Data analytics is a concept that has been around for decades. Bankers and traders of the past used handwritten spreadsheets to analyze client behavior and predict market trends. But as the years have passed, technology development has provided us with a more thorough and efficient take on data analytics. We’ve come to the age of big data, which has become one of the most significant issues for businesses of all kinds. But why is it so important? Read on to find out why you should care about big data analytics.
What is big data?
Big data is a term that refers to the enormous amounts of data that are processed and analyzed by organizations of all sorts to create significant value. It is a combination of structured, unstructured, and semistructured data coming from various sources. To understand what big data is, we can characterize it by the six Vs.
- Volume
This is the most prominent characteristic of big data. It’s the nature of the data stored in big data environments to occur in extremely high amounts. It contains all the data you could possibly think of - from online transactions to weather reports. It’s hard to imagine what amount of information is generated daily by machines, their sensors, and regular internet users. For example, in 2021, 79 zettabytes of data were generated.
- Variety
Big data analytics require different types of data. They can be divided into three categories: structured data, such as financial records or login times; unstructured data, which means text or multimedia files; and semistructured data, e.g. streaming data from sensors or HTML files. All these different types of data need to be stored and processed together.
- Velocity
The data is collected and processed at an extremely high speed. Often big data analytics happens in real-time or almost real-time, bringing the most up-to-date insights and allowing for making the best possible decisions.
These three characteristics are the most important features of big data, but several more Vs can be added to extend the definition.
- Veracity
The trustworthiness of data is crucial in big data analytics. It ensures there are no data quality issues. False or inaccurate data leads to errors which make the whole analytics process pointless because then it leads to invalid results.
- Value
For big data analytics to be useful, there needs to be some potential for creating value. A significant part of the collected data holds no value at all, so it is necessary to confirm which data elements relate to essential business issues.
- Variability
This characteristic is different from variety, as it refers to the fact that the data is constantly changing. This ties in with the velocity factor, meaning there needs to be an instant reaction to shifts in the data to extract valid, useful information from it.
How big is big data?
To summarize, big data is data that is too large to be processed by traditional data management systems. It’s impossible to describe precisely how big it is, but big data deployments involve terabytes, petabytes, or even exabytes.
To understand how large amounts of data are processed in big data analytics, we should take a look at where the data comes from. A significant part of it is user generated, including the data coming from medical records, emails, mobile apps, and social media platforms. Over 45% of people use at least one of these, so you can only imagine the amount of information gathered from social media constantly all around the world.
Another source of big data is machine-generated data, for example, the information collected by IoT devices or by manufacturing machines’ sensors, network and server log files. There are also transactional data and financial records. External data is also collected: weather and traffic conditions, financial market reports, scientific research, geopolitical events, basically anything that can be processed and correlated to bring valuable insights.
Why is big data important?
We’ve all heard by now that knowledge is power. And what is an enormous database other than knowledge? Big data is a source of precious information about your customers, their needs, behaviors, preferences, when they do certain things, and how different factors affect their choices. Big data analytics is the power of knowledge.
Big data processing can be used by scientific researchers, especially in medicine. It allows researchers to identify risk factors and prevent and diagnose severe illnesses sooner. Information from online health records can also be used to avoid an epidemic outbreak, as the threat would be recognized early on.
The information collected from big data can be used to improve operations, for example to optimize factory production based on the data from manufacturing machines. A financial organization would use big data to improve risk management in its systems, and a transportation company would plan the most efficient supply chain. Government organizations can use the information from security cameras and weather reports to plan urbanization in the best way possible, with many cities benefitting from data driven design - from Chicago to San Francisco.
Big data is also useful in UX and UI. You can read more about data-driven design on our blog.
Another significant benefit is creating personalized marketing campaigns based on consumer behavior. Using historical and real-time data to analyze preferences allows marketers to make better-informed and, therefore, more profitable decisions.
“More profitable” is the clue to the benefits of big data analytics. Big data is turned into information, which helps us understand how the world around a specific business works. This knowledge allows companies to make well-informed decisions, leading to lower operation costs, better customer experience, and more profit and efficiency. Data-driven innovation is the key to success in the modern-day business environment.
How do big data analytics work?
Once we know what big data analytics are and why they are crucial, it’s time to understand how they work. There are several technologies that allow for managing and processing enormous amounts of data. Big data tools work together to collect, store and analyze the data to provide valuable information. Here’s a list of technologies that make big data analytics possible.
Computing in the cloud
Cloud computing provides the efficiency and scalability necessary for processing big data in a subscription-based model. This makes big data analysis accessible without owning expensive hardware. Cloud computing also provides great scalability, which is another plus. Organizations of all sizes can use this technology.
If you're interested in cloud computing, check out our articles on AWS cost optimization and Microsoft Azure cost management.
Data mining
Data mining is a vital part of big data analytics. It is the process of sorting large amounts of data, which allows for identifying patterns and relationships. Based on that, future trends can be predicted. This process highlights the relevant information hidden in large data sets, which makes decision-making more efficient.
Data management
Big data analytics is not only about processing data - it also needs to be managed. Data management is the process of governing and categorizing data. This allows maintaining a high-quality, reliable database despite the constant flow of new information.
An important part of data management is data lineage, which means recording all the transformations that data went through while being processed. Data lineage lets data engineers and business stakeholders know where the data came from and how it’s changed and ensures the veracity of the data and validity of the outcomes.
Data storage
Systems able to store vast amounts of data are necessary for big data analytics. They should allow for easy access by data scientists and ensure data security. It’s important to remember that various types of data need to be stored, which is why companies usually use complementary data storage methods - databases or data warehouses. The first one stores data in its raw form, for example, video or voice data. The second one is for structured data and analytics.
In-memory analytics
In-memory analytics means the data is analyzed using system memory instead of a hard disk drive. It allows for removing latencies and increasing efficiency. Analytics scenarios can be run in parallel and iteratively, making the whole process agile. This ensures that decisions are made based on real-time data analytics.
Machine learning
Machine learning is a subset of AI crucial to big data analytics. It is responsible for training machines to produce models for complex, large database analytics. This automates big data processes and, as a result, makes them more efficient. When correctly designed and trained, this big data tool allows for accurate and quick results even on a large scale.
Predictive analytics
Predictive analytics is an important part of data science. It means using historical data, machine learning, and statistical algorithms to define the possibilities of future outcomes. It provides organizations with knowledge of potential future scenarios so that they can be prepared and make accurate decisions.
Text mining
Text mining is a method that uses machine learning and natural language processing to analyze data from raw text, for example, comment sections, books, Tweets, blogs, emails, and others. It searches through the text files and allows discovery of the opinions of customers and potential customers and identification of the topics being discussed. For example, using text mining can give you information regarding the sentiment towards a certain product or company.
Why are many organizations turning to NoSQL databases to manage big data?
Big data management is a complex process that requires specific solutions. This is why most organizations consider using NoSQL (not only SQL) databases. Unlike traditional data management tools, such as relational databases based on SQL, they can process structured and unstructured data. They are also usually out-of-the-box adjusted to working with the cloud.
Moreover, where traditional, relational databases focus more on data consistency and handling well-structured data, new approaches favor flexible ways of storing pieces of information and achieving a common state between data copies (“eventual consistency”) and therefore increasing processing velocity. In the big data era, the NoSQL solutions simply tend to be better suited to modern use cases.
That being said, it is also true that the devil is in the details and while choosing their desired solution, organizations need to carefully analyze a given business scenario and pick an approach tailored to their specific needs.
Big data technologies at CodiLime
At CodiLime, we stay up-to-date with current business needs. This is why we provide big data analytics services using various technologies to ensure a personalized solution for each client. We use public and private cloud environments, such as AWS, Microsoft Azure, IBM Cloud, RedHat, and OpenStack. For database storage, we use SQL-based as well as NoSQL solutions, e.g. PostgreSQL, MariaDB, Elastic Stack, MongoDB, Cassandra and Snowflake.
Big data, big challenges - how to avoid them?
Due to the nature of data science, the size of databases, and their sources, some issues can arise for organizations deploying big data analytics. First, it is challenging to design an architecture allowing one to store, manage, and process data while keeping it secure. Big data analytics requires a tailored system personalized to each particular organization’s needs.
Another challenge of big data is making it accessible to data analysts. Environments that include different types of data, especially unstructured data, make it difficult to find what one is looking for. It is necessary to build data catalogs, including functions that categorize the data sets and make them more accessible. This makes data analysis by humans more efficient.
There is also an issue of privacy and data security. Data breaches and misuse are real fears for many users, so there are strict laws regarding data storage in the EU - the General Data Protection Regulation. It grants citizens the right to be forgotten and requires consent for collecting personal data. It also regulates the types of data that can be stored. In the US, there is no such law - only the state of California gives consumers some control over their data collection and use. However, the California Consumer Privacy Act only applies to the companies that do business in the state. Other states, such as Virginia and Colorado, plan to implement their own privacy laws, taking effect in 2023.
At CodiLime, we are aware of the importance of data security. You can read about the security standards at CodiLime on our blog.
Conclusion
To summarize, big data analytics is a source of knowledge that can potentially put any business ahead of their competition. There needs to be an efficient data management system to be able to get valuable results. Such a system should consist of various big data tools and answer specific business needs.
Big data, when used wisely, can improve operations, lower business costs, and help gain customer insight. It’s a tool that’s used by more and more companies. Implementing big data analytics in your organizational strategy is a way of taking your business into the future.
