Among the many good things automation has enabled, such as improved efficiency across operational areas, better access to data is one of the most important. Data can provide insights that can help in developing focused strategies, be it in improving production, controlling costs or increasing customer success.
However, organizations are not necessarily reaping these benefits. In fact, an Experian Data Quality research reveals that US businesses are making losses to the tune of $600 billion, as much as 12% of revenues, due to bad data. According to another estimate, it is as high as $3.1 trillion annually!
Data for Decision Making
Every organization has a variety of information technology systems that ensure ease of operations. They could range from ERP, EQMS, CRM to financial accounting, some of it on premises and some on cloud. Today, organizations also have access to external data – quantitative and qualitative - adding to the wealth of information they can leverage for better decision making. If applied correctly, data can enable companies to identify and respond to new opportunities, quickly turn around root cause analysis of failures into actionable intelligence, and learn from the organization’s experience to start predicting future events. Needless to say, data improves overall efficiency and productivity and further enables the speed and agility of an organization to gain competitive advantage.
Considering the value data can bring to an organization, data quality is important because without high-quality data, decision making becomes difficult and reduces confidence and trust in making knowledgeable decisions. Companies are facing a wider range of challenges than ever before, especially for highly regulated manufacturers, and data accuracy has become much more important. That’s why data accuracy and data quality improvement has become crucial.
Although human error is the main source of data problems, there are several steps to getting to data quality. Some of the key components to achieve quality data are:
- Usefulness – only having data that is purposeful and complete
- Accuracy – identify data entry points which show excessive errors
- Consistency – use templates, guides, instructions and training to aid in reducing inconsistency
- Cleanliness –perform data cleansing on a regular basis for a consistent database maintenance
- Governance – gain control of who, how and what data is entered into key systems to ensure the integrity of data
- Timely - lastly getting the right data at the right time
Inaccuracy is inevitable whether due to human error or due to data migration. Utilizing tools either through good governance control or use of automated software can help organizations maintain high data quality. If we can improve the quality of data, then we will have better information to support decision making. Better decisions will lead to better outcomes/results and will in turn be likely to have better quality data arising from them.
Behind the Scenes
Having such high quality data is the ideal, but the reality is far from it. Challenges emerge due to data being available in silos and in disparate formats, from textual content, images, videos to databases and social media content, amongst others.
Within an organization, the coexistence of legacy and new systems is one reason for such data variations. Mergers and acquisitions can compound the problem as the two merging entities are likely to have different systems for managing their different operations and store them differently. Or as stated previously, human error, which is inevitable and persistent.
Some of the symptoms to detect data mismanagement would be missing or hard to locate data, too many errors in the documents and files, increasing customer complaints and inability to resolve them to the satisfaction of the customer, missed deadlines and difficulties during audits.
As a result, organizations face multiple challenges across the entire manufacturing process from design to delivery, including:
- Product quality
- Supply chain planning and management
- Process quality
- Contract negotiations
- Output forecasting
- Energy efficiency
- Customer management
- Service innovations
These can have repercussions right from regulatory compliance to customer satisfaction and, of course, revenue losses.
QMS to the Rescue
Given the criticality of data for an organization’s growth, efficiency and effectiveness, access, management and organization of data becomes important. Quality data needs quality processes and systems to support data management. Quality Management Systems can help by supporting the several areas needed for data quality.
The following four steps involved in data quality management can be better managed by organizations that have implemented a good quality management system:
|Data Quality Management||Quality Management System|
|Data Profiling: This includes reviewing the data, analysing its health based on its metadata, running statistical models and documenting the quality of the data. This leads to the next step of establishing the quality goals.||A QMS can help streamline the process that can help access data during auditing and improve compliance|
|Defining Data Quality: Based on the assessment of the existing data, the organization next sets quality goals based on the business goals. This helps in establishing what to focus on to make quality management more effective.||This also helps define the training needs and help in managing and coordinating schedules|
|Data Reporting: This involves identifying, recording and removing data that does not fit into the established quality rules. Reporting and monitoring are critical for ensuring ROI.||Change management ensures that all authorized users have access to the latest version|
|Data Repair: This requires assessing the root cause for data distortion and setting a process for remediation as well as prevention of future loss.||Improves CAPA by timely rectification and implementing preventive measures|
Leave it to the Experts
A good quality management system such as the one offered by Compliance Quest can help organizations streamline their data quality management. It can provide a clear audit trail, stores data in a secure manner accessible through proper authorization, enable appropriate training, help in change management and take appropriate corrective action preventive action (CAPA).
CQ’s years of experience in handling data, quality management systems and processes make it the right partner for supporting any organization’s data quality improvement needs. Its EQMS solution is reliable, versatile and scalable for all sizes of companies with built-in best practices and seamless processes running on the latest modern-cloud Salesforce platform.