The role of ETL processes in Data Integrity
Decision-making is based on multiple factors in an organization, but in recent times the majority of the decisions taken are strongly influenced by different representations of the data generated in all walks of organizational operations. These operations include sales, marketing, consumer interactions, finance, products, and more.
Data is an essential part of enterprises because the insights it generates are invaluable in determining several key performance indicators for the company, therefore the quality of the data can make a world of difference to these metrics. Generating and gathering data is now easier and normative, and the presence of this allows businesses to size the market and their competitors.
What is Data Integrity?
In layman's terms, Data Integrity is the separation between good and bad data; it is attributed to information accuracy, reliability, and consistency. Reiterating, data integrity is the state of your data, entailing completeness, accuracy, and, reliability, but, there are more aspects around integrity such as processes and methods to validate data previous to its use or execution.
Data Integrity can be broadly broken down into 4 major components:
ETL Process: it stands for Extract, Transforms and Load. This part of the data integration process is used to combine multiple data sources into one.
Data Security: this is a process of protecting digital information from unknown users throughout its lifecycle.
Data Quality: the accuracy and consistency of data that is used for specific purposes in organizations.
Data Integration: is a combining of data from multiple sources in a single one. The ETL process is a part of it.
Data Integrity process
It is worth mentioning that Data Integrity can be approached in two ways; as a state of data or as a process. The state is when data is valid and accurate, and the process describes measures that ensure the validity and accuracy of data contained in a database. For example, error-checking and validation methods are part of the processes.
Why does your company need Data Integrity?
According to the global consulting firm McKinsey, data-driven organizations are 19 times more relevant and profitable than their competitors. Data is changing how businesses operate and Data Integrity can take the spotlight here.
Many questions may rise when discussing data integrity: What is it? Why is it important? What is the impact on organizational operations and customer satisfaction? How can I assure Data Integrity within the organization?
Before mentioning the benefits of Data Integrity for your company, let’s understand the challenges surrounding managing data:
- Human errors: These are part of any department, just like deleting or duplicating data in a spreadsheet.
- Inconsistencies: This point refers to when there is a lack of standardization in entering information or data is of free form.
- Collection error: When data is collected, it is not exact or doesn't have enough information for correct analysis.
- Cybersecurity: Cyberattacks are also possible in the organization's systems and these could be carried out with bad intentions to damage or steal information.
Moreover, Data Integrity is necessary for any organization that wishes to succeed. Here are several reasons why companies need Data Integrity as a part of their processes.
- Protection: We know that Data Integrity is not synonymous with security, but is the path to taking measures and upkeeping processes to prevent data issues such as leaks or corruption. For today's businesses living in the digital world, protecting information is important, and the best option is to implement stages that facilitate information management, thus it will be easier to carry out maintenance of data.
- Appropriate decision-making: Using data not only represents collecting several volumes of it but also knowing the value that it contains. Paying attention to details grants your organization data reliability and higher levels of precision in the performance of your strategies.
For instance, imagine that the sales team executes a campaign based on incorrect data regarding your top customers. More than likely, that strategy will fail and could make you lose clients due to poor data interpretation and usage.
Making smart decisions is the main gear in a complete engine of a business. Data integrity participation enables data reliability because its responsibility is to monitor data quality and take care of precise details.
- Precise workflow: If the data that you need is accurate and reliable, Data Integrity represents access to correct data at the right time but the process will be successful only.
In order to avoid issues related to retrieval or access to data, this process allows having information whenever your business or team requires it, whether collecting data for research, campaign, or presentation, the company will get the necessary information for changing its future.
Types of Data Integrity
Data Integrity is a hot topic in the data world, and also it is important in different fields such as business, entertainment, politics, health, and education. That is why there exist different types of Data Integrity that describe particularities:
- Physical integrity: this is to protect data from natural events, power issues, or hackers. This point is related to recovering and retrieving data.
- Entity integrity: the purpose is to guarantee proper data storage, which means to have correct data and eliminate duplicated data or detect empty spaces on a board.
- Domain integrity: a domain is a cluster of values; in this sense, domain integrity refers to the precision of data that should be in the correct column in a dataset.
- Referential integrity: this is the process that ensures correct data through implemented rules. This prevents duplication and prohibits the entry of incorrect information.
- User-defined integrity: a set of rules defined by the user to accomplish specific requirements and commercial standards that entity, domain, and referential integrity can not achieve.
The workflow of organizations depends on Data Integrity, but beyond the benefits and types of data, there is another topic that connects with the main topic of this article, and this is the ETL process.
What is an ETL process?
The ETL (Extract, Transform, Load) process is transcendent to reach goals through data. The link between Data Integrity and ETL is to ensure that data processing will be complete and accurate.
ETL processes have to make tests to verify data. In the same path, ETL testing helps Data Integration to ensure data will be used in the best way to harness it and reach the organization’s goals.
All these processes function like an engine or chain, where one error can affect the final product. It is important to understand that Data Integrity is a stage that will help reach the next stage, Data Integration, which implies combining data from different sources into a single system.
When you need to integrate your information into one source, you would commonly extract, transform and load, but you need a strategy for the process to achieve a correct integration of your information. Here is where integrity appears.
Previously, at the data transformation stage, you must make sure that data is accurate and reliable on it for your business decisions. The optimized way to do transformation is with the support of Data Integrity and the process can be automatic with the correct tools and pipelines.
How to achieve Data Integrity through ETL processes
ETL can be a complicated process to carry out, but nowadays, some products and solutions can help make Data Integrity more precise and Data Integration more efficient.
Sometimes, the ETL process needs manual intervention from programmers since it requires advanced code. This involves a high level of skill from staff members, which includes error-free code, expertise in the entire process, and deep knowledge of data architecture. However, some organizations don't have specialized departments that can implement all steps with excellence.
Here at Arkon Data, offer a low-code platform and automated processes with the help of specialized pipelines which support information integration.
These automated processes allow advantages that facilitate all workflows in a company for obtaining valuable data for decision-making.
Benefits of Arkon Data
- User-friendly interface: Doing manual work from the ETL process is difficult even for IT experts, and modifying code in ETL means deleting old code and writing the new one. Our Arkon Data team has created functional, user-friendly features which need low programming intervention. Automated platforms bring benefits to the execution of processes such as Data Integration, and Data Integrity, which use pipelines to finish data processing with the expected quality. Thus, tasks need less time to be effective, resulting in cost savings as another avail.
- Support and maintenance: Processes like ETL can be written in multiple programming languages (Java, Python, SQL, among others), which implies that organizations require a team who solves problems and understands code with enough background knowledge. This cluster of obstacles makes maintenance expensive and delayed. Pipelines make it possible to identify errors that can be solved without code expertise, meaning every member of the team can understand what is the problem and how to tend to it.
- Cost-saving alternative: Manually maintaining an ETL process is expensive and takes away time off efficient work. With a unique platform that offers tools to make integration, governance, and BPMN like Arkon Data Platform, you will be able to manage your data in one place and you will avoid higher costs for doing it in alternative ways.
Managing data can be a difficult path if you decide to proceed, but fortunately, now there are many options that offer tools and features made with the best technology, which facilitates performing information through all processes that organizations need to reach their missions, visions, and goals.
If you offer any type of products or services, data is a potential ingredient to improve them, so, we recommend obtaining most of its value. The way to do it is through specialized processes, such as Data Governance, Data Integrity, Data Integration, Data Migration, ETL, etc.
In order to become Data-driven, you should have an ideal partner who guides you to harness your data.
Try Arkon Data Platform and obtain all the potential your data has in one place.
Do you want to know more? Find out how Arkon Data helps you achieve integrity in your database.