Big data, artificial intelligence (AI) and machine learning (ML) have changed the business landscape over the last few years especially in financial services and now are impacting many industries at an alarming rate. According to Edge Delta, by 2023 97.2% of companies have invested in big data and AI, signaling widespread adoption. Numbers may vary but GMI research says the big data market which was $170 billion in 2022 will reach $378 billion in 2030 growing at 10.5% CAGR between 2023 and 2030.
This will happen because these technologies are becoming more and more important for decision making, personalization and risk management. By looking into the industry specific applications, technologies and strategic reasons for them companies can use these to their advantage.
Evolution and application of big data, AI, and ML
Big data, AI and ML have changed many industries. The amount of data globally has exploded over the last 10 years with the rise of IoT devices, digital transactions and cloud computing. In 2020 the total amount of data created, captured, copied and consumed globally was 64.2 zettabytes and will be 180 zettabytes by 2025.
This has made big data, ML and AI a necessity for companies to get insights from big data. Real time analytics and better data visualization tools are key business intelligence strategies. NLP and predictive modeling are key to extracting information from big data.
While big data, AI and ML are trending in many industries, these sectors have seen the most benefits:
- Retail: Retailers use these for inventory management, customer behavior analysis and personalized recommendations. They can maximize their stock levels by analyzing browsing and buying behavior and offer targeted product recommendations.
- Healthcare: Big data and ML for predictive diagnosis, patient data analysis and therapy optimization. These help healthcare professionals to improve operational efficiency and patient outcomes.
- Financial Services: Banks and other financial institutions use big data and ML for credit scoring, fraud detection, personalized product offers and investment strategies1. Banks can analyze customer credit scores and spending patterns to offer creditworthy customers personalized loan products or higher credit limits.
- Insurance: Insurance companies use these for fraud detection, claims processing and risk assessment. Claims department can simplify processes and detect fraud, underwriters can use ML to predict risk more accurately.
- Manufacturing: Big data and ML for predictive maintenance and supply chain optimization in the industrial sector, to reduce downtime and increase operational efficiency.
Key technologies and tools
Robust tech is the foundation of big data, ML and AI. Some of the most popular tools are:
· Apache Spark: . Renowned for speed and scalability, Apache Spark is a potent engine for big-scale data processing. It facilitates ML techniques for effectively analyzing enormous amounts of data.
· Kafka: Open source stream processing tool for real time data streams. Companies can control and examine data as it’s being generated.
· Hadoop: Widely used for distributed storage and big data processing, Hadoop lets companies store enormous volumes of data on many servers.
· Cloud: Amazon AWS, Microsoft Azure, and Google Cloud provide scalable infrastructure for big data operations, enabling organizations to readily modify their storage and computing capacity as their data needs develop.
Implementation strategies for businesses
Companies need to implement big data, AI and ML through a systematic approach that aligns with company goals and gets the most out of their spend. This involves key steps.
First, identify use cases and set goals. Before investing in new technology, find internal use cases within the company, such as business opportunities that can benefit from better data analytics and machine learning. According to an IBM survey, 35% of companies are already using AI and 42% are exploring AI. This is a growing trend of using AI to drive business outcomes.
Then evaluate the current infrastructure to see what new technologies are needed. Review the current IT stack, including analytics tools, data storage and processing power. Based on this assessment, what additional systems or upgrades will be required to support ML projects and big data initiatives.
Given the sensitivity of big data, especially in finance and healthcare, it’s essential to carefully consider security needs and compliance requirements. This includes internal security policies, industry standards and data protection laws.
Organisational buy-in is key to any digital transformation project. Present a full strategy to management, including project goals, required tools, benefits and risks. A cost-benefit analysis and expected ROI will get leadership buy-in. For example, a Deloitte study found that companies using AI and big data saw 15-20% operational efficiency gain. Once the strategy is approved, get all necessary clearances, including security, network and compliance permissions. This stage ensures the project meets all organisational and legal requirements.
Based on the plan, procure the required hardware and install the software, including ML systems, cloud services and big data processing technologies like Hadoop or Spark. Proper training is key to getting the most out of the new technology and acceptance.A McKinsey report found that companies investing in training programs for AI and ML saw 25% productivity gain for employees.
Run detailed training sessions for employees who will be using or handling the new systems. Monitor and refine big data and ML systems through user and stakeholder feedback and adjust as needed.
As the technology shows value, consider rolling it out to other business use cases. This phased approach reduces the risks of large scale deployments. Keep stakeholders informed throughout the implementation phase. Regular updates on development, issues and progress will get management and end user buy-in and keep the project on track.
Impact on Customer Experience and Trust
According to a PwC study 86% of customers are willing to pay more for a better customer experience, so these technologies are key to customer satisfaction. Big data, AI and ML can help with fraud detection and risk assessment, faster and more efficient customer service, personalisation and targeted marketing. The data insights from these technologies help companies to understand and anticipate customer needs. AI powered customer service can reduce response times by up to 90% and that’s a big improvement for the customer.
AI can also personalise interactions by understanding customer behaviour and preferences which means more meaningful and engaging interactions. For example AI powered chatbots can handle common queries so human agents can focus on the complex stuff and that’s more efficient. 65% of customers expect AI to improve their overall experience so there’s growing acceptance of AI driven interactions.
Organisations need to value data collection and usage transparency to maintain customer trust. A Deloitte survey found 80% of consumers are more likely to trust a company that is transparent about data usage. Implementing robust data governance policies and adhering to data protection regulations is key to building and maintaining customer trust. Companies that are transparent don’t just comply with regulations they build stronger relationships with their customers and that means loyalty and long term engagement. And data security can mitigate risk and protect customer data which means even more trust.
Regulatory compliance and future trends
Some industries (financial services and healthcare to name a few) must comply with regulatory standards when using big data and AI. Key ones are data protection and privacy regulations like GDPR and CCPA, banking security acts, anti-money laundering regulations and data ethics guidelines. As these get more widespread new regulations will emerge; stay ahead of the curve.
Data will grow exponentially and we need more advanced and scalable solutions. To stay relevant in this fast changing world you need to be informed about industry trends and emerging technologies.
Key points
Corporate operations in many sectors are big data, AI and ML dependent. These can give companies a competitive edge, better customer experience and better decision making.
Using these becomes more and more important as data grows exponentially. Realizing big data is for insights, appreciating ML can do predictive analytics and identifying use cases before adoption will ensure success. Companies that use big data, AI and ML effectively will be ready to face future challenges and opportunities and thrive in a data driven corporate world.
