Among business leaders that implemented artificial intelligence, data science, or machine learning, 63% had reported that it increased revenues, and 44% — reduced costs, McKinsey claims. AI automation can significantly benefit businesses; in contrast, wrong solution choices could cost millions.
While any businessperson knows what they want to achieve, finding the quickest and most beneficial way to reach that goal could be challenging. You should know where to look and whom to ask about machine learning development. You don’t go to a marketing department with your customer retention problem because that’s the task of a customer success and support department. Thus, to successfully resolve issues with AI, data science, or machine learning, it is essential to describe the problem accurately as well as find the best team for the task.
To solve these issues, businesspeople need to know the problem space for data science and machine learning to know whom to ask and what to ask, expressing themselves in domain terms.
What is Artificial Intelligence?
As we touched on the term AI, let’s briefly describe it. A rather hyped tech term used frequently in pop culture, AI is often associated with a machine-dominated world and futuristic robots. Yet, in reality, artificial intelligence is far from that.
Those robots and fantastic machines from sci-fi, mimicking human intelligence or even consciousness, are called general AI. Extant technologies are far from reaching it (if it’s even feasible). Nowadays, the AI widely used in different industries is called narrow or task-specific. It can brilliantly (swiftly and accurately) solve specific issues, for example, recognizing human speech or detecting objects in video.
However, AI as a term is often used interchangeably with machine learning (ML). Nevertheless, AI, as a collection of methods and technologies, is a superset that includes ML. So most, if not all, examples of ML are also examples of AI. Therefore, there are examples of “AI without ML”: expert systems and knowledge graphs fit the bill. The most well-known one is Deep Blue – the first machine that beat humans in chess. It had all the rules coded into it without machine learning implemented. However, it was fast enough to consider various options with downstream consequences and to choose the best move.
Artificial intelligence (AI) and machine learning play a significant role in business intelligence and data analytics, dominating parts of data science.
What is Machine Learning?
Machine learning is a branch of artificial intelligence focused on developing algorithms that can learn from data. This learning objective can be a prediction but might also be something else. It can also be seen as “teaching a machine to think.”
Machine learning algorithms create a useful model by looking at the data provided and finding patterns to infer rules that help solve an objective. This may be as simple as finding a correlation like “When the weather is cold, people tend to buy less ice cream” and putting an exact number to how much less ice cream people buy per one-degree change in temperature. Still, machine learning is typically used to find more complex and far less obvious patterns.
Machine learning as a field deals with devising algorithms that are good at learning these rules for any given objective and describing how the data should be represented for the algorithm to work. There are general approaches or model types, such as decision trees or deep learning, which are adjusted or modified for learning a specific task like predicting demand or deciding on what’s depicted on an image.
Machine learning helps address labor-intensive problems. Moreover, it can make predictions about complex topics, providing accurate results for informed decision-making efficiently and reliably.
As a set of tools and concepts, machine learning is applied in data science and appears in fields beyond it. For example, meteorologists use machine learning to gather information on past weather trends to assist with forecasting analysis.
Clearly, machine learning is a valuable asset for any industry, with the promise to revolutionize technology in countless fields, from healthcare to the military, from computer security to entertainment, and beyond.
The inherent limitations of Machine Learning
Though machine learning benefits may seem like a panacea, a cure for any problem, it is not all-powerful.
Even with its ability to create valuable results with minimal intervention, ML requires machine learning engineers and programmers to maintain and develop these algorithms to adapt to new problems.
There are also plenty of issues that machine learning cannot solve efficiently, such as cases with a wide variety of input or with subtle and ethical decision-making rules. For instance, IBM Watson for Oncology still cannot be applied to real-world situations, as they remain too complex, even after six years of development and $5B in training data. Or Tesla’s fully automated manufacturing, promised by 2018 but turned into Production Hell, doesn’t look like it will be up and running any time soon.
Thus, if a traditional program or equation can solve a problem, adding machine learning might actually complicate the process rather than simplify it. If the rules are clear from the beginning, simple automation is enough, and ML is an unnecessary step.
Signs of a good machine learning team
They have a clear and well-defined process for their work.
They have a track record with similar projects. However, this point could be tricky because business people might not always be the best judge of what similar means when it comes to data science or machine learning projects.
They have a range of skills and competencies that cover what you are looking for. This point requires extra attention and consultation because first-time tech business leaders don’t always have enough knowledge to recognize the skills they should search for.
They can function both independently and as a part of your machine learning team at the same time. However strong the AI team is, if they don’t cooperate with the business side, the project will not reach maximum efficiency or may even fail due to goal misalignment.
What is Data Science?
Let’s start with the definition for data: simply, it is information of any kind — text, numbers, audio, images, or video. Data science is an interdisciplinary field that applies mathematical analysis, statistical methods, and machine learning algorithms to extract facts, rules, and patterns from data. Additionally, this field researches how to interact with data: formulate research questions; collect, pre-process, store, and analyze data; and present research results using data visualization.
Data science is essential for business: instead of counting on someone’s highly subjective opinion, business leaders have numbers and facts to serve them. Working with data analytics helps companies from different industries to optimize business processes, understand their customers, and offer better products.
The limitations of Data Science
As the name suggests, data science relies on data. Affordable computers and the rise of massive datasets enabled the development of data science as a discrete field. This is, however, a field of scale wherein small datasets and messy or incorrect data can waste time, creating models that produce meaningless or misleading results. Data science will only succeed if the data capture the real causes of variation.
Signs of a good data science team
Data come from different channels and are growing fast, so big data analysis is beyond human capabilities, at least without special tools and techniques.
Therefore, to work in data science, the team needs a diversified set of technical skills. Team members must know programming, computer science, statistics, math, and data visualization. Moreover, it’s essential to possess a research-oriented mind, notice knowledge gaps, and formulate the questions that can help fill them in.
Data Science vs. Machine Learning: key differences
As explained above, data science and machine learning are two different fields that improve different aspects of businesses. The main task of Machine Learning is to create self-learning machines, enabling them to execute varying specified tasks, while Data Science concentrates on helping businesses gather and analyze data, and then understand emergent trends. However, Machine Learning and Data Science depend on each other, with overlaps between the two domains. As data are essential for business development, machine learning technologies have become vital advantages for most industries.
Data Science is not a subset of Artificial Intelligence (AI)
Machine learning technology is a subset of Artificial Intelligence
Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge from many structured and unstructured data
Machine Learning is the scientific study of algorithms and statistical models. Machine learning methods are used to perform a specific task, creating output from input
Data science methods, algorithms, and processes extract insights from vast amounts of data
Machine Learning, in contrast, enables a system to learn from data through self-improvement, despite the rules not being explicitly coded by a programmer
Data Science is a complete process
Machine Learning is a single step in the entire data science process
Data science techniques help you to create insights from data dealing with all real-world complexities
Machine learning methods can predict or classify some value for previously unseen samples after being trained on historical data
Data science technology may include manual methods, like generating statistics for a BI dashboard
In Machine Learning, after the algorithm has been implemented and trained, the results are generated automatically
Data Science combines algorithms, tools, and machine learning techniques to find hidden patterns in the raw data
Machine learning models enable computers to learn automatically and improve with new data
Data science technologies create insights by analyzing data with all real-world complexities
Contrarily, machine learning methods help you to predict or classify some value for previously unseen data of an exact kind, e.g., the topic of a news article or the chocolate demand this Christmas
Data Science vs. Machine Learning: use cases in business
Most examples of business use cases include both Machine Learning and Data Science components. Hence, it is better to consult with experts (machine learning engineers and data scientists) on how to implement them most beneficially for your task. For further insight, below, you can find some examples.
Machine Learning business applications
Customer service automation
End-user customer support automation is a flourishing field for machine learning business applications, such as the conversational AI of chatbots or callbots. Human speech recognition via natural language processing enables machines to answer the users’ questions or direct them to the most qualified person, thus reducing inefficiencies and saving time for both clients and agents.
For example, the leading Northern European telecommunications company Elisa implemented Annika, their chatbot. It handles 70% of all the inbound contacts and fully resolves customer contacts with a 42% FCR (first contact resolution).
Fault detection
Machine Learning-based solutions can monitor equipment or products and identify necessary maintenance and repairs. Then, the AI alerts a human supervisor, who can watch the video/images to validate or fix the operation. As the faults are detected early, maintenance costs and downtime decrease. The use case based on the collaboration between Hepta Airborne and MindTitan, described in the computer vision case study, reveals the benefits of fault detection in detail.
Dynamic pricing tactics
With the help of Machine Learning, companies (e.g., retail and transport) can analyze their historical pricing data and datasets of additional variables to find connections. For example, AI can suggest how certain factors — from time of day to weather to the seasons — impact demand for goods and services. Machine learning models can learn from those big data, processing vast and numerous variables. By combining insights from that processing with additional market and consumer information, machine learning algorithms help companies dynamically price their goods. This strategy ultimately enables companies to maximize revenue.
The clearest example of dynamic pricing (also known as demand pricing) occurs in the transportation industry. Just remember surge pricing at Uber when rain increases the number of people looking for rides simultaneously or sky-high prices for airline tickets during school vacation weeks.
Recommendation engine systems
Today, businesses utilize recommendation engines to provide the correct offer/product to their clients at the right time. This standard technology for online shops and many other services considers browsing history and proposes new customized offers accordingly.
For instance, Fuzu, a Helsinki-based company, aims to provide ambitious young East African professionals with job opportunities, career advice, and new skills. Fuzu, with MindTitan’s help, implemented a recommendation engine that matches job seekers with potential employers to enhance client satisfaction. The tool increased the job applications’ click-through rate by 30%, the recommendation engine case study shows.
Data science business applications
Gain customer insights
Crucial for business, customer details, such as habits, demographics, preferences, aspirations, and more, come from data analysis. Those data come from multiple sources, for instance, website visits, purchases, or open social media interactions.
Understanding your clients and particularly their motives result in better targeting, thereby improving sales, as your product will meet customers’ specific needs.
The same works for retargeting efforts, website improvements, and user experience personalization.
Forecasting future trends
Continuous data collection and analysis on a larger scale allow businesses to forecast customer behavior trends.
For example, Nielson’s research showed that 81 percent of consumers feel that companies should care about the environment. Consequently, clothing retailer Patagonia leaned into this emerging trend by launching Worn Wear, a site created to help customers upcycle used Patagonia products.
By staying up-to-date on the behaviors of your target market, you can make business decisions that allow you to get ahead of the curve.
Data Science vs. Machine Learning: which is better?
It is impossible to compare the two domains to decide which is better – precisely because they are two different branches of study. It is like comparing the sciences and the arts.
The machine learning method is ideal for exhaustive task automation, with minimal human intervention. For example, machine learning algorithms can speed up production processes, increasing revenue.
Data Science can help you to analyze, understand, and identify a pattern in the data. You can use this input to train a machine. For instance, Data Science can also help you prevent significant financial losses by performing sentiment analysis to gauge customer brand loyalty.
However, as the field is complex, it is wise to consult Data Science and Machine learning experts to find the best solution.
