Data quality management (DQM) handles, in essence, a system for managing the quality of data — measured by the completeness, validity, accuracy, consistency, and availability of the data within your organization, while meeting statutory and regulatory requirements related to a product or service.
Data that is of high data quality achieves each of these characteristics to the extent that the data is able to perform its intended purpose. Whether its purpose means you have the ability to consistently and accurately measure your business’s trends in profitability based on available, accurate files and statements, or you maintain consistent, up-to-date records on the clients, customers or patients your organization services in order to provide the best care or level of service. Discrepancies in data, or missing data types both impede data quality, interfering with top business performance.
Data quality management helps businesses improve and govern data quality by determining the roles, responsibilities, procedures and policies dealing with the disposition, dissemination, maintenance, and acquisition of data. However, while data quality plays a role in your internal operations and dealings with clients, DQM practices can be easily overlooked due to:
- No business unit or department feels it is responsible for the problem.
- It requires cross-functional cooperation.
- It requires the organization to recognize that it has significant problems, or the potential for them.
- It requires discipline.
- It requires an investment of financial and human resources.
- It is perceived to be extremely manpower-intensive.
- The return on investment is often difficult to quantify.
While it’s easy to overlook the importance of data quality management, poor handling of data management costs businesses 600 billion dollars annually. By understanding, and not underestimating, the importance of Data Quality Management, your business can improve the quality of its data and avoid the financial repercussions of data inaccuracies and losses.
Challenges:
Businesses adopting better strategies for Data Quality Management first have to handle some of the internal challenges that such strategies can produce.
Many businesses, whether knowingly or unknowingly, do not recognize that they have a significant problem, or the potential for one. Even the businesses that do are often hesitant to budget financial resources and manpower to accomplishing improved data quality. Most of the time it is not until a catastrophic event within the business takes place before they realize that there is in fact a problem, often leading to negative financial repercussions and serious data losses that can damage customer relationships.
Misuse of DQM can also carry with it investment problems, such as the duplication of a customer so that the data is misconstrued and can negatively affect the company’s statements.
Another common challenge is that no business unit or department wants to take responsibility for the DQM, especially when their plates are already full and they don’t understand their part in the DQM process and the importance of it. However in order for DQM to really work, everyone who utilizes data needs to work together, or with the help of an IT consultant, to manage data quality, detail out definitions and common business rules for the management of data.
To ensure your company records and maintains accurate data, these challenges have to be met with strategic DQM practices and the discipline needed to create change.
Solutions:
Simply put, there are two broad ways to tackle the challenges businesses face when working to improve data quality: proactive and reactive components.
Proactive components deal with detecting potential problems in data quality before they become actual problems. These components consist of defining the roles and responsibilities of the data, establishing the accuracy of data and the overall governance of it, and supporting an environment that establishes these practices.
If and when a problem with the data becomes apparent, the reactive components can assist in dealing with them. For example, a customer may have accurate information on file so much so that billing is not a problem, but understanding the profitability of the customer is. Since this problem already exists, a reactive approach would be to acquire the information needed to understand the customer’s profitability.
Responsibility and Roles
An important guideline to follow is that DQM cannot be managed by a single person; rather it must be maintained horizontally in multiple departments, otherwise known as cross-functional. You can also get employees to take DQM seriously by appointing a data quality ambassador to oversee follow-through and implementation of the DQM strategy.
As previously mentioned, data quality management operates horizontally (or cross functionally) with each department, so as to ensure that there is no overlap in each respective department. A single person should not be responsible for all data quality operations, but by adding a data quality ambassador along with the horizontal operations, quality of data can be that much easier to manage. A data quality ambassador would be responsible for maintaining a constant focus on data quality and also for focusing on how data is continuously changing so that the process and culture of acquiring and managing the data stays up to date and works with your organization.
Employees in each department also need to understand their responsibility and stake in upholding data quality so that their commitment contributes to the betterment of data quality, rather than detracting from it. You can empower employees to better manage DQM by creating and communicating well-defined data management policies, with the help of your DQM ambassador.
DQM requires discipline. If the quality of the data is to be accurate, then employees need to know how to incorporate DQM into their day to day operations and facilitate data accuracy, quality and retrievability. Assigning specific DQM responsibilities to specific employees is one thing, but proper DQM practices should be interwoven into core responsibilities and data handling practices in order to improve and sustain the quality of the data at hand.
Building a Safety Net for DQM Failures
Even with employee and department cooperation, mistakes happen. Data quality can easily be derailed when important files or documents are overwritten, accidentally changed or deleted, or even when a catastrophic occurrence such as a virus or hardware failure contributes to the loss of data, therefore rendering data quality sub-par. Events such as these, which are sometimes out of your employees’ hands, can lead to missing or inaccurate data that contributes to operational downtime, lost profits, customer relationship headaches and more.
While it’s important to incorporate strong policies for adherence to DQM, it’s just as important to have a good fall back plan, should human error, or technical/hardware failure come into play.
Ideally, this safety net should:
- contribute little to your company’s financial investment in upholding data quality
- be robust enough to serve as a fallback option for any data quality failure
- allow you to recover data from any point in time (with no expiration date on these versions), despite accidental changes to data, losses, or deletions
- reliably manage itself, without additional internal resources from your staff
Many businesses have found a reliable safety net, with each of these qualities, in cloud backup. Cloud backup as a service can be used as both a proactive and reactive solution to data quality management. Cloud backup, like that provided by Nordic Backup, allows businesses to recover data from any point in time when a file change or deletion damages the quality, integrity or accessibility of critical data.
This key trait saves the day when accidental obstructions, unexpected catastrophes, or malicious activity strikes, rendering data inaccessible or irretrievable. Ransomware is a primary example of cloud backup saving the day. Ransomware is a type of malware that encrypts data, rendering it unreadable and requires ransom be paid before that data is released back to the business. This malicious virus, proliferated by cyber criminals, can lead to incredible data loss, critically jeopardized data quality and hours or even days of lost work. With Nordic Backup in place, you can start from a clean state to remove the virus, then simply restore your data from a date before the virus struck.
Key Takeaways
Data quality management is vastly important and requires the full cooperation and discipline of the business and it’s departments and staff in order to keep accurate, reliable data. Each employee understanding his/her job and having the will to complete it can go a long way in maintaining DQM and the cross functionality of the employees will only guarantee more attention and precise data calculations. If data is kept up to speed and with detail, than the business can stray from problems like customer duplication, overall absence of data, and other potential investment problems, and instead bring about improved information productivity and being able to better serve customers. DQM is a necessity of business, and with discipline, attention to detail, and the right safety net, your data quality management strategy can save your business from the financial repercussions of false, lost, and deleted data.
Let Nordic Backup assist you in backing up your data to prevent data loss and recover from DQM failures across your organization. Our reliable cloud backup solutions and sophisticated, zero-downtime data recovery tools have been helping businesses eliminate downtime and data loss for over 15 years.
