In today's economy, all businesses are transforming into data-driven enterprises. According to a study conducted by Forrester Consulting, 98 percent of companies stated that analytics are important for advancing their business priorities. However, less than 40 percent of workloads utilize advanced analytics or artificial intelligence. Machine learning provides an opportunity for organizations to derive greater value from their data, increase revenue, gain a competitive advantage, and reduce costs.
Introduction to Machine learning
Machine learning is a form of proactive analysis that allows organizations to move up the maturity curve of business intelligence (BI) and move away from exclusive dependence on descriptive analyses of the past to integrate future-oriented, autonomous decision support. While the technology has been around for decades, the hype around new approaches and products is causing many companies to take a new look at it.
Analytical solutions based on machine learning often work in real-time and add a new dimension to BI. While old models continue to deliver important reports and analyses to decision-makers, real-time analyses bring information to employees "on the front lines" to improve performance on an hourly basis.
In machine learning, a subfield of artificial intelligence, information systems are "trained" to use specific algorithms to investigate, learn, and output predictions and recommendations based on massive amounts of data. Prediction models that are exposed to new data can adapt without human intervention and learn from previous iterations to achieve more reliable and reproducible decisions and results.
Over time, this iteration makes systems "smarter," allowing them to uncover hidden insights, historical relationships, and trends more effectively and uncover new opportunities in all areas, from buyer preferences to supply chain optimization and oil extraction. Of particular importance, machine learning allows companies to achieve more with big data and integrate new functions such as IoT analytics.
Machine learning is a powerful analytical technology that is currently available. Many new commercial and open-source solutions for machine learning are available together with an extensive ecosystem for developers. It is very likely that your company is already using this approach, for example, for spam filtering. Through wider application of machine learning and analytics, you can respond more quickly to dynamic situations and benefit more from your rapidly growing data.
Widely used: predictive analytics
An important reason for the growing popularity of advanced analytics based on machine learning is that it can provide business benefits in practically any industry. Wherever large amounts of data and predictive models need to be adjusted regularly, machine learning makes sense.
Providing recommendations for books, movies, clothing, and dozens of other categories is a well-known example of machine learning. But there are many other applications.
In retail, machine learning and RFID tagging enable significantly improved inventory management. The overview of the position of an item is as well as the reconciliation of the inventory with the book inventory is a major challenge. With machine learning, the data used to solve these problems can also improve product placement and influence customer behavior. For example, the system could scan the physical store for misarranged inventory to rearrange it correctly or to identify items that sell well and place them in a more visible location in the store.
When machine learning is combined with linguistic rules, companies can scan social media to find out what customers are saying about their brand and products. It can even find hidden underlying patterns indicating interest or frustration with a particular product.
The technology is already playing a crucial role in sensor applications. Machine learning is also essential for self-driving vehicles, where data from multiple sensors must be coordinated in real-time to ensure safe decision-making.
Machine learning can contribute to analyzing geographic data to uncover patterns that can predict the likelihood that a particular location is the right place for generating wind or solar energy.
These are just a few examples of many for machine learning. It is a proven technique that is currently delivering valuable results.
Clear competitive advantage
With machine learning, companies can gain a competitive advantage by solving problems and uncovering insights faster and easier than with conventional analytics. It is particularly suitable for value creation in three situations:
When large amounts of data need to be analyzed that would not be manageable manually.
When the analyzed data quickly becomes outdated and processing must be done in real-time.
When the company can gain a competitive advantage through fast and accurate decision-making.
Machine learning also offers the advantage that there are no human biases in the analysis. This means that decisions are based on objective facts and not on personal opinions or emotions.
By implementing machine learning, companies can also reduce their operating costs, as the technology reduces the workload of employees. This gives employees more time to focus on other important tasks."
The solution to a problem changes over time: Tracking a brand's reputation on social media is a good example. Demographic data of individual platforms shifts; new platforms emerge. Such changes lead to confusion and compel marketing professionals to regularly re-evaluate whether the right goals are being achieved with the right messages using rule-based analysis. In contrast, machine learning models simply adapt, deliver reliable results over time, and free up resources to solve other problems.
The solution varies from situation to situation: In medicine, for example, personal or family history, age, gender, lifestyle, allergies to certain medications, and many other factors make each case different. Machine learning can take all these factors into account to enable personalized diagnosis and treatment while optimizing healthcare resources.
The solution surpasses human ability: Humans can recognize many things, such as voices, friends' faces, certain objects, etc., but cannot explain why. The problem? Too many variables. By working out and categorizing many examples, machine learning can identify and recognize certain external variables, such as those that characterize a voice (pitch, volume, overtones, etc.).
The competitive advantage arises from the development of machines that do not rely on human recognition, description, intervention, or interaction to make new types of decisions. This feature opens up new possibilities for numerous fields, including medicine (cancer screening), manufacturing (defect detection), and transportation (using sound as an additional signal for driving safety).
Faster and more cost-effective
Compared to other analytical approaches, machine learning offers various advantages for IT, data scientists, various business areas, and their organizations. Machine learning is agile and flexible with new data. Rule-based systems work well in static situations, but machine learning can use its strengths especially when data is constantly changing or being added. This is because the system does not need to be constantly optimized or supplemented by new rules to achieve the desired results. This saves development time and significantly reduces the need for major changes.
The personnel costs for machine learning are typically lower than those for conventional analyses. Initially, the company must, of course, hire highly qualified specialists from areas such as probability theory, statistics, machine learning algorithms, and AI training methods. However, once machine learning is active, predictive models can adapt themselves, meaning that fewer people are needed to optimize accuracy and reliability.
Another advantage is scalability. Machine learning algorithms are built with parallel processing in mind and are therefore better scalable, ultimately leading to faster responses to business problems. Systems that rely on human interaction are also not very scalable. Machine learning minimizes the need to constantly rely on people for decisions.
Finally, machine learning applications may cost less than other types of advanced analytical methods. Many machine learning methods can be easily scaled to multiple machines rather than a single expensive high-end platform.
Getting Started with Machine Learning
Success in getting started with machine learning begins with identifying a business problem where the technology can have clear, measurable impacts. Once a suitable project is found, companies must provide specialists and choose a suitable technique to teach systems how to "think" and respond. These include:
Supervised Learning: The system receives example inputs and outputs and must create general behavioral rules. For example, the recommendation systems of most major brands use supervised learning to increase the relevance of suggestions and thus sales.
Unsupervised learning: In this mode, the system examines the data for structures and patterns. This approach can be used to identify patterns that would otherwise go unnoticed, such as buying behavior in stores that could be influenced by revenue-boosting changes in product placement.
Reinforcement learning: With this approach, the system is placed in an interactive, ever-changing environment, provided with a task, and given feedback in the form of "penalties" and "rewards." This technique has been used with great success to train factory robots in object identification.
Regardless of your project, a company's progress in effectively utilizing machine learning for analysis depends on mastering these fundamental techniques.
Intel: Powerful processors are just the beginning
Intel is helping businesses integrate machine learning into realistic applications that require extremely fast performance. This is accomplished through a system concept that includes processors, optimized software, developer support, and a large technology ecosystem with industry partners.
High computational power is required for machine learning. Intel® Xeon® processors provide a scalable baseline, and the Intel® Xeon Phi™ processor was specifically designed for the highly parallel workloads typical of machine learning, as well as the memory and fabric (network) requirements of machine learning. Intel's hardware technology also includes programmable accelerators and those with fixed function, system and mass storage, and network technology.
In addition, Intel provides software support that enables IT organizations to effectively and efficiently transition from business problems to solutions. This support includes:
Libraries and languages with Intel Xeon processor-optimized building blocks, including the Intel® Math Kernel Library (Intel MKL) and Intel® Data Analytics Acceleration Library (Intel® DAAL), as well as the Intel® Distribution for Python*.
Optimized frameworks for simplifying development, including Apache Spark*, Caffe*, Torch*, and TensorFlow*. Intel supports both open source and commercial software, allowing businesses to take advantage of the latest processors and system features as soon as they become available.
Software development kits (SDKs), including Intel® Nervana™ technology, TAP, and Intel® Deep Learning SDK, which provide a range of application programming interfaces so that developers can immediately use the best machine learning algorithms.
Intel employs several approaches for optimization, including coaching customers and sales partners on how to run their machine learning code on Intel hardware faster, as well as implementing some learning functions in silicon, which is always faster.
Intel technicians are constantly in the field, speaking with IT and business managers to gain insights into how predictive analytics based on machine learning can solve real business problems.
Conclusion:
In conclusion, machine learning is a powerful analytical technology that provides organizations with the ability to move up the maturity curve of business intelligence and transform into data-driven enterprises. Machine learning offers a clear competitive advantage by providing faster and more accurate decision-making, reducing operating costs, and eliminating human biases in analysis. It is a proven technique that has a wide range of applications in practically any industry, from retail to sensor applications and renewable energy. With the potential to derive greater value from data, increase revenue, and gain a competitive advantage, machine learning is a valuable tool for organizations looking to stay ahead in today's economy.
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