Nowadays, data takes an important role in the operation and context of all organizations. Data allows us to make informed decisions and take advantage of the past to learn and get to know our customers, users, employees, processes, competitors, and more. To be successful a company must extract value from its own data and that of its environment, pursuing the objective of becoming more and more competitive. When a company decides to enter the world of data and data science, the opportunities are immense.
But the question that often arises is: “what is data science all about?” The answer may seem simple, however, many times we fall into misconceptions that do not help us understand the concept. Let’s work it out together.
What is Data Science?
If we look for the formal definition, we could say that it is a discipline that focuses on uncovering insights (i.e. discovering hidden knowledge) from large amounts of data, both structured and unstructured. In fact, this is how IBM defines it, adding the idea that Data Science uses statistics, machine learning, data mining, and predictive analytics to achieve its goals.
From a practical point of view, we could say that by implementing Data Science processes in our organization, we are seeking to develop an approach that allows us to better understand our environment, our problems, our opportunities and, mainly, our business. Data science allows us to understand and explain past events and generate models to estimate or predict situations or behaviors that will generate impact in the future. So, it is necessary to understand that it is an interdisciplinary field, which combines formal sciences (i.e. mathematical modeling and statistical analysis), computing (i.e. computer science and software development), and the specific domain over the knowledge of a certain business or industry.
At Huenei, we take advantage of advances in data science and technology to enhance our software developments from a comprehensive business vision.
What About BI and BA?
It is very common to confuse Data Science with Business Intelligence and Business Analytics. It is important to know the differences to be able to evaluate what elements can help your company become competitive.
Business Intelligence is a business and strategy process that incorporates technology and software solutions for data analysis and information communication in a timely manner, with the aim of supporting decision-making. If correctly implemented, Business Intelligence can be weighed as a major organizational advantage these days.
On the other hand, Business Analytics represents an integral part of the Business Intelligence structure of an organization, which focuses on building statistical models and analyzing databases to predict scenarios and understand potential future states.
In short, both of them take advantage of available data to improve decision making. But their differences lie in the fact that Business Intelligence represents a general business strategy, which uses historical information in order to learn and boost decision-making. Whereas Business Analytics is a key component within the Business Intelligence strategy that focuses on using the organization’s historical data to predict future events. So, we have a global strategic business vision and advanced management of analysis tools and methodologies; by adding the boost of computational power, we enter the world of Data Science.
How Can Data Science Help your Business?
Data science can help you solve business problems, get to know your consumers or users, understand your employees and your competitors. Technologies available are varied and each professional will determine the right ones for their business.
We suggest you read our article “What’s the most important programming language in 2021?” to learn more about the leading programming language in 2021.
Regardless of the technology, languages or tools used, there are various applications of Data Science –and their integration with Business Intelligence and Analytics– in a company, that you could take advantage of depending on your needs. There are a large number of methodologies, analysis and strategies, and it would be impossible to name them all. But we want to take advantage of this space to introduce you to the world of data science through some examples:
1. Segmentation of customers, employees, suppliers, etc. Statistics and machine learning allow you to naturally group people, companies, or any unit of analysis, counting on variables of our interest. Thus you can create groups that are heterogeneous amongst each other but internally homogeneous. In the marketing environment, for example, this can help you recommend personalized products, display customized ads, among others.
2. Churn. Churn analysis can help you understand when a customer will stop buying from you, when a user will uninstall your app, or when an employee will quit. In addition, you can evaluate the probability of “abandonment” (churn) of each individual.
3. A/B Testing. This methodology is about conducting experiments where two groups are randomly created: A and B (the control group and the experimental group). For example, you could compare the typical production of an industrial plant with an alternative production by implementing a different methodology. You could also compare two versions of an advertisement, or two different website designs. The objective is, through comparison, to determine which one offers better performance.
4. Sentiment analysis. Through the use of Natural Language Processing, it is possible to extract subjective information from texts and speeches, to standardize it in variables and quantitative indicators. The classic example is the social media sentiment analysis, where Tweets, comments on Instagram posts, and so on, can be summarized in an indicator of sentiment towards the brand (i.e. positive, neutral or negative sentiments).
In a nut shell, it is essential for a company that wants to increase its competitiveness to get involved in the world of Data Science. For this, the strategy must be planned and it is important to understand the field of action of the different components of a Data Science strategy in an organization. The applications are varied and the ways in which your organization can benefit are endless.