Machine learning and application of Artificial Intelligence (AI) which enables the systems to automatically learn from experience without the need for explicit programming is automating the process of generating insights from data. With the massive amount of data getting generated day by day, it is impossible to extract useful information by the use of traditional analytics. Machine learning enables organizations to automate these tasks in generating insights. The four key steps that organizations need to address for implementing machine learning are:
- Source the data: This includes evaluating the various data types that can be used as inputs as well as the technology required to acquire these data. Data sources may include market research data, customer-provided information, databases, etc.
- Establish a trusted zone: Once the sourcing is done, data needs to be organized through a Single Source of Truth (SSOT), which is the concept of aggregating the data from numerous sources into a single location. The trusted zone must have a central repository where data from multiple sources are organized, documented data elements, and methods for handling exceptions in certain data elements as well. Moreover, the data store must be flexible and less prone to errors and failures. The data warehouses are now being hosted on cloud which provides additional benefits to organizations like cost reduction, higher availability as well as horizontal and vertical scaling. There is also a recent trend of using NoSQL databases which provide the flexibility of adding new data types according to the changes required and enhance the efficiency of storing unstructured and semi-structured data.
- Creating the ML Modelling Environment: The data from the SSOT is moved to an ML Modelling Environment which is designed to implement Machine Learning. ML Modelling generates various models for extracting insights from the data. The three components involved include modeling infrastructure, development tools, and DevOps. The various options available for modeling techniques are ready to use services such as text to speech, OCR (Optical Character Recognition), ML Workbench which are ML modeling environments with e prebuilt programming tools.
- Provisioning Insights from ML: Machine learning helps in the generation of useful insights that add value to the business. The insights are to either categorized as real-time delivery which is processed and generated within a short time frame and batch delivery which creates insights in groups or batches.
Thus with sound knowledge on Machine Learning implementation, organizations can make use of the data to generate valuable insights for their businesses.
