It may be a cliché, but the undeniable fact is that data is the oil of the 21st century. If we talk about big data, you can think of it as a really big oil field that is also very deep, almost too deep to make economic sense. The word almost is important - with the massive emergence of cloud services, machine learning and the Internet of Things, big data and the information it contains are a little bit closer again.
What is Big Data?
What is the definition of big data? We've already given you one in the perex. Another says that big data means large, diverse, and complex sets of data that are typically collected and stored from a variety of sources. This data is characterized by three main dimensions (sometimes known as the "three Rs"):
- Scale - big data is characterized by enormous amounts of information. It can include billions of records, transactions or messages.
- Variety (diversity) of data - big data includes various types of unstructured and semi-structured data, including text, numbers, images, video, audio, and other formats. This data comes from a variety of sources such as social networks, sensors, websites and internal enterprise systems. Because of its diversity, big data usually requires some preprocessing to even add the necessary metadata.
- Data velocity - big data is generated quickly and often in real or near real time. This requires the ability to process and analyze data quickly so that it can be acted upon in a timely manner.
Consideration is being given to adding two more dimensions, which are value and credibility. However, the intrinsic value of the data must first be discovered and determined, as well as the credibility of the data integrity.
Big data analysis
Big data allows us to get more accurate answers because it contains significantly more information. Data analysis is the key to obtaining them. Based on it, you can gain new insights and discover new connections. You can follow a number of methods and procedures:
- Deep analysis - involves using advanced machine learning algorithms and artificial intelligence to uncover patterns and connections in the data. This helps you predict future events and make decisions based on the data you analyze.
- Sentiment analysis - often used to monitor customer sentiment and emotions on social media. It helps companies understand how customers perceive them and how they can improve their products or services.
- Exploratory analysis - allows you to discover data without predefined hypotheses. Companies can use various visualization tools to explore data and find potential trends and patterns.
Regardless of what you're specifically collecting - customer information, product information, device information, etc. - the main goal is to add relevant data points to your master summaries and analytical outputs. This will then lead to more accurate conclusions. It is crucial to realize that there is also a difference in whether you are monitoring sentiment for all your customers or focusing on only the most relevant ones.
Big data contains a lot of sensitive information, so it's important to ensure it's protected. Below are some key elements of big data security:
- Data encryption - data should be encrypted in transmission and storage. This ensures that even if the data is leaked, it will not be readable by unauthorized persons.
- Roles and permissions - roles and permissions of employees and users need to be defined and managed to restrict access to data to only those with permissions.
- Activity monitoring - systems to monitor and detect unusual activity and threats should be actively used to identify potential security incidents.
- Updating security measures - companies should, in their own interest, regularly update their security measures and monitor for new threats and vulnerabilities.
- Data backups - regular data backups and the ability to restore data in the event of a disaster are key to maintaining data integrity.
→ Tip: You might be interested to know that backing up your data to the cloud has never been easier.
Big data in practice
To better understand how you can use big data analytics in particular, here are some examples:
- Customer experience - online stores use big data to analyse customer behaviour and personalise offers. They enable you to reduce customer dissatisfaction and proactively solve problems. For example, Amazon recommends products based on purchase and browsing history.
- Healthcare - Healthcare facilities can analyze patient records and sensor data to predict disease progression or improve care.
- Financial services - banks use big data to detect fraud and analyze markets. Algorithmic trading is another example where data is analyzed and then trading is automatically performed.
- Automotive - car manufacturers use data from sensors in vehicles to monitor their performance and for maintenance. This improves safety and reliability.
- IT marketing - big data combined with psychometric techniques was used by the now infamous and defunct Cambridge Analytica, which was able to predict users on social media with great accuracy and then manipulate them effectively. However, Cambridge Analytica's big data was obtained illegally from the then Facebook.
- AI and ML - machine learning is the process, applied AI is the output. Big data is used to train neural networks and AI models. Practical commercial outcomes are, for example, self-driving cars or optimised chips and processors.
→ Tip: Did you know that the end of classic processors as we know them today may be approaching?
There are many benefits to analyzing big data, but it is also important to ensure its safety and security. Medium and larger enterprises should invest in the technology and expertise that will enable them to work effectively with big data and get the most out of it. Contact us, our in-house data centre will guarantee maximum security and availability of your data.