As data is transformed into insight, it becomes a strategic asset for a business. To cash-in on this currency of the new economy, organizations are taking steps to make analytics a core competency across the enterprise.
Machine learning, in particular, is a topic that clients are bringing up more frequently when discussing how to pursue this data-driven advantage. Forty-one percent of organizations are using machine learning, 36 percent are experimenting with it, and 16 percent are considering using it, according to a recent Accenture survey.
While it is recognized that machine learning is considered a new or next-gen approach to analytics, there is still some mystique surrounding it. Some businesses aren’t clear on what it is, when they should apply it and how they should apply it.
Machine learning can be valuable to companies, helping them to apply their data to defend, differentiate and disrupt in their markets. Here are some key points to help shed some light on machine learning and how it could help businesses become more agile and insight-powered to effectively pursue their goals:
Understanding Machine Learning: Not As Scary As It Sounds.
Machine learning is an evolution of analytics. It is not a single technology or technique but rather a field of computational science that encompasses modern mathematics, and various statistical techniques including clustering, trees, dynamic systems, and deep learning, just to name a few of its key areas.
At its foundation, machine learning leverages massive amounts of data internal, external, sensor, social, etc to identify and define associations between the data to discover solutions. As machine learning can be complex, data scientists are generally needed to determine the set of algorithms that can solve for the business problem and learn from the data to find patterns and connections, and apply its knowledge to future situations. Such intelligent algorithms acquire experience, enabling their software to self-evolve and make discoveries for innovation. And keep pace with the rising expectations of its users. Machine learning is also a key enabler to cognitive computing, defined as IT systems that can sense, comprehend and act. Leveraging computer vision, natural language processing, and inference engines, cognitive systems enable more natural interactions with the environment, people and data.
In terms of potential use cases to illustrate what’s possible, machine learning can help a company to: learn from past behavior and predict behavior of new customers, segment consumer behavior in an optimized, market friendly way (e.g. customer lifestyles modeled from geo-location data on cellphones), or conduct crowd simulation models where each customer’s response to a reward is modeled.
Pursuing Machine Learning: You Might Not Be Ready for It Yet.
As all businesses are unique, the rate of adoption varies. For instance, a firm’s analytics maturity level is a big factor in machine learning adoption. Additional elements to consider would be the culture of the company and the availability of technology and talent needed to support machine learning.
If a company is just beginning to explore the possibilities of analytics, the timing would be premature for them to dive into machine learning projects. When the time is right to begin exploring machine learning, they should develop a comprehensive understanding of their firm’s software intelligence, including how it is currently used and could best be used in their company. Then, they should begin to invest— in educating employees, hiring technical experts, and encouraging use of tools like Python and other open source libraries.
Alternatively, if a company is experienced with analytics and pursues a lot of research and development initiatives, machine learning would be an excellent tool for them to use to fast-track innovation. These companies would begin by identifying their data assets, leveraging new ones, and starting to explore the data they already have in search of hidden insights. They will start small and add on until machine learning is prevalent throughout the enterprise.
Applying machine learning: machines don’t solve real, complex business problems. People do. An over-reliance on data—regardless of how smart the underlying algorithms used to analyze it are—can hinder innovation. To overcome this potential limitation, companies should establish a level of collaboration between humans and computers.
Machines can compute with incredible accuracy and scale, and can consistently get better at doing so. Humans complement this ability as they excel at thinking creatively and in context, so they can question and improve the conclusions of the intelligent software. Indicating that this collaboration is seen as an important path forward, 78 percent of survey respondents stated that successful businesses will manage employees alongside intelligent machines.
Through this collaboration, entirely new data associations can be discovered —ones that almost certainly could not have been made by humans alone. Normally, these unique connections are highly predictive in nature, empowering enterprises to acquire the insights that enable them to adapt, empowering them to develop new products and enter new markets ahead of their competitors.
The data-driven future. One leap at a time.
Machine learning technologies will pave the way for intelligent software to evolve itself to keep pace with technology and enable companies to adapt to the ever-changing digital world. Cognitive computing will go another step further to capitalize on its unique reasoning capabilities to address questions that were once unanswerable due to their ambiguity.
When a company understands the opportunity machine learning could offer their business, pursues it when it’s ready to take the plunge, and builds a collaborative environment between humans and machines, they are in a place to harness the power and potential of software intelligence. As a result, they’ll be able to innovate more rapidly, run more efficiently, and serve customers more effectively unearthing the value and business in their data.
Narendra Mulani is senior managing director for Accenture Analytics.
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