Taking Businesses To The Next Level By Incorporating Analytics And Machine Learning
The world is changing at an astronomical rate and the amount of data we have access to now could not have been imagined by anyone decades ago. This connectivity has presented innumerable ways to generate and store data for analysis. Enterprises that deal with massive influx of data can greatly benefit from the application of machine learning and predictive analytics tools. From maintenance to risk management, predictive analytics is deployed in enterprises across departments. In this perspective, digital technologies such as machine learning and data analytics play a critical role in devising the right algorithm for the detection of the right end-user. According to a study conducted by Salesforce Research, using AI and machine learning has transformed customer engagement as per 83% IT Industry leaders.
Why analytics & ML-driven solutions have potential to transform enterprises?
To stay relevant Businesses have to continuously learn from user patterns and infer trends. This is where analytics and ML-powered solutions can be employed by enterprises. They help in analysing big data and facilitate market research and segmentation, understanding customer behaviour, personalising recommendations, understanding and predicting future trends for businesses, supporting decision making and interpreting data patterns. A recent report by Fortune Business Insights predicts that the value of the combined market of analytics & MLpowered application was at $8.43 billion in 2019 and is likely to reach $117.19 billion by the end of the year 2027- a CAGR of 39.2 percent.
Interesting insights help to cash in new business opportunities
Sectors across the economy including retail, manufacturing, financial services, healthcare, automobile, travel & hospitality, energy & utilities, media, and many more are using these applications to infer interesting insights that are helping businesses to drive business growth.
Banking and Finance sector
Machine learning and AI has significantly contributed to modernize banks and business while keeping the existing system intact. According to the Narrative Science and National Business Institute, Predictive analytics, recommendation engines and voice recognition are already in use for 32% of financial service executives. It has helped businesses associate with Fintech services to keep up with the demands of safety and security. Fintech enterprises are consistently using applications across channels on the available data to improve services and meet the customer’s needs. The analysis of this data then helps in mitigating risk management, improved marketing and preventing fraudulent activities. ML has also made customer service easier by introducing Chabot’s that take care of repetitive queries and learn from these interactions. According to a research by Accenture, banks will save $1 trillion by 2030 with the help of AI and the greatest amount of investments in AI will be made by companies in the financial industry. Credits can now be given based on the borrower’s credit score and can be personalized based on customer or business. The risk of frauds can be minimized as well. It has increased the overall efficiency by continuously evolving and documenting the internal process and updating as and when needed. According to a study conducted by Deloitte, Around 77% of customers prefer to pay with a credit card over cash. Digitization makes the process hassle-free thereby making it a safe and convenient method for customers.
“Fintech enterprises are consistently using applications across channels on the available data to improve services and meet the customer’s needs”
Public Services sector
Machine learning and analytics hold immense possibilities to improve government services ranging from traffic management to processing taxes to healthcare facilities. Public sectors can utilise AI for citizens facing roles on city development, justice and policing revenue and administration. Using predictive analytics solutions can perform tasks that involve lots of data, complicated calculations or tasks with clear rules with ease which presents great options for automation in this sector. The sector has already been using Chabot’s to answer queries of the citizens based on the data collected over decades. The applications of these solutions are growing as a lot of governments across the world are adopting AI and ML. It is used to take decisions on welfare payments, fraud detection and plan new infrastructure projects. The public sector can improve both their efficiency and effectiveness by creating an ecosystem for tech based companies to associate and provide solutions. A major application would be for data security. 1% of today’s organizations report they spend more on machine learning for cybersecurity than they did two years ago. (Source: Webroot) and this trend is gaining prominence across sectors.
In the retail sector, both online and offline retailers are extensively using analytics and ML-powered solutions for inventory planning, buying behaviour, region-specific insights, and targeting, cross-selling of products, new product development, and many more. By 2023, more than 33% of large organisations will have analysts practicing decision intelligence, including decision modelling (source: Gartner) and the retail sector can highly benefit from this. Not only online retailers, but offline retail chains are also gauging various metrics related to consumer demand through analytics and ML-powered solutions.
Healthcare & life sciences sector:
Healthcare and life sciences sector has been witnessing an increased use of ML-powered solutions. These are being used for real-time diagnostics from patient’s data, disease identification, proactive health management, and risk classification among others. Deep learning employed in these processes is the most prominent segment of the Machine learning market which is expected to reach almost $1 billion by the year 2025. (Source: finances online).
The world of manufacturing is going through a wave of automation and analytics and ML-powered solutions are at the forefront of this automation drive. From predictive maintenance to condition monitoring; from process optimisation to demand forecasting; algorithms designed with the help of these digital technologies achieve it all. As cloud service providers such as Google, Microsoft, IBM, and AWS among others come up with customised applications powered by these technologies the global machine learning market is projected to grow from $7.3B in 2020 to $30.6B in 2024, attaining a CAGR of 43%, according to Market Research Future.