Database Application: Amid the information age, with technologies like Big Data emerging, database management assumes a strategic position for companies. It involves information technology and areas such as marketing and strategic business management.
Data analysis allows for promoting a personalized customer experience, taking the relationship between brand and consumer to another level. Consequently, with decisions based on data, a company can stand out in the increasingly competitive market.
If you still need convincing of the importance of database management for a company, read this post. In it, you will understand and learn to apply the benefits of this practice.
How Important Is Database Management?
One of a business’s greatest assets is its information — especially in today’s market, where dynamism and quick responses make all the difference.
The entire operation of a company — its business strategies and decisions — is guided by the analysis of this information. Hence the importance of database management.
A database consists of files that communicate to store information. With ever-increasing volumes, managing these banks has become essential for companies.
According to surveys, 88% of IT professionals indicate the database as the main problem or challenge in business application performance- which reinforces management’s importance.
No matter how small the business (as long as it has software, whether for financial management or customer registration), there is a database. It stores, processes, and provides information to managers, albeit in a simplified and automated way.
However, there is only a point in having quality information if the database is well managed. Good management is essential to allow managers a quality analysis of the direction the company is following, indicating where it is necessary to focus and the points of improvement and correction.
In this sense, all kinds of data can be analyzed — from the quality of service and the performance of marketing actions to team productivity and sales growth.
In addition, the customer also wins! From the data collected, their needs are recognized, enabling the company to provide an improved shopping experience.
In today’s market, customer experience is a brand’s competitive advantage. And it is precisely the block of information at the company’s service that allows it to offer a personalized service, leading to satisfaction and loyalty.
What Are The Main Types Of Databases?
Several types of databases differ in some aspects, such as the interaction between objects and the modeling of the database. Among the models, we have:
This is the first DBMS model. In it, data is sorted hierarchically in a descending tree scheme. Pointers are used between the various records.
The organization is similar to that of hierarchical banks. However, each record can be linked to more than one superior record. Thus, complex connections are created, which is why they are used in systems for large computers — mainframes.
Data are stored in tables, and their definition is based on predicate logic and set theory. This model is suitable for underlying DBMS.
Used for data warehouses, it is a logical design technique. It contrasts with entity-relationship modeling, which should be used for the administration phase of building a data warehouse but avoided in the end-user system. In this case, dimensional modeling is the only viable technique.
In this model, data is stored as objects, structures called classes that contain data members. Fields are instances of these classes. The most used model is the relational one, the basis for the main software. It is the modeling that defines the language and structure of the database.
How To Create Efficient Databases?
With technological evolution, the software is increasingly prepared to interpret a large volume of data, being able to identify patterns or divergences — data mining.
In this sense, data analysis to predict trends is increasingly important for companies. It serves as a basis for developing products or services and creating marketing campaigns, in addition to helping managers make business decisions.
However, for data analysis to achieve these goals, it is necessary to follow some steps. See below.
There’s no point in looking at data in vain, so every analysis must answer a question. It takes a strategy behind the assessment with a well-defined focus.
Set an objective that is relevant and clear, as well as measurable. Consider how this analysis will be useful, talk to team leaders, and map problems, demands, and long-term goals.
Another important step is the definition of metrics. Data analysis can assume different approaches according to the theme and objective. The use of indicators guarantees the quality of the insights.
Data analysis can be quantitative or qualitative. The first is numeric, while the second involves more specific characteristics of the object of study.
Just as quantitative results must be tabulated, qualitative data must be categorized and organized with their respective frequencies and percentages. This step is as important as reading the information.