Data science is shifting towards a new paradigm where machines can be taught to learn from data to derive conclusive intelligent insights. Artificial Intelligence is a disruptive technology that collates the intelligence displayed by machines mimicking human intelligence. AI is a broad term for smart machines programmed to undertake cognitive human tasks that require judgment-based decision making.
With all the hype and excitement surrounding Artificial Intelligence, businesses are already churning data in massive quantities over call logs, emails, transactions and daily operations. Machine learning (ML) is a dynamic application of artificial intelligence (AI) that empowers the machines to learn and improve the model accuracy levels. Machine Learning is categorised into deep learning, reinforcement learning based on the capability of machine learning algorithms to relearn from experience. Machine learning deploys several theories and techniques from Data Science, which includes, classification, categorization, clustering, trend analysis, anomaly detection, visualization and decision making.
Modern enterprises seeking to apply for AI advance ahead to digitally transform their business and operational models by leveraging on the following roadmap-
• Step 1: Brainstorm the right use cases
• Step 2: Set up analytics capability
• Step 3: Deploy machine learning algorithms
Enterprises need data science know-how to connect data pipelines to analytics and machine learning algorithms. Digital transformation and machine learning move together leapfrogging the entire digital transformation. ML assists to analyse complex data for intelligent insights based on that data to achieve seamless digital transformation.
Access to the right data and analytics tools can greatly enhance decision-making. Machine learning algorithms can process thousands of data points in real-time without the intervention of any human intervention to generate actionable intelligence, for instance, predictive maintenance leverages machine learning to input historical data into the models to analyse failure patterns before they even happen.
Building ML Algorithms of Scale
Machine learning algorithms learn from existing patterns and data at great speed, something that is impossible when it comes to handling by human-only teams. ML algorithms have an edge over data analysts who may take up months to analyse data, which ML platforms achieve in a mere matter of minutes.
Machine learning algorithms can analyse humongous inflows of data much more than a human could do to potentially help streamline and expedite several digital processes. Besides, machine learning models help to discover data patterns and relationships that may help an enterprise to channelize operational efficiencies and new revenue streams.
Artificial Intelligence has the potential to drive processes at scale to aid enterprises to make informed decisions. AI can be an important tool in the digital transformation process. However, organizations must gain a deeper understanding of use cases and analytics capacities to effectively leverage them for business and process transformations.
In a crux, what is most important for the business is to focus that digital transformation is more about embracing a holistic approach to bring business transformation. Digital transformation is about re-engineering and rebuilding business operations in the era of IoT, analytics, Machine Learning/AI, and cloud, to improve processes, decision outcomes and customer experiences. This explains why Machine Learning is more relevant to Digital Transformation to automate time-consuming tasks to usher an era of innovation.
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Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.
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