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If a company needs to make a strategic decision, it must first look at the relevant data. It used to be that companies based their strategies on limited data sets— usually collected through interviews and surveys. But today, the power of the Internet and cloud technology has given rise to “Big Data”, an entire industry focused on analyzing sets of data that are simply too large or complex for traditional software. Data will continue to be an invaluable resource in the years to come.

But data also impacts small and medium-sized businesses, from marketing strategies to consumer personalization. This new era of data calls for an updated perception and understanding of data. Here’s why data science is so important to businesses today.


Importance of Data Science


To understand the value of data science, we first have to understand its definition. So what exactly is data science anyway?

According to IBM, one of the leading companies in big data and artificial intelligence, data science is defined as, “a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations.” Put more simply, data science combines the power of math, science, technology, and storytelling to yield deeper business insights.

Data science is a broad topic and can cover a large range of topics. For example, data science statistics is concerned with the mathematics of data, from concepts like averages and medians to more complex ideas such as R, a statistical programming language.

On the other hand, data mining centers around extracting data from relevant sources and finding patterns that others may not notice (such as correlations or causations). Predictive analytics is the side that focuses on taking the mined data, analyzing the historical trends, and projecting or predicting future results. Other aspects of data science focus on the presentation of the data, or on the application of machine learning and artificial intelligence.

Data scientists conduct data science experiments to test their theories, from better understanding the target market, to providing direction to a company. These experiments are useful in making sense out of large sets of numbers and statistics and can inform a company’s overall strategic decision making.


Why Companies Use Data Science


1. To create more relevant products and services. Data scientists can learn a lot from purchase behavior. Information such as units purchased, product impressions, or average usage time can all reveal clues as to which offerings a company should focus on the most. It may also suggest new products, services, or verticals to explore.

2. To acquire more customers. Data can also reveal information about customers themselves. Demographic data covers population information, things like education, age, income and occupation. Psychographic data can go deeper, revealing the various aspirations, sentiments, and pain points of customers. All of this can be used to better target ideal audiences, or reach out to potential customers.

3. To innovate.  Autopilot cars were a science fiction dream until Tesla came along. Rather than solely rely on humans to build the autopilot system, Tesla has collected over 1.3 billion miles of data from its vehicles to improve its mapping data. True innovation can be supported by large data sets.

4. To recruit and hire more highly qualified employees. While humans will always be necessary for determining a candidate’s fit for a role, big data is increasingly shaping how applicants are screened. Everything from resume analysis to social data mining can be conducted before they even step in for an interview, helping save time and eliminate the risk for the company.

5. To discover marketing trends. These days organizations must stay a step ahead of their competitors in marketing, and one way to do so is by catching a trend before it loses steam. For example, sentiment analysis is a new form of social media monitoring, one that allows marketers to better understand how audiences feel about a particular topic. Marketers can use this data to find new trends before they become viral, or avoid negative subjects in their marketing.

To develop personalized experiences. Modern marketing has to adapt to a customer’s unique traits and experiences— a customer in Chicago, Illinois will respond differently to an ad than a person from Stone Ridge, Virginia. Data scientists can collect data that will allow marketers to better understand and tailor experiences to their customers.


Data Science vs. Machine Learning vs. Data Analytics


Data science is often used interchangeably with other terms, notably machine learning and data analytics. While there is overlap in the subject matter, the three subjects are markedly different concepts.

Machine Learning is defined as the study of computer algorithms that improve automatically through experience. Machine learning can be used in data mining software that helps analyze large sets of data, or in learning user preferences, as is the case with Netflix’s recommendation system (among many other media companies).

Data Analytics, on the other hand, refers to the analysis of raw data to find trends and answer questions. Data analytics can be further divided into subcategories, including descriptive analytics (what happened based on the data), predictive analytics (what might happen based on the data), and prescriptive analytics (what should be done).

Data science plays a part in both machine learning and analytics, but each concept pertains to a specific aspect of data science. For machine learning, it’s about algorithms that learn over time. For analytics, it’s about understanding the data at hand to find answers. The distinction between the three may be subtle, but it does illustrate the broadness of the data science field.


Data Science Trends


Reinforcement Learning. A burgeoning field of machine learning, reinforcement learning involves intelligent agents that are able to take the optimum decisions and actions to receive the ideal reward within a given environment. Facebook AI Research is building its own toolkit, ReAgent, which is a decision-making AI capable of receiving feedback. ReAgent is currently used for personalizing billions of decisions at Facebook, from user notifications to robotics research.

Deep Learning. Another aspect of machine learning, deep learning centers around computers learning by example based on labeled data and complex neural networks. In recent years, deep learning has become increasingly accurate, used in driverless cars and image recognition systems.

Natural Language Processing. Natural Language Processing (NLP) is the manipulation of natural language (the way humans communicate) by computers. This can range from text-based communication, such as search results and email filters, to speech-based communications, like language translations or voicemail transcription. Already we are seeing the rise of voice assistant use through our phones and our televisions.

Explainable Machine Learning (XAI). The results of artificial intelligence can be difficult to interpret by humans, and that’s where XAI comes in. These are models that essentially act as black boxes, and make the data more easily understandable by data scientists or even the average layperson. These models will become crucial in the ongoing development of data science.

Computer Vision. Computers don’t comprehend imagery or videos, at least not the way humans do. While it may be able to read certain labels, like “cat” or “guitar”, it has trouble understanding more nuanced symbols, like emotion or abstraction. Deep learning is bringing more complexity to computer vision, allowing major AI to detect objects for robots, fashion ecommerce, patent search, and other common applications.

Multi-agent systems. Most of the research in AI has involved a single intelligent agent, until recently. Multi-agent systems have allowed two or more agents to work together, solving problems previously thought impossible to humans. With more agents in the system, there is less chance for a bottleneck or critical failure and more efficient means of data processing and collection.

The world of data science continues to be an inspiring and exciting glimpse into our future. Problems previously thought impossible, ideas previously viewed as fiction, have been given new life through the lens of data and technology. Thankfully, data science isn’t reserved for big AI companies or major corporations— small and medium-sized businesses can stand to benefit from these advancements as well. It only takes a little curiosity and research to start applying data science to one’s business strategy.