Regardless of the area, size, or niche of operation of a company, one thing is certain: data management is as essential as it is complex. After all, it involves different sectors of an organization and requires employees to know the main types of errors and how to avoid them.

However, most businesses end up making mistakes in the small details and compromising the entire management with recurring failures.
According to research conducted by SEBRAE, most business owners still face difficulties in dealing with the new technologies available in the market. This is completely understandable, especially considering that 35% of Brazilian entrepreneurs have only completed elementary school.

Undoubtedly, we need to find ways to overcome these challenges and ensure good management of data, especially since the digital era is here to stay, and any business that wants to survive in an increasingly competitive landscape needs to be part of this revolution.

In today's blog post, we at CodeBit have listed the most common data management errors and some strategies to avoid them.
Read on and follow along.

1. Absence of a backup policy

The first step in establishing efficient data management is to define a clear backup policy that integrates media backup procedures, retention processes, and recovery techniques.
After all, backup mitigates human errors and ensures that information remains protected and accessible, even in critical situations, such as when systems are compromised.
In the era of digitization, with so many resources available, total data loss is unacceptable, especially if the business's value is concentrated in the information.
Therefore, it is fundamental to determine a retention policy that stipulates the period for which data will be retained, according to the company's area of operations, managers, and the information technology sector.

2. Ineffective normalization

Normalization is the process that transforms an approximate layout of tables and columns into a database design that follows a specific set of rules and requirements, contributing to the reduction of redundancy and resulting in greater efficiency.
In this context, the management of a database must follow normalization (except in cases of design for a Data Warehouse, which determines a different set of rules).
Generally, the procedure varies according to the business's area of operation, the database's objective, and the projects developed.
Each developer may adopt different rules and use their expertise and mastery of data to complete different projects.
Ineffective normalization can cause a series of problems with the database, such as duplicated information, deleted entries, data removals, and much more.

3. Unlicensed software

Using unlicensed software, in addition to demonstrating a lack of professionalism, can also cause numerous conflicts in data management processes. After all, without manufacturer support, it is not possible to update systems or report and apply fixes. It is worth noting that many software options offer free versions, but with limited features and functionalities.
Therefore, when defining a database solution, patch management is imperative to secure a licensed product, to have specialized monitoring and support whenever necessary.

4. Redundant data

Data redundancy is very similar to normalization, but they are still different issues, since it is possible to have a seemingly normalized database that presents redundant data (or, in other words, unnecessary information that does not need to be stored).
As an example, we can mention data stored in other systems or information that can be calculated automatically (such as the current age of a particular customer).
To prevent this type of problem, the most recommended approach is to invest in a central account management system that includes password validation. Thus, there will be no need to store access keys in the company's database.

5. Poorly estimated sizing

For a database system to operate effectively, resources must be sized correctly.
In most situations, the default installation does not work adequately, so some factors such as disk size, the number of processors, configuration of memory quantity, and the number of sessions that can be opened must be analyzed and sized customly.

6. Using the wrong data types

During the process of designing a database, the stage arrives where you must determine what data types each column should present. Normally, there are three columns: number, text, and date.
However, within each of these, there are several others, making it fundamental to categorize correctly.
To do this, it is necessary to take into account the individualities of each business. For example, if all account numbers are numeric, the storage should be done in the field corresponding to that format.
If the numbers are written as text, the correct option is to store them in the column corresponding to text.
Great care must be taken in data entry, because information recorded in incorrect formats can cause problems in storage, and consequently, in recovery.

7. Using outdated versions

Most of the time, DBMS (Database Management Systems) allow the installation of a trial version.
However, when the trial period ends and the tool is contracted, the application of patches (updates or bug fixes to optimize usability or performance) is necessary.
This action solves several problems, ranging from incompatibilities with system resources to database language settings.
In short, patches fix all application instabilities. In view of this fact, it is important to prevent the system from running on the raw version and run all updates made available by the vendor.

8. Not investing in security

Considering that information is one of the most powerful tools a company can have, having security features is essential to protect the data that circulates through computers and mobile devices.
Taking into account the highly competitive market, it is also paramount to protect the company's information from competitors and, at the same time, keep it available (in a secure manner) to facilitate queries and drive good customer relationships.
In general, data security, in the enterprise environment, encompasses everything involving the protection of information of natural and legal persons, and is based on the following pillars:

Confidentiality

Guarantee that an organization's data is restricted to authorized access

Authenticity and integrity

Full preservation of the original characteristics of the information and prevention of possible modifications (whether caused by internal failures or hacker attacks).

Without a doubt, a database server is a very sensitive asset. In view of this, its access must be restricted to employees who have appropriate credentials identified by the business's privacy policy. Another equally important factor is to use secure and unique passwords.
Password encryption tools are one of the most basic measures to protect access to systems.
In short, encryption is a security feature that uses specific keys to scramble characters and make information unreadable, preventing password content from being accessed by malicious software, hackers, etc.
Without access to the cryptographic key, it is extremely difficult to translate the content, therefore, encryption hinders criminals' work in gaining access to the credentials of a company's employees.

#Extra Tip: Why you should invest in Cloud Computing

Cloud computing, also known as