Enhancing data analytics with machine learning and AI
How are some of the world’s largest data analytics providers utilising machine learning to enhance their offerings?
Recent research has shown that companies which use analytics for decision making are 6% more profitable than those that don’t. Harnessing analytics within business operations can benefit companies in a number of ways, including the capacity to be proactive and anticipate needs, mitigate risks, increase product quality and personalisation and optimise the customer experience.
As a result of these benefits, the technology industry has seen giants such as Microsoft, Amazon and IBM ramp up their investments in Big Data with the sector expected to reach over US$273mn in value by 2023.
What is machine learning and how can it be applied to data analytics?
IBM describes machine learning as a form of artificial intelligence that enables a system to learn from data rather than through explicit programming.
“As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine-learning model is the output generated when you train your machine-learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model,” explains IBM.
This technology is used to improve the accuracy of predictive models. Depending on the business problem, there are four approaches to harness machine learning alongside data.
Supervised learning typically starts with an established data set and an understanding of how the data set is classified. Supervised learning finds patterns in data which can then be applied to an analytics process.
This approach is used when a problem requires a mass amount of unlabeled data. Understanding the meaning of this data requires algorithms to classify the data based on patterns or clusters it finds. This form of learning is conducted without human intervention.
Reinforcement learning is a behavioural learning approach. By receiving feedback from the data analysis, this algorithm guides users to the best outcomes. However, reinforcement learning is different from other types of learning due to the system not being trained with a sample data set, instead the system learns through trial and error.
A deep learning approach incorporates neural networks in successive layers to learn from data in an iterative way. This form of machine learning is particularly useful to learn patterns from unstructured data. Deep learning’s complex neural networks are designed to emulate how the human brain works.
“Machine learning offers potential value to companies trying to leverage big data and helps them better understand subtle changes in behavior, preferences or customer satisfaction. Business leaders are beginning to appreciate that many things happening within their organisations and industries can’t be understood through a query. It isn’t the questions that you know; it’s the hidden patterns and anomalies buried in the data that can help or hurt you,” comments IBM.
Leading technology and data analytics companies and how they are utilising machine learning and AI to enhance their data analytics offerings
IBM’s offerings for data analytics can be broken down into five key areas: modernise, collect, organise, analyse and infuse. Its portfolio of services can help organisations accelerate its digital transformation journey with the adoption of AI and machine learning technology.
To accelerate innovation within any organisation, IBM offers IBM Cloud Pak for Data and IBM Cloud Pak for Data System V1.0. The two offerings provide a secure environment for its users to collect, organise and analyse data.
Within these environments, users can harness machine learning capabilities to drive increased value from data with the ability to run in-database machine-learning models, using tools and advanced algorithms.
“Make your data ready for AI” - IBM
Built for robust performance, IBM Db2 on Cloud is designed to provide a high-availability option with a 99.99% uptime service-level agreement (SLA). This solution harnesses an array of innovative technology, but specifically harnesses machine learning for the simplification of AI development.
“Make your data simple and accessible in an AI-driven, multicloud world” - IBM
With IBM Watson’s Knowledge Catalog, organisations can activate data for AI and analytics via intelligent and collaborative cataloging. This technology is backed by dynamic data-access policies and enforcement.
IBM Watson uses machine learning to curate and shape analytical assets as well as drive productive use of their data quicker.
“Create a business-ready analytics foundation” - IBM
“Scale AI everywhere with trust and transparency” - IBM
Finally, IBM Watson Explorer, provides its users with the ability to explore and analyse structured and unstructured, interal, external and public content to discover trends and patterns to improve decision-making, customer service and return on investment (ROI).
“Operationalize AI throughout the business” - IBM
As part of its multitude of analytics offerings, Microsoft Azure has a dedicated service for enterprise-grade, machine learning service for building and deploying models faster.
With its offerings, Microsoft Azure strives to “accelerate the end-to-end machine learning life cycle” for data analytics, by empowering developers and data scientists with a wide range of products to build, train and deploy machine learning models faster. Microsoft Azure boasts its ability to accelerate time to market and to foster team collaboration with industry leading MLOps and DevOps, in order to innovate on a secure and trusted platform specifically designed for AI.
Amazon Web Services (AWS)
One of the biggest giants in the technology space, Amazon Web Services (AWS) offers an array of machine learning capabilities. In order to be successful when adopting machine learning technology, AWS stresses the importance of the right security, data store and analytics services working together.
AWS Data Lakes
A data lake is a central repository for storing structured and unstructured data at any scale, allowing organisations to run multiple analytic models. There are several key parts to a data lake one of which is machine learning, where models are designed to forecast outcomes and provide recommended actions to make better business decisions.
“More machine learning happens on AWS than anywhere else, with over 10,000 customers using Amazon’s machine learning services” comments AWS.