cyberthreat patterns, you can create predictive models that trigger alerts the moment a pattern is observed within the entry point of your network.
Software services like AI and machine learning help ensure the accuracy of the cyber threat patterns used in developing predictive models. If a cyber threat goes beyond the touchpoint of your network, your system can counter it with a real-time defense response.
2. Monitoring and Automation at Scale
Employees’ ignorance of healthy cybersecurity practices causes a large degree of cyberattacks in many organizations. If your employees don’t know what to do to avoid an attack, they might do things to help attackers get into your network.
While building a healthy cybersecurity culture in your organization is highly recommended, you can also use big data analytics to monitor the activities of your employees to detect vulnerable activities. If someone takes an action that puts your network at risk, the system responds to limit the effect.
3. Real-Time Intrusion Detection
One chance is all an attacker needs to bring down your network. It’s your responsibility to ensure that they never get that chance.
Implementing intrusion detection systems is a great way to stop an attack before it gets close to your network’s point of entry. With big data analytics, you can automate this kind of process at scale.
Real-time analysis of data generated by intrusion detection systems exposes the details of an attack, showing you all the information that you need to stop it. You can integrate this data into your defenses on the ground to nullify an attack instantly.
4. Intelligent Risk Management
The more knowledgeable you have about possible dangers around your system, the stronger your defenses will be.
Big data analytics gives you concrete information about the activities in and around your network. An in-depth analysis of your cybersecurity data exposes the root causes of security breaches, giving you an overview of how those activities originated.
5. Threat Visualization
Big data analytics enables you to be proactive in your cybersecurity strategies by helping you make more accurate predictions about activities in your network.
If your organization has already suffered a cyberattack, you can use your big data analytics report to gain insights into the attack.
How did the attacker gain unauthorized access to your network? What points did they come in through? You can find answers to these questions and more from your data analytics reports to prevent a future recurrence.
Leaving No Room for Cybercriminals to Penetrate Your Network
Most cyberattacks happen due to human error and blind spots in a network. You are either unaware of an existing loophole or fall prey to malicious antics by cybercriminals.
Big data analytics brings vital information about the operations of your system to the fore. And, if you ever make a mistake in your operations, you can rest assured that there'll be a real-time response to save the day.
