What is Big Data Analytics
Big data refers to a set of data that is too big for a traditional relational database to capture and process. This data is not only large in volume, but also in velocity and variety, and includes data generated in real time from sensors and other devices, video or audio, networks, applications, websites, social media and more. Big data analytics, then, is the set of advanced analytics techniques required in order to properly analyze these large and complex datasets that are becoming more and more popular with the rise in artificial intelligence (AI) and other technological advances.
The purpose of the big data analytics process is to uncover patterns and trends in the raw data in order to assist stakeholders in making informed decisions. These processes use a combination of traditional statistical analysis techniques that can now be applied to larger datasets as well as newer methods based on machine learning and AI.
How Does Big Data Analytics Work?
There are four steps in the big data analytics process: collecting the data, processing it, cleaning it and analyzing it:
Technological tools facilitate the collection of data from a wide variety of sources – from mobile apps to IoT sensors and more – which is then stored in data warehouses. Different analytics solutions can then access the data as needed.
It is especially important for large and unstructured datasets to be processed and organized before being analyzed to ensure the most accurate results. Data processing is becoming more complex the more the amounts of available data grow. Some companies rely on batch processing which deals with large blocks of data over time, while others prefer to use stream processing which focuses on smaller batches of data. Stream processing is more complicated and therefore more expensive, but has a faster turn-around time which can make a difference when decisions need to be made quickly.
Regardless of the size of the dataset, it must be “scrubbed” in order to remove duplicate entries, make sure all formatting is correct and fix any other errors. Having a clean dataset is crucial to ensuring high data quality and accurate and relevant results. As the saying goes, “garbage in, garbage out” – if the data that is being analyzed is not of good quality, the resulting analytics will not be accurate and can lead to poor decision making.
Once the data is finally prepared and ready, it is time to begin the analysis process. There are many different analysis methods, including:
- Data mining – the identification of anomalies and creation of data clusters in order to identify patterns and causal relationships.
- Predictive analytics – the use of historical data to predict what will happen in the future.
- Deep learning – AI and machine learning-based algorithms look for patterns in complex and abstract datasets.
Advantages of Big Data Analytics
- Improved decision making – huge amounts of data can be analyzed quickly, providing new insights leading to better, data-driven decisions.
- Increased efficiency – easily identify trends and patterns that reveal where the inefficiencies in the business lie so that action can be taken to address the challenges and improve efficiency.
- Better product development – use data to respond to customer needs and wants and improve product offerings.
Challenges of Big Data Analytics
- Accessibility – the more data that exists, the harder it can be to collect and process. The data is worthless if relevant employees do not know how to access it or analyze it, so it is important to account for any additional training that may be needed.
- Quality – a lot of time and effort is required to ensure that the vast amounts of data are kept “clean” without duplicates, inconsistencies or other errors that can compromise the quality of the analytics.
- Security – it can be a challenge to protect the privacy and security of huge amounts of data. Companies must make sure they are in compliance with all regulations and have protections in place.
Big Data Analytics in Manufacturing
A treasure trove of data is present throughout the entire production line in a manufacturing company. A solution like Matics can collect, process and analyze the data from a wide range of sources and provide the actionable insights needed to prevent downtime and increase productivity.