This post was published on Hortonworks.com before the merger with Cloudera. Some links, resources, or references may no longer be valid.
The advantages of machine learning are clear: the subset of artificial intelligence learns by observing and aspires to mimic the human brain. It can help organizations with prescriptive analysis and automated decision-making.
Such benefits help explain why there is significant interest in machine learning. Spending on machine learning and the closely related area of artificial intelligence (AI) is projected to grow from $12 billion in 2017 to $57.6 billion by 2021. Companies want to use these technologies to learn and predict so they can perfect their business strategies.
Organizations that are likely to make the most of the advantages of machine learning must understand how the technology helps make accurate decisions in complex situations. By taking a tight grip on machine learning, your organization can make successful digital transformation a business reality.
Defining Machine Learning
Machine learning often gets talked about in the same context as AI and deep learning. It’s important to clarify the differences because the terms are often used interchangeably, which can cause confusion.
In its simplest form, AI involves using computers to mimic human intelligence. Deep learning, on the other hand, involves neural networks and is the furthest level of advancement, where knowledge and computing power are used in autonomous thinking.
Machine learning sits somewhere between AI and deep learning. More than simply a technique for analyzing data, machine learning is a system fueled by information. Machine-learning technology can learn from data and improve continuously, using algorithms that provide new insights.
Recognizing the Rise in Connectivity
While deep learning will eventually lead to huge scientific advances or serve more advanced use cases, machine learning is likely to be the key to helping most businesses improve their day-to-day operational activities. For instance, machine learning models can be applied to streaming data to get predictive alerts or real-time insights for decisions.
Such information sources change in real time. These sources might include stock updates, news feeds, social streams, usage data, and network traffic. Tracking and tracing these disparate sources of information can be a challenge—and it’s about to get even tougher.
We are already in an age of hyper-connectivity, where millions of Internet of Things (IoT) devices and sensors send out signals and readings every few seconds. Smart businesses will embrace this rise in streaming data and use machine learning to interpret trends and create a competitive advantage.
Using Knowledge to Identify Patterns
Your business, therefore, must ingest data from multiple sources. Some of that information will be routed into a big data store for historical trend analysis. Other real-time data will have a perishable life span and must be analyzed immediately to help make quick decisions.
Data-flow technology, such as Apache NiFi, will help ensure that high-speed streaming data is ingested and routed to the right systems quickly. A streaming analytics solution will then help deal with processing and analytics, allowing your business to identify patterns in streaming data within the data flow.
Complex Event Processing (CEP) capabilities within such analytics tools enable the understanding of such patterns or correlations intuitively. Take an example from finance, in which machine learning can help businesses detect a real-time fraud pattern if the data suggests that two cash withdrawals are taking place in geographically distinct locations using the same credit card within a very short time span.
Understanding the Benefits
The advantages of machine learning can be seen in other sectors, too. Online retailers can track customer behavior and use algorithms to create technical solutions for business challenges. For example, automated chat bots can push answers to customers searching for products. Retailers might make further recommendations or push a coupon to help sway a consumer’s decision.
Utility companies, on the other hand, can use connected smart meters to trace consumer usage and equipment activity. Connected meters can report abnormal activity automatically, and companies can use this information along with data points from similar locations to respond quickly to outages.
Finally, social streams provide brands with a rich source of customer sentiment, but trawling through this data is a tough process. Organizations can use machine learning to filter the noise in their social streams. They can focus on sentiment related to the launch of a new product or feature, informing their long-term strategy.
Solving Key Organizational Challenges
Your competitors could already be gaining an advantage by using insight to improve their decision-making processes. Inevitably, the keys to solving today’s business problems with machine learning are threefold: volume, velocity, and variety.
In terms of volume, organizations are drowning in data. It’s impossible to digest all the data and make sense of the noise without the help of machine learning. When it comes to velocity, the speed at which data is coming through only continues to increase, and your executive team must make crucial business decisions quickly.
Finally, streaming data is not your only data management concern. Your organization must deal with a huge variety of sources, including information at rest. For example, in healthcare, awareness of historical insights and patterns can help clinicians make life-changing diagnoses and decisions for a patient.
Embracing Digital Transformation
Your strategy for machine learning will rely on an integrated approach, where technical excellence is matched with the human capability to focus on business imperatives. Anyone can build a dashboard that illustrates trends and patterns. Success comes from machine-learning algorithms that can create insights that will boost business performance and meet tight governance guidelines.
While digital transformation remains a work in progress for all organizations, data-enabled change through the power of machine learning can help your company take giant strides forward. Your role is to help your organization align its business imperatives with the potential of machine learning. If your firm can include or enhance machine learning in its next set of transformational initiatives, your business challenges could become your competitive advantage.
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