Over the next few posts, we will be talking about the progression of Data Analytics — where we are today and where we are headed next. But, first, we start with some history. With basic statistics being the foundation of Analytics, the use of Analytics dates back to the 1900s, which began receiving significant attention in the late 1960s when computers became decision-making support systems.
Data analytics has dominated almost all the industries of the world, and data collection has become an integral part of any organization. These days every click or scroll you do, and every time you open an app, huge amounts of data are being generated and stored for business intelligence and data mining.
Various industries like finance, banking, transportation, manufacturing, e-commerce, and healthcare, use this data to make smarter decisions, gain meaningful insights and predict outcomes. Today, businesses are increasingly using data science to uncover patterns, build predictions using data, and employ machine learning and AI techniques.
For example, the Banking industry uses data analytics in credit risk modeling, fraud detection, and evaluate customer lifetime value. Erica, the virtual assistant of Bank of America, gets smarter with every transaction made by studying customers’ banking habits and suggests relevant financial advice. Finance industries use machine learning algorithms to segment their customers, personalize relationships with them, and increase their businesses’ profitability.
Predictive analytics is another aspect of data science that has become necessary for the transportation and logistics industry. Public and private transportation providers use statistical data analysis to map customer journeys and provide people with personalized experiences during normal and unexpected circumstances. Logistics companies use artificial intelligence to optimize their operations in distribution networks, anticipate demand, and allocate resources accordingly.
Data science and AI in biomedical and healthcare data are modernizing the healthcare industry by providing public health solutions. From medical image analysis and drug discovery to personalized medicine, data analytics is revolutionizing patient outcomes. Data science and machine learning have revealed that there are solutions to the most difficult problems in different industries, and the future success of companies relies on their adoption of data-centric approaches to discover actionable insights. By automating the analytic process, the time value of unlocking insights can be accelerated to provide rapid forecasting and decision making.
“By 2020, 50% of analytic queries will be generated using search, natural-language processing or voice, or will be auto-generated.” – Gartner Analytics Magic Quadrant, 2019.”
We will discuss major challenges and opportunities in adopting various Data Analytics techniques for their businesses in next week’s post. Watch this space or follow us on LinkedIn to stay tuned.