The advances brought about by technological transformation are changing the world, and to fully leverage these advances, it is necessary to explore the capacity of the data they generate. Today, we have not only an abundance of data but also an abundance of technological innovations that alter the way we collect, store, and analyze this data, this information.
Every day, the amount of data increases, and it is necessary for the processing and analysis of this data to be done quickly. This is a current challenge. Generating analysis reports quickly so that they can guide the company’s goals and objectives and ensure that the best decisions are made.
To ensure the best decisions, tools and methodologies capable of transforming data into insights are used.
A widely used methodology is Data Science, which involves collecting data from various sources to analyze and support decision-making based on predictive analyses and insights. For data analysis, one of the methodologies used is CRISP DM (Cross Industry Standard Process for Data Mining). This methodology brings together best practices to make Data Science as productive and efficient as possible, analyzing financial data, human resources, production, customer habits, or any important data within the business, to propose improvement models or problem-solving solutions.
The CRISP DM methodology is based on the collected data, and its process is divided into 6 steps:
1 – Business Understanding
The first step is to understand your business and the problem to be solved, seeking all its impacts on the company.
2 – Data Understanding
This is where data mining truly begins. After collection, it is necessary to organize and document the data to understand important aspects for problem-solving and observe important business trends.
3 – Data Preparation
With the data collected, documented, and analyzed, it is time to choose the data to be worked on and define how they will be cross-referenced to solve the problem at hand.
4 – Modeling
Here, Data Science techniques are applied, and predictive analyses are conducted so that the company can prepare for the future, addressing the main issue. The mined data can be used to feed algorithms that predict business trends.
5 – Evaluation
This moment is important because it is when the results are monitored in relation to the objectives and also the application of the knowledge obtained with Data Science.
6 – Deployment of Data and Presentation of Models
Here is the moment of truth, where all the knowledge obtained from the data is delivered in a way that can be applied. From this, processes within the organization can be changed, and new products can be created based on data, thus ensuring business success.
What are the Advantages for your Business?
As mentioned earlier, Data Science is capable of transforming a quantity of data into business insights through statistical methods and algorithms that find patterns in the data, which helps companies make more strategic decisions, resulting in better outcomes and making predictions about what will happen in the future.
Check out 4 advantages of Data Science for your business:
1 – Precision in Decision-Making
With a complete view of all the information about your business, it is possible to have insights and information based on effective analyses for decision-making. This allows for quicker action and, consequently, better results. Companies with a “
“In God we trust. All others must bring data.”
2 – Maximizing Return on Investment
For companies that invest in technologies and seek quick results, this is an important indicator, and Data Science allows for its optimization and increased financial return.
With data analysis, it is possible to verify which actions bring returns and which need improvement. Thus, the marketing, sales, and technology teams can invest more in activities that bring more returns and quickly correct those that did not yield satisfactory returns. Data Science enables the “fail fast, learn fast, and correct fast” approach.
3 – Customer Acquisition and Demand Forecasting
With the amount of information available, customers are becoming more demanding regarding services and support. As a result, acquiring and retaining customers has become a challenging task. By using Data Science, it is possible to collect information about the target audience, carry out targeted actions to acquire these customers, and engage with the customer base more efficiently.
4 – Security Optimization
With the advancement of technologies and the increase in data, there is also a growing need to stay ahead of cybercriminals and protect corporate systems. By using Data Science, it is possible to study recurring behavior patterns in your network and identify weaknesses. This allows for strengthening security strategies and preventing data breaches, fraud, and other threats.
Is your Organization Truly Prepared for Data Science?
How about answering the following questions yourself:
- Does your organization already have experience with data intelligence but finds itself stagnant in the face of innovations?
- And, as a manager, do you have any difficulty extracting information, making forecasts and market analyses, and improving your decision-making process?
If the answers to any of these questions were yes, you probably need a partner for your Data Science journey.
In this case, rely on Effortech’s advanced solutions and make your data work for your business’s purpose.
Remember: investing in technologies is essential to having a competitive edge in the market. Get in touch with one of Effortech’s consultants and discover the personalized services and advantages of innovating.
