Machine Learning – The competitive advantage for your business

Artificial intelligence (AI), a scientific discipline that empowers machines to comprehend, learn and act, is transforming and reinventing how businesses operate. When implemented as part of a holistic data science strategy, AI helps organizations transform customer experiences, improve productivity, lower costs and create new growth opportunities. AI is often injected into modern applications to influence how humans act, such as recommending specific products or services to buy, or helping them perform tasks faster and more efficiently.

The business case for machine learning (ML) is compelling. A subset of AI that utilizes analytical models and adapts or “learns” from data, machine learning can deliver both incremental and game-changing results to organizations. A number of factors have converged to make ML a prerequisite for business success, including increased data-processing power, improved access to big data and advancements in the Internet of Things. Combined with improvements in algorithms and the ease with which ML-enabled applications can now be built, these factors have propelled ML into the mainstream.

Intelligent business processes

Machine learning enables companies to optimize core processes while improving customer experience and increasing employee productivity. Most organizations use rule-based processing to automate tasks in departments such as finance and human resources. This task-based automation has driven productivity improvements, but still requires employees to spend considerable time on repetitive work such as checking invoices for accuracy or reviewing hundreds of applicant resumes. Machine learning is now being applied more widely within enterprises, where it can exploit the wealth of business data and free up employees to spot potential areas for process improvement or reimagine new business models.

There are some tasks that machines do well and others that humans do well, and those will remain in the human domain. Much of the value machine learning offers lies in its ability to identify patterns humans cannot detect and do so at scale. It can also simplify, reduce risks caused by humans including insider threat, and streamline business processes. For example, in the insurance industry, thousands of claims can be automatically processed without human intervention and legitimate claims for settlement can be handled while flagging potentially fraudulent claims for further investigation.

To derive greater value from machine learning, both humans and machines need to work together. Furthermore, data access and models must continuously improve through experimentation and training in order to produce intended results and help increase return on investment.

Real-world benefits

Today's machine learning adopters are experiencing promising results. In healthcare, machine learning is driving quicker diagnoses, better treatment plans and improved patient outcomes. Retailers use it to anticipate what shoppers want and extend more individualized offers. Utilities can forecast demand and reduce or prevent outages. In each of these applications, machine learning can speed through massive amounts of information from many sources faster than a human ever could.

Businesses are most likely to achieve a high ROI from ML when they choose the right problem to solve, access data that will produce meaningful results and tap into people with the appropriate expertise. For example:

• Volvo applies machine learning techniques to automatically discover patterns and learn from the vast amount of vehicle data it collects. The company can now identify the necessary parts and provide repair instructions, even before the truck arrives for service, reducing diagnostic time by up to 70 percent, lowering repair time by more than 20 percent and putting trucks back on the road quickly.
• Munich-based Versicherungskammer Bayern, one of the leading insurers in Germany, adopted machine learning to resolve claims more effectively, provide customers with individualized products and deliver seamless customer service.
• The world's largest media investment company, GroupM, needed to help clients find the right messages for the right channels at the right time. Machine learning enables the company to streamline marketing campaigns and drive better decision making around where and when to place ads, lifting conversion rates by up to 50 percent and slashing time spent on analysis by 80 percent.

It is important to understand where the benefits of machine learning end and where human cognition takes over. Although businesses are now able to uncover patterns and behaviors that were previously invisible to humans, people still serve an essential role: the ability to design processes and applications to influence end-user actions, apply domain expertise to interpret results and train machines to improve outcomes through deployment and optimization.

Machine learning as part of a data science platform

There is a diverse range of tools available for machine learning, including different ML frameworks and APIs. Many of the algorithms used for machine learning, such as regression and neural networks, have been in use for decades. Chances are that most organizations already have some of the capabilities or tools in place to begin building machine learning models.

The benefits of these capabilities can be amplified when they are part of an integrated platform designed to speed time to data insight, model management and deployment. Organizations that succeed in machine learning also utilize platforms to empower staff with a wide range of skills, including coding and visual modeling. Improved productivity makes it easier to manage the end-to-end data science lifecycle from ingestion to actions taken. The ideal data science platform should include:

• Collaboration, sharing and reuse of analytical models in streaming, right time and batch processing
• Leading open source tools such as Python, R and Spark
• Scalability to hundreds or thousands of users, including business analysts, data scientists, engineers and developers
• A continuous feedback loop that improves model accuracy over time
• Flexible options for deploying ML into applications with ease of data access

IBM offers a comprehensive data science and machine learning platform that can scale at the speed your business requires, either on premises or in your private or public cloud. IBM SPSS Modeler. helps businesses accelerate time to results and improve the productivity of their data science and analytics teams. IBM Data Science Experience empowers data scientists, data analysts and engineers to collaborate in one workspace, exploring, learning, modeling, predicting and delivering results more efficiently. Learn more today.

Source: IBM Analytics