In many of our blogs, we are talking about how vital data integrity is. To clarify what it means, we decided to write a detailed guide for those that may have data integrity challenges and looking for solutions and best practices.
It’s undebatable: data is the foundation of modern business as we are living in a data-driven economy. Organizations rely more and more on the information available, whether it’s regarding their processes, customers, marketing, or financial data. The rapid development of technology has caused this. Nevertheless, while the amount of data we gather and utilize has grown extremely fast, too often, the integrity of the data was not prioritized.
Maintaining the integrity of the data has been especially challenging when data is shared across departments or organizations. While many have noticed the importance of sharing information, enterprises still too often deal with poor-quality data. Just by fixing data quality challenges, one can reduce downtime, improve troubleshooting time, and utilize the information better for decision-making.
What is data integrity?
Wikipedia defines data integrity as the following:
“Data integrity is the maintenance of, and the assurance of the accuracy and consistency of, data over its entire life-cycle, and is a critical aspect to the design, implementation, and usage of any system which stores, processes, or retrieves data.”
The goal of data integrity is to ensure that all necessary information is included in a message that was intended by the sender or data that is requested by the recipient. Data should always be reliable, trustworthy, traceable, verifiable, complete, and secure.
Typically, the term data integrity is used as a synonym for data quality that is usually in symbiosis with data validation that one may use in validating the content of the information.
There are many reasons for issues with data quality. Often, it’s just a human error – someone has forgotten to include some vital information in a specific data field. While other times, it may just be a hardware failure. Also, changes in the data may occur while replicating the information or transferring the message and translating the message standard or message format. Therefore, it’s crucial to use a reliable data integration service (we will talk about this later in this article). Nevertheless, incomplete data may be worthless for the recipient, so the validation process is almost a must.
It’s essential to mention already in the beginning that data integrity and data security are not the same. Unfortunately, data breaches may also cause issues with the quality of the data. Therefore, it’s essential that everyone at an organization complies with the data security policy, as well as with the data integrity one.
Database integrity
The idea of database integrity is very similar to data integrity. The only difference that in this case, we do not consider any information sent by any system or application, but only information that is situated within databases. Still, both senders and recipients need to be concerned with the consistency and accuracy of the data sent and received.
Databases are typically multi-user databases, so many people or departments have the right to access them and enter information. In this case, there is a lot of room for mistakes: someone may miss entering specific information, but there could also be logical errors or errors with the system that can result in corrupted data.
Data integrity challenges
Maintaining data integrity faces several challenges along the road. Let's look at those ones.
1. Data comes from various sources
Cooperation internally (across departments) and externally (with your ecosystem) is becoming increasingly important.
Nevertheless, integrating data from various sources jeopardizes the integrity of the data and leaves strains on the quality of the data. This then affects the usability of the data for insights and the value of the information.
2. Lack of data integrity policy
Once you commit to sharing business-critical data or receive information from your partner network, you should also ensure that you have a data integrity policy in place that will describe in detail what steps and processes need to be established to make sure that the data is always of the highest quality and 100% clean.
3. Lack of quality assurance (QA) and quality control (QC) review
You have QA and QC reviews in place for your products, services, or perhaps software, but you do not yet have one for your data?
That is something that needs to change.
As data is so robust and has so much value, ensuring the quality of the information you receive or send, shouldn’t be neglected either.
This is not only for the benefit of your employees. Your trading partners are more likely to do business with you in the future if they can trust that the information that they receive from you is accurate and reliable.
4. Lack of data governance system
According to Forrester, only less than 15% of companies have business-led data governance in place.
Having a data governance tool in place is essential for ensuring data integrity. If you want to evaluate the best software or tool, you want to take a look at Capterra’s list of Data Governance Tool providers and shortlist the most suitable vendor for your needs. Forrester also pointed out that no single data governance tool that can be used for all five data governance pillars (MDM, ILM, metadata, security). Although, some of the vendors can provide services to support most of these data governance initiatives.
On the other hand, some may not want to handle data governance stewardship as a separate cost. In that case, it can be a good idea to cooperate with an integration vendor that offers solutions for data governance while transferring your data from source A to source B.
You certainly need to find an adequate governance tool. For example, financial companies need a different one than those that need master data management (MDM).
5. Lack of validation process
The data validation process is an integral part of data governance, and it’s essential for ensuring that the data is cleansed and the quality of the information is useful.
Just by setting up a simple data validation solution, you will be able to become much more efficient.
Often, data is still “validated” manually by employees. It is not only extraordinarily error-prone but also time-consuming.
How to maintain data integrity?
It is vital to have a plan for how you are going to maintain data integrity, especially if you have tons of data or you are working with a lot of stakeholders internally and externally.
While it’s important to plan how you are going to maintain data integrity, you also need to ensure that you have clear guidelines regarding data governance and you communicate this effectively so everyone is on board with your rules.
To achieve this, you should take the following steps:
1. Have a data integrity policy
The best way to go about communicating your data integrity practices is to develop a data integrity policy that everyone needs to follow. Typically, a data integrity policy applies to both paper and electronic information. While data integrity does not relate to data security, many tend to take data integrity actions to ensure data security, too. Some may also include a data security policy as part of the data integrity policy. Nevertheless, it is not necessary.
Data integrity defines how one is supposed to handle the information, and how the validation or enrichment processes happen.
2. Management to stand behind the importance of the quality of the data
Any time you try to implement change within your organization, you should ensure that the management stands entirely behind the cause. This is no different in the case of data governance either. As the quality of data becomes increasingly important, getting support from the management to ensure that everyone understands this is necessary.
3. Stress the importance of governance and develop processes
When you develop your data integrity plan, make sure that you develop processes of how to ensure that the quality of the data is maintained.
Define clearly how you are going to handle the data, when does the validation and enrichment happens, how does it happen, and what action does it take from your team and from your partner.
4. Find the right vendor and tool
When you develop your data integrity plan, make sure that you develop processes of how to ensure that the quality of the data is maintained.
Define clearly how you are going to handle the data, when do the validation and enrichment happen, how does it happen, and what action does it take from your team and your partner.
5. The role of data integration in data integrity
Data integration tools have immensely evolved over the last few years. Most vendors have adapted to an integration platform as a service (iPaaS) to develop and deploy data integration solutions. If you want to read more about iPaaS, we suggest that you read our detailed blog on the topic.
While data integration solutions are built on top of an integration platform most commonly known for transmitting data from system A to system B, these can do so much more.
While transferring the data, the solution can also take care of the validation process (often based on your rules or the master data) and the enrichment. Once the solution validates the data and it detects any errors or missing data, it will automatically forward back to the sender so that they can make the necessary corrections.
Conclusion
Those that want to make sure the most out of their collected data ensure that their data integrity policy is on-spot, understood company-wide, and implemented well.
As data becomes a commodity ensuring its quality with minimal effort needs to be on the top of your mind. After all, the better data you have at your disposal, the better you can develop your business.
To learn more about how we help you with data integration and data integrity, get our eBook about iPaaS:
