With market competition and new information technology solutions, data has become the main asset of companies, whether public or private.

Among the advantages of having the right information at hand, companies can achieve increased profits, cut expenses, optimize internal processes, and, most importantly, meet customers’ essential needs.

However, it is necessary to process these data until they become relevant when generating useful insights.

What is Data Science?

Not all the data generated by an organization’s processes and people is essential, so a science applied to information becomes essential.

Data science is the process of extracting insights that generate real value for companies from the information collected. It can happen in an automated way, with machine learning technologies, or through statistical analysis.

Data Science X Data analysis

Data analysis brings an understanding of a company’s data. According to Adam Hunt, CTO of RiskIQ, a software company that provides phishing and malware monitoring tools, data analysis investigates the information.

From the moment you use that observation to explain something, it becomes science. For Hunt, the real solution to the problem has more to do with data science than analysis.

The connection between data science and business data

Creating a data-based method of work is the best way to prevent failure and increase revenue so that it is necessary to integrate the areas of technology and business.

In addition to having an efficient structure, ideally with cloud computing, using the data requires a solution and a “non-technical” force, represented by professionals with knowledge in business and sales.

With this integration added to machine learning processes, the entire data storage, collection, and analysis process becomes sustainable.

Confira alguns benefícios que os dados podem gerar para as organizações:

Simplifies the understanding of what are the vital points for business

With all the information stored and processed, it is possible to identify patterns of customer behavior, market, and employees, improve their needs, focus on growth, reduce downtime, and cut costs.

Increases rejection and relationship with the public

Creating customer-centric strategies, where customers are the core of all operations, requires knowledge of the target audience. With the possibility of generating large amounts of data with digital marketing activities, IoT, etc., it is possible to simulate and seek answers to all the doubts that companies have concerning consumers.

Agility in decision-making

Data science is a relevant resource in decision-making. With the right information, the business area can make more accurate decisions quickly.