Adopting effective IT solutions to stay competitive in today’s digitalized world is important, big data analytics lies at the heart of any organizations’ decision-making to drive innovations and further enhance their business performance. According to Forrester, data-driven organizations are 58 percent more likely to exceed sales targets than non-data-driven companies. The challenges of deploying big data analytics are in how to tackle with a lot of data efficiently to serve for both accurate and insightful decisions. AI and machine learning solutions can help organizations overcome these and turn their troves of data into actionable insights by leveraging techniques like algorithms and computer science, which make it as a very necessary implementation to maximize big data potential in analytics.
Various beneficial ways AI and machine learning applications can be leveraged for big data analytics
When it comes to big data analytics, businesses are facing problems of handling extraordinarily huge volumes of structured and unstructured data, which cannot be solved by using traditional approaches. Machine learning applications can efficiently translate big data into useful insights for business decision-making. Specifically, big data turns out to be advantageous for machine learning systems since the more data a system receives, the better it functions for analytics.
AI and Machine learning applications can now perform automation, which minimizes manual tasks and further mitigate the resources needed for an analytics process. Such tasks like building analytics models and making predictions and generating insights can be automated, which may take up weeks or months if leveraging traditional approaches. Machine learning automation can make your analytics get increasingly more accurate at prediction without being explicitly programmed to do so.
There are 3 techniques in machine learning to help you decipher valuable insights from the complexity of big data:
- Clustering: Different types of data are grouped according to the similarities, such as buying behaviors, are similar.
- Elasticity: Results are deduced by answering the question “Which factor is responsible for which outcome when numerous factors are changing at the same time?”
- Natural Language: Pertinent facts are used to feed the decision-making process. Thanks to this approach, you are not required to comprehend technical issues in order to conduct in-depth analysis. You may just ask the computer ordinary queries like "What are consumer habits on our e-commerce store?” and the system will interpret those terms.
Use cases of machine learning in big data analytics
The primary role of Machine learning applications in big data analytics is to enhance its capabilities in delivering quicker and more accurate knowledge information for decision-making. Let’s look at some practical use cases of machine learning that bring in positive impacts on your business model:
- Enhance Marketing Analysis: With machine learning services, tasks like market research and segmentation, as well as consumer behavior exploration, can be completed timely and correctly. As a result, you may gain a deeper understanding of your customers' insights and develop a solid plan to boost your entire business performance and earnings.
- Improve Customer Experience: Machine learning solutions can assist firms in personalizing their customer services. Fueled with big data, machine learning can work as a recommendation engine. It blends context with predictions of user behavior to impact user experience depending on their online activity. As a result, businesses will be able to give helpful suggestions that customers would find intriguing.
- Fraud Detection: Machine learning can learn from complicated data patterns to timely and correctly identify and halt a wide range of fraud attacks and crimes. This can be accomplished by employing advanced decision models to reduce false positives and discover network linkages in order to provide a comprehensive picture of fraudsters' and criminals' behavior. Even with the increasing sophistication of threat actors, machine learning applications can be a comprehensive technique to detecting aberrant activity in real-time.
- Predictive Maintenance: Manufacturing companies often use preventive and corrective maintenance procedures, which are frequently costly and inefficient. Organizations in this area, however, may utilize machine learning services to identify significant insights and patterns concealed in their manufacturing data. This can be termed as predictive maintenance, which helps to reduce the chances of unexpected breakdowns, while also eliminating wasteful costs in the operation.
Define the right direction for machine learning solution implementation
A strong corporate leadership is an essential element of machine learning solutions due to the first stage in a successful machine learning implementation is to identify business objectives and goals. Based on the business understanding and objectives, you can proceed to collect the right data for analytics. Be mindful that feeding your machine learning models with high data quality is critical, so good data management has to be in place for any advanced analytics projects. Essentially, developing a data-centric company culture among your employees can help you prevent your staff from formulating habits of acting on hunches, which can enable your business to consistently make data-driven decisions across your organizational functions and achieve the objectives of business growth as a result.
Machine learning applications are an important technology for big data analytics, which guides your business to attain revenue goals. Adnovum can help you qualify your big data management and successfully implement AI and machine learning solutions customized and compatible with your business demands and circumstances.
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