How to apply data science in business
Data science drives business value through churn prediction, segmentation, and fraud detection. Practical application examples by domain.
Data has taken over the world. If in the first industrial revolution the most valuable asset was energy generation for machines and industrial processes, in the fourth industrial revolution (the one we are living through right now), knowing how to use data science in business in a multidisciplinary way is the key asset.
Without a doubt, we are living through the technological revolution of knowledge, which is about optimizing how processes are carried out through the use of the internet and computing, automating repetitive processes, and making better decisions while considering both sustainability and people’s quality of life.
In this blog, we will explore how to apply data science in business, and why this concept is important for the not-so-distant future.
What is data science?
To understand what data science is, we first need to understand its importance and usefulness. Have you ever stopped to think about the volume of data generated each day by every person on Earth? Research indicates that this number reaches up to 2.5 quintillion bytes every day—a surprising figure, but one that makes perfect sense when we consider that almost every human activity today depends on or generates data.
A purchase made in a store signals inventory reduction and the new cash balance in a system. A photograph generates an authentication code containing the date, time, and even the location where it was taken. Even when we try to escape the connected world and isolate ourselves from technology, we still go through data generation and analysis systems, directly or indirectly. So what happens to all this information being generated?
Well, that is the role of data science: to collect, clean, structure, understand, and transform this entire flow of information into something useful for individuals and organizations. When data is untreated and ignored, it is not very useful, but when there is strategy and technique behind these processes, data usage can be truly revolutionary.
Based on this discipline, it is possible to formulate hypotheses, retrieve relevant information, understand the quality and accuracy of collected information, and also structure data to prepare it for deeper analysis. Today, it is possible to build algorithms and automate a large part of this work, and this is already being done in many data-driven organizations. This improves data analysis performance, reveals new analytical possibilities that previously went unnoticed, and makes the process much more agile.
Want to learn more about this topic? Here on the Erathos blog, we have a complete article about it: click here.
Futurism versus Reality
In the media, we see data scientists as geniuses working in government labs, designing the new technologies of the future. It is interesting to observe how natural it is to think of science and business as two distant elements—but that is exactly where the danger lies!
There is indeed a large gap in the market between academic knowledge and practical knowledge, but data science should not exist as a separate entity, especially in the business world.
Therefore, thinking that integrated data analysis to boost the performance of each area in an organization is something from the future is a major mistake, because this knowledge already exists and is being applied now.
Companies that have already realized this are ahead in the market, because they can deliver better solutions, reduce costs, improve the experience of their users, customers, and employees, and still generate value through insights. Check out some practical examples of data science in business and get inspired to start applying this knowledge in your company!
4 applications of data science in business
1) Financial and quality auditing
An excellent example of the power of data science in business appears in audits. To understand how processes, cash flows, metrics, and compliance are being met internally, it is possible to apply automated statistical models to identify errors, discrepancies, and improvement points that might otherwise go unnoticed by business management.
For small businesses, this may not seem very relevant, but companies that handle customer financial data, for example, can benefit from advanced data science techniques in their anti-fraud department, in addition to enabling greater process automation.
2) Understand your Customer Journey
Companies that take a more scientific approach to their commercial area often achieve even better results, because they can make better decisions and get even closer to customers.
In sales, data science helps map an even more detailed customer journey, providing better insights such as customer consumption behavior, preferences, interaction and experience, and other strategic metrics on where opportunities are and how to convert leads into loyal customers.
In addition, for the Customer Success area, it is possible to have more precise indicators of sensitive points in your relationship with customers, making it easier to implement initiatives that bring your business closer to your consumer.
3) Map the Employee Journey
An organizational aspect that could be better known is the employee journey. This approach, borrowed from the Customer Journey in the commercial area, helps map out every stage an employee goes through in your company—from recruitment and selection to eventual promotions, and retirement or offboarding.
Here, data science can help map employee satisfaction levels, identify improvements and skills gaps that could be addressed with training and interventions, understand possible reallocations, guide promotions, bonuses, and terminations, and increase the accuracy of your recruitment and selection process.
This is extremely important for reducing turnover, increasing the efficiency of your HR area, and ensuring holistic, more agile people management.
4) Improve User Experience
In a world where UI/UX concepts have gained greater prominence, it is possible to apply data science to reduce the distance between users of your product or service and your developers and service providers.
Through the use of automations, survey forms, and systematic feedback analysis, it is possible to improve LTV and deliver an even more transformative experience for your end user, ensuring they recommend your brand to other partners and creating better opportunities.
Moving beyond guesswork
In the business world, it is common to have insights throughout day-to-day operations. We always have ideas about why customer X decided to stop using the product, or why social media stopped bringing in as many leads as before. However, even if this _feeling_ helps point in the right direction, when it comes to taking action, only data can guide the best decisions for your company. So, avoid guesswork and embrace data!
Conclusion
Today, the volume of data generated per person daily is much higher than at any other time in human history. Every human activity currently leaves some kind of record in some database, which creates an unprecedented challenge for companies, digital media, and data science professionals—but also a business opportunity and greater depth in their operations.
But the truth is that all this collected data is only useful when it is actually used, and this is still a major challenge: how can you use all the data at your fingertips to drive every area of a business?
The simplest answer is: through data science—by building more precise statistical models, automating processes, forecasting scenarios, finding management issues, bringing customers closer to your brand, and above all, making the most important decisions based on real-world scenarios.
All areas of an organization can benefit from a data strategy and initiative: sales, HR, financial management, process management, logistics, marketing, IT, etc.
Where there is a decision, there is room for data analysis to guide it with greater accuracy. In this mission, companies like Erathos can help by delivering complete data engineering services, business intelligence, and an entire specialized data team at your disposal.
Request a contact and learn more about how to boost business through the use of data science.
Data has taken over the world. If in the first industrial revolution the most valuable asset was energy generation for machines and industrial processes, in the fourth industrial revolution (the one we are living through right now), knowing how to use data science in business in a multidisciplinary way is the key asset.
Without a doubt, we are living through the technological revolution of knowledge, which is about optimizing how processes are carried out through the use of the internet and computing, automating repetitive processes, and making better decisions while considering both sustainability and people’s quality of life.
In this blog, we will explore how to apply data science in business, and why this concept is important for the not-so-distant future.
What is data science?
To understand what data science is, we first need to understand its importance and usefulness. Have you ever stopped to think about the volume of data generated each day by every person on Earth? Research indicates that this number reaches up to 2.5 quintillion bytes every day—a surprising figure, but one that makes perfect sense when we consider that almost every human activity today depends on or generates data.
A purchase made in a store signals inventory reduction and the new cash balance in a system. A photograph generates an authentication code containing the date, time, and even the location where it was taken. Even when we try to escape the connected world and isolate ourselves from technology, we still go through data generation and analysis systems, directly or indirectly. So what happens to all this information being generated?
Well, that is the role of data science: to collect, clean, structure, understand, and transform this entire flow of information into something useful for individuals and organizations. When data is untreated and ignored, it is not very useful, but when there is strategy and technique behind these processes, data usage can be truly revolutionary.
Based on this discipline, it is possible to formulate hypotheses, retrieve relevant information, understand the quality and accuracy of collected information, and also structure data to prepare it for deeper analysis. Today, it is possible to build algorithms and automate a large part of this work, and this is already being done in many data-driven organizations. This improves data analysis performance, reveals new analytical possibilities that previously went unnoticed, and makes the process much more agile.
Want to learn more about this topic? Here on the Erathos blog, we have a complete article about it: click here.
Futurism versus Reality
In the media, we see data scientists as geniuses working in government labs, designing the new technologies of the future. It is interesting to observe how natural it is to think of science and business as two distant elements—but that is exactly where the danger lies!
There is indeed a large gap in the market between academic knowledge and practical knowledge, but data science should not exist as a separate entity, especially in the business world.
Therefore, thinking that integrated data analysis to boost the performance of each area in an organization is something from the future is a major mistake, because this knowledge already exists and is being applied now.
Companies that have already realized this are ahead in the market, because they can deliver better solutions, reduce costs, improve the experience of their users, customers, and employees, and still generate value through insights. Check out some practical examples of data science in business and get inspired to start applying this knowledge in your company!
4 applications of data science in business
1) Financial and quality auditing
An excellent example of the power of data science in business appears in audits. To understand how processes, cash flows, metrics, and compliance are being met internally, it is possible to apply automated statistical models to identify errors, discrepancies, and improvement points that might otherwise go unnoticed by business management.
For small businesses, this may not seem very relevant, but companies that handle customer financial data, for example, can benefit from advanced data science techniques in their anti-fraud department, in addition to enabling greater process automation.
2) Understand your Customer Journey
Companies that take a more scientific approach to their commercial area often achieve even better results, because they can make better decisions and get even closer to customers.
In sales, data science helps map an even more detailed customer journey, providing better insights such as customer consumption behavior, preferences, interaction and experience, and other strategic metrics on where opportunities are and how to convert leads into loyal customers.
In addition, for the Customer Success area, it is possible to have more precise indicators of sensitive points in your relationship with customers, making it easier to implement initiatives that bring your business closer to your consumer.
3) Map the Employee Journey
An organizational aspect that could be better known is the employee journey. This approach, borrowed from the Customer Journey in the commercial area, helps map out every stage an employee goes through in your company—from recruitment and selection to eventual promotions, and retirement or offboarding.
Here, data science can help map employee satisfaction levels, identify improvements and skills gaps that could be addressed with training and interventions, understand possible reallocations, guide promotions, bonuses, and terminations, and increase the accuracy of your recruitment and selection process.
This is extremely important for reducing turnover, increasing the efficiency of your HR area, and ensuring holistic, more agile people management.
4) Improve User Experience
In a world where UI/UX concepts have gained greater prominence, it is possible to apply data science to reduce the distance between users of your product or service and your developers and service providers.
Through the use of automations, survey forms, and systematic feedback analysis, it is possible to improve LTV and deliver an even more transformative experience for your end user, ensuring they recommend your brand to other partners and creating better opportunities.
Moving beyond guesswork
In the business world, it is common to have insights throughout day-to-day operations. We always have ideas about why customer X decided to stop using the product, or why social media stopped bringing in as many leads as before. However, even if this _feeling_ helps point in the right direction, when it comes to taking action, only data can guide the best decisions for your company. So, avoid guesswork and embrace data!
Conclusion
Today, the volume of data generated per person daily is much higher than at any other time in human history. Every human activity currently leaves some kind of record in some database, which creates an unprecedented challenge for companies, digital media, and data science professionals—but also a business opportunity and greater depth in their operations.
But the truth is that all this collected data is only useful when it is actually used, and this is still a major challenge: how can you use all the data at your fingertips to drive every area of a business?
The simplest answer is: through data science—by building more precise statistical models, automating processes, forecasting scenarios, finding management issues, bringing customers closer to your brand, and above all, making the most important decisions based on real-world scenarios.
All areas of an organization can benefit from a data strategy and initiative: sales, HR, financial management, process management, logistics, marketing, IT, etc.
Where there is a decision, there is room for data analysis to guide it with greater accuracy. In this mission, companies like Erathos can help by delivering complete data engineering services, business intelligence, and an entire specialized data team at your disposal.
Request a contact and learn more about how to boost business through the use of data science.