The time when data was scarce or difficult to analyze is in the past. Nowadays, people’s data circulates easily. After all, you need to provide this personal information in everyday situations.
You’ve certainly found yourself in a physical or online store, filling out a customer record. Or, you shared your personal ID number at the supermarket once you finished your purchase. It is also very common to use personal data such as email and phone numbers to use social networks.
In fact, in the modern world we live in today, the amount of data collected daily by small, medium, and large companies is immeasurable. And that’s why data analysis is so important.
Companies are increasingly investing in technology and hiring qualified professionals to be able to filter and extract the most important information from the data collected daily.
It is also important that everyone is aware of scams and possible data leaks. After all, as people’s personal information is more easily available, many criminals take advantage of this reality. And so they manage to take money from common people and also from the big corporations of the world.
With this critically important area in mind today, in this article you’ll understand what are the biggest trends for data analytics.
What is the use of data analysis in companies?
With the advancement of technology, companies have been investing in technology and hiring qualified personnel so that it is possible to use data strategically. Thus, employees analyze the data and contribute to strategic business decisions.
What is changing, however, is that data is now being treated more carefully. The customer needs to be at the center of decisions, which is why analyzing all this information carefully is a big trend.
At this point, it is important to emphasize that today the critical evaluation made by humans cannot be replaced by algorithms and machines. This is because, to reach more and more people, a company needs the human factor present.
Advantages for the market
The benefits of this type of market approach are diverse, including quick sales analysis as well as consumer profiling for new product development.
Thus, with an efficient data analysis process, a company in the food sector, for example, can perceive that the consumption profile of its customers is changing. And with that, instead of developing greasy products without a reliable origin for its inputs, they are looking for new suppliers and inputs of organic origin.
The result of this action based on data collected from the market is a movement of brand recognition and social impact. And so people start to promote the company in a natural way. In addition, they value the investment in developing products that are in accordance with what customers are looking for.
The same happens with social and environmental causes, recycling programs, reduction of environmental impacts, among others. When a company manages to communicate these attitudes, it is natural that they are recognized by them. In this way, the reach of their products and/or services grows exponentially.
Modernizing data management
As it was already possible to notice, data is present in our daily lives, both in our personal and professional lives. All actions performed in a virtual environment (mobile phone, computer, television, etc.) can be used by companies.
Likewise, data provided in physical environments also helps companies to understand more and more who their customers are.
However, since the beginning of Industry 4.0 and the use of strategies and technologies such as big data, a great challenge has existed. This challenge is the ability of companies to manage all the data collected.
Today, considering a new stage of big data, companies need even more control over this information generated for deeper and more effective analysis.
Therefore, companies need to be aware of their data management systems. And unfortunately, this reality is still not common in Brazilian business.
How to modernize data management
When a company does not have any structure for data analysis, it is essential to create a qualified team to put this project into practice. In addition, of course, there is support from the business leaders and investments when necessary.
On the other hand, when the company already has a certain base, it is important to verify it and ensure that data collection and storage are being done correctly.
With the entire base organized and well-structured, it is possible to develop new processes and modernize the management as a whole. For this, the company needs to have good communication between departments and work concisely to perform this task correctly.
In this sense, understanding where the information is coming from and also having a clear path to follow from the collection is what differentiates efficient data management from inefficient ones.
What happens to the data that reaches the company? Who is responsible for the business’s data strategy? Is there a well-defined workflow for employees to perform?
All these questions are important and must be answered in order to have modern data management.
Process automation is necessary
There is no doubt that the automation of processes, as much as possible, is necessary for companies. And much of what can be automated happens through Machine Learning.
This is important because the amount of data is increasing. And because of that, the difficulty of analyzing all of it with due attention also continues to grow.
Manual and operational activities in the companies, such as preparing data and also discovering insights, can be automated. This happens because machines and their advanced algorithms are able to learn to perform repetitive activities with Machine Learning.
Then, analysts and data specialists are able to have more time and dedication to perform more advanced, value-added activities. Consequently, the company is able to stand out even more and increase its presence in the market.
Therefore, with the correct use of Machine Learning, it is possible to have a more interactive data analysis, with collaborative tools and more natural communication. The time of static dashboards is coming to an end, making room for more dynamic solutions.
There are already several sectors benefiting from process automation through Machine Learning. This is possible through investment in technology, qualified personnel, and active data analysis. Reports from the previous month are no longer useful, companies need to be agile and make decisions based on the collected data.
Ensuring customer trust
As organizations use more customer data, people’s trust begins to erode. To make this scenario worse, there are constant cases of data leakage and data control issues in companies.
Therefore, it is necessary to work ethically and build a relationship of trust with consumers. Teams dedicated to data security and communication of actions related to this personal information is an interesting attitude.
After all, upon realizing the company’s concern with their personal data, consumers naturally trust the organization more.
At the same time, decision-making must continue to be done by humans. This is because, when faced with ethical dilemmas with customer data, only human critical sense is capable of correctly analyzing the scenario.
Bringing consumers closer to Machine Learning actions and data usage also helps build trust.
So, it is possible to see that ensuring the anonymity of the data, as well as making it clear what the company does with this data, are good actions to take.