Big Data Analytics Combines With Machine Learning - What Is The Outcome?
The reason behind the success of many large technology companies is no longer a secret: big data. But as more and more companies use it to store, process and extract value from their vast amounts of data, the use of collected data in the most effective way is becoming more and more challenging for them.
This is where machine learning technology jumps right in. Data is a perk of a machine learning system. The more information it receives, the more patterns it can learn to improve the business’s performance. Therefore, using machine learning technology to analyze big data is a potential logical step for companies to maximize big data analytics adoption.
Create a sense of big data
When we mention big data, we refer to vast structured and unstructured data sets that cannot be processed by traditional methods. Big data analytics helps to make these data sets understandable by exploring trends and patterns. Machine Learning can make use of the big data analyzing process using decision-making algorithms. It can classify incoming data, identify patterns, and transform the data into useful business operations insights.
Compatible with all elements of big data
Machine learning algorithms are beneficial for data collection, analysis, and integration in large companies. They can be executed in all elements of big data operations, like data labeling and segmentation, data analysis, and scenario simulation.
Here are some examples of how machine learning algorithms can be used to analyze big data:
- Conduct market and segmentation research: All businesses need customers, and understand their target audience has become the top priority tasks of any companies that want to succeed. Thus, conducting market research and segmentation research is a vitally important task to dig deeper into the potential and current customers’ minds and gain valued insight. By using supervised and unsupervised algorithms, machine learning can accurately interpret the target audience’s patterns and behaviors. The media and entertainment industry are utilizing machine learning to gain the audience’s preferences to generate content that catches their attention.
- Exploring customer behavior: After helping you conduct market and segmentation research, machine learning will continue to help businesses explore their customer behavior and establish a solid framework for their customers. This is called user modeling, which is a direct result of human and computer interaction. It uses data to capture users’ thoughts and enables companies to make informed decisions. Tech giants like Facebook, Twitter, or Google rely on user modeling systems to understand their users to make relevant recommendations fully.
- Personalizing recommendations: Nowadays, people expect to receive personalizing customer experiences. Whether it’s a smartphone or network series, businesses need to establish strong connections with their customers to provide relevant and personalizing experiences. Therefore, combining the power of big data and machine learning, companies can see their user behavior predictions accordingly to one specific context; consequently, they can make accurate recommendations, make adjustments at the right time, and enable customers to find exciting recommendations. Netflix is a typical business that uses machine learning-based recommendation systems to suggest compatible shows to viewers.
- Forecasting trends: Using machine learning technology to analyze big data, we can learn future trends and forecast them as a business. With the help of interconnected computers, machine learning networks can continually learn new things independently and improve their analytical capabilities every day. This way, it calculates data and uses past experience to shape the future like an intelligent system.
- Helping to make decisions: Machine learning uses a time series analysis technique, which can analyze a set of data together. It is an excellent tool for summarizing and analyzing data, allowing managers to make future decisions more quickly. Companies, especially retailers, can use this ML-based method to predict the future with great precision.
- Decoding patterns: In industries where understanding customer patterns and behavior can make significant breakthroughs, machine learning is an indispensable factor in solving big data. Taking the healthcare and pharmaceuticals industry as an example, this industry possesses vast data needed to be processed. Machine learning technology enables hospitals to better manage patient services by analyzing past health reports, medical reports, and other medical treatment histories. In the long run, all of these can boost better health diagnoses at healthcare centers and improve medical research evolution.
How to effectively transit to machine learning
The transition to machine learning can be a giant leap for any company. The change needs redefining workflow, architecture, data collection, storage, and analytics, and other modules. The scale of a system should be assessed and communicated to appropriate stakeholders.
Although it may sound cliché, the step-by-step approach is best suited for such a transition. First of all, companies need to establish a strong AI and ML-based strategy to keep pace with their business goals. Second of all, companies have to remember that quality data is critical to reaching the full potential of machine learning tools. A corporate culture around data should be created since the right person with the correct data can make a big difference. Lastly, time is of the essence, and companies need to act quickly.
Since the amount of data continues to increase over time, collecting and managing them has become challenging for many businesses. If you want to make the most out of your big data, we recommend implementing machine learning as a technology enhancement for big data analytics. This considers a smart move.