Optimising business decision-making with machine learning and data science
Businesses that fail to take full advantage of every digital tool available cannot reach their true potential. Data analytics, machine learning (ML), data science, artificial intelligence (AI), and AI analytics are some tools businesses use to make sense of a highly-valued commodity in today’s world — data. These technologies help businesses make structured analyses of large amounts of operational and historical data. Businesses use these findings to arrive at actionable insights and create projections to guide future strategic decisions. Let’s take a closer look at the trends and technologies that are making waves today.
Data science and data analytics
Data science is the discipline of extracting relevant knowledge and useful insights from large amounts of data. It is a set of processes aimed at identifying meaningful correlations between extensive collections of data, also called datasets. Data analytics is a branch of data science that focuses on analysing data sets to discover specific insights, identify trends, and draw conclusions that help businesses form sound, data-driven strategies to achieve their targets and goals. Businesses use data analytics to streamline processes, optimise operations, guide decisions, boost profits, enhance efficiency, and more.
The most basic form of data analytics is descriptive analytics, which is the collection and summarisation of large amounts of raw data to provide information on past occurrences. This includes tracking variations in website traffic, sales figures, customer behaviour, market trends, profits, etc. Diagnostic analytics is a slightly more advanced form of data analytics and employs statistical techniques to identify the underlying causes and triggers behind observable patterns and trends within historical data.
Taking things a step further, predictive analytics aims to provide organisations with accurate predictions of future outcomes based on the analysis of historical data. Armed with these projections, businesses can make timely, strategic decisions and stay ahead of their competition. This leads us to prescriptive analytics — the most advanced form of data analytics — which provides businesses with data-driven recommendations based on the analysis of various possible outcomes to help them achieve their long-term goals. Prescriptive analytics provides suggestions and recommendations aimed at boosting profits, growing market share, increasing customer engagement, improving sales, and a lot more.
Artificial intelligence and machine learning
AI is an umbrella term for all technologies directed at developing machines that can replicate human intelligence and independently make accurate, unbiased decisions without human intervention. AI systems are capable of learning from experience and rely on the tenets of intentionality, intelligence, and adaptability to help businesses speed up the decision-making process. The ability to swiftly analyse large amounts of data in real time has made modern AI analytics indispensable to leading organisations across various industries.
ML is a subfield within AI that deals explicitly with developing computers that automatically learn, deliver predictions, and churn data without a task-specific programming. ML systems build models based on algorithms, and these algorithms are constantly being refined and optimised as the system receives new data. ML algorithms analyse input data to predict possible outcomes and continually learn from the historical data that is being analysed. The system will make increasingly accurate predictions as it encounters and learns from new datasets.
Data science versus AI
Data science and AI are both fields of computer science used by businesses to analyse and leverage large amounts of data generated in today’s digital world. Data science focuses on extracting actionable insights and drawing conclusions from large amounts of raw data using statistics and scientific methods. On the other hand, AI is a group of intricate algorithms that mimic human intellect and have the ability to automatically solve particular issues based on knowledge and experience gained from input data.
For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like living organisms will be imperative for business excellence. A comprehensive yet modular suite of services is doing precisely that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.
How can Infosys BPM help?
To get the most from their data, businesses need a comprehensive data analytics solution. Infosys BPM Analytics Services applies domain expertise, cutting-edge technology, and a metric-driven approach to help organisations extract, analyse, and leverage data to attain their goals. Reach out to explore how Infosys BPM can give your organisation a competitive edge by integrating analytics and intelligent insight generation into your business processes.