Defining Data Analytics with Machine Learning
75% of business pioneers state ‘growth’ as the key source of value from analytics but only 60% of those pioneers have predictive analytics abilities. So what’s preventing the businesses from accomplishing predictive analytics capabilities? The major roadblock is applying the right set of tools, which can pull powerful insights from this stockpile of information. Using Machine learning and Artificial Intelligence tools , businesses can optimize and reveal new statistical patterns which form the backbone of predictive analytics.
This is time; organization must adopt more on to automated analysis than the traditional analytics approach. As Machine Learning and Artificial Intelligence landscape, automated predictive analytics is finding its way into more business use cases. As quoted by IBM, When comes to security analytics, “Machine learning can reduce the white noise, but without an injection of security-relevant data, it has a long way to go before it can be considered a generational leap in analytics”. The next generation of machine learning-based security analytics will undoubtedly have a heavy focus on data acquisition. Using ML, businesses can anticipate customer churn, generate customer delight, prevent customer exhaustion and fraudulent transaction and improve the company’s ROI.
Machine learning progressions such as neural networks and deep learning algorithms can discover hidden patterns in unstructured information sets and uncover new information. But building a comprehensive data analysis and predictive analytics methodology, requires big data and dynamic IT system.
Today machine learning algorithms have become popular in the field of business analytics because industry experts believe that it’s an excellent technique of solving complex-data-rich business problems that are not resolved by the traditional approaches like human judgment or software engineering.