There are several advantages of Database management system over file system. Few of them are as follows: No redundant data: Redundancy removed by data normalization. ... Each user has a different set of access thus data is secured from the issues such as identity theft, data leaks and misuse of data.
Your staff needs to have constant access to data of quality
data. Data integrity is important as it guarantees and secures the
searchability and traceability of your data to its original
source.
Data performance and stability also increase when you ensure
effective data accuracy and data protection. Maintaining the
integrity of data and ensuring the completeness of data is
essential. Compromised data is of no use for most companies.
This includes big data. Organizations collect more and more data
and it has become a priority to secure and maintain the integrity
of this data. Without integrity and accuracy, your data is
worthless.
If you fail to do so, your data could be compromised. In this case,
cumbersome and expensive data audit trails could be necessary to
trace errors and recover the data.
Data is now more important than ever and most companies have set
specific goals for their data. But without integrity, data is not
of much use. Moreover, data loss, corrupted or compromised data can
considerably damage your business. Ensuring data security has
become a number one concern of many organizations.
Data integrity can be compromised in many different ways. Data is
mostly digitaland is transferred online in a number of places,.
This results in an increasing amount and varied types of data being
collected.
For example, data should remain unaltered whenever it is
transferred or replicated between updates. As a result, data
integrity and data integration can be closely related.
Data is not static, stored once and for all in your systems. Many
things can alter your data from the very first day it is created in
your system and throughout its lifecycle. It can be transferred to
other systems, altered and updated multiple times.
Organizations typically use several management systems (ERP, CRM,
SCM, etc.). Things such as human interactions, data transfers,
software viruses or compromised hardware can compromise your data’s
integrity. This is where maintaining data integrity can become a
tricky task.
In my job, I am accustomed to providing data integration solutions
to organizations. I am also used to discussing data quality and
data integrity in great details with various companies. Most of my
customers know that it is crucial to improve data integrity.
Data integrity should be at the top of your mind at every stage of
your data lifecycle. From the design and implementation phase of
your systems. Data integrity also ensures recoverability and
searchability, traceability and connectivity. Protecting the
validity and accuracy of data also increases the stability and
performance of your systems.
HOW DO YOU SECURE DATA INTEGRITY?
What helps to ensure data integrity? Ensuring the integrity of
your data within a database at its design stage through the use of
standard rules and procedures. Error checking and validation
routines can ensure that data hasn't been compromised between
transfers and updates. This has to be done at every step in every
process.
Preserving data integrity should be the top priority of your
organization.
Bad data can have significant business consequences for companies. Poor-quality data is often pegged as the source of operational snafus, inaccurate analytics and ill-conceived business strategies. Examples of the economic damage that data quality problems can cause include added expenses when products are shipped to the wrong customer addresses, lost sales opportunities because of erroneous or incomplete customer records, and fines for improper financial or regulatory compliance reporting.
From a financial standpoint, maintaining high levels of data quality enables organizations to reduce the cost of identifying and fixing bad data in their systems. Companies are also able to avoid operational errors and business process breakdowns that can increase operating expenses and reduce revenues.
In addition, good data quality increases the accuracy of analytics applications, which can lead to better business decision-making that boosts sales, improves internal processes and gives organizations a competitive edge over rivals. High-quality data can help expand the use of BI dashboards and analytics tools, as well -- if analytics data is seen as trustworthy, business users are more likely to rely on it instead of basing decisions on gut feelings or their own spreadsheets.
Effective data quality management also frees up data management teams to focus on more productive tasks than cleaning up data sets. For example, they can spend more time helping business users and data analysts take advantage of the available data in systems and promoting data quality best practices in business operations to minimize data errors.
Emerging data quality challenges
For many years, the burden of data quality efforts centered on structured data stored in relational databases since they were the dominant technology for managing data. But the nature of data quality problems expanded as big data systems and cloud computing became more prominent. Increasingly, data managers also need to focus on the quality of unstructured and semistructured data, such as text, internet clickstream records, sensor data and network, system and application logs.
The growing use of artificial intelligence (AI) and machine learning applications further complicates the data quality process in organizations, as does the adoption of real-time data streaming platforms that funnel large volumes of data into corporate systems on a continuous basis. In addition, data quality now often needs to be managed in a combination of on-premises and cloud systems.
Data quality demands are also expanding due to the implementation of new data privacy and protection laws, most notably the European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Both measures give people the right to access the personal data that companies collect about them, which means organizations must be able to find all of the records on an individual in their systems without missing any because of inaccurate or inconsistent data.
Fixing data quality issues
Data quality managers, analysts and engineers are primarily responsible for fixing data errors and other data quality problems in organizations. They're collectively tasked with finding and cleansing bad data in databases and other data repositories, often with assistance and support from other data management professionals, particularly data stewards and data governance program managers.
However, it's also a common practice to involve business users, data scientists and other analysts in the data quality process to help reduce the number of data quality issues created in systems. Business participation can be achieved partly through data governance programs and interactions with data stewards, who frequently come from business units. In addition, though, many companies run training programs on data quality best practices for end users. A common mantra among data managers is that everyone in an organization is responsible for data quality.
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