Feedback Loops: How Can They Improve Data Quality?

Consider the following scenario: Max, a Marketing Analyst, reviews reports to see how a specific product performed. While he discovered the figures, something seemed odd because the ones given at the weekly meeting yielded higher outcomes. There are numerous options, and it is impossible to investigate each one to understand why there is a discrepancy. This causes a schism and a lack of confidence among teams, resulting in data and processes scattered across a business. It is frequently the case that those who notice a quality issue are not the ones who remedy it, nor do they have the necessary expertise. Knowledge distribution occurs naturally as businesses grow. Data quality management is a real-world issue in enterprises, producing disruptions, inaccurate reports, and roadblocks to opportunities and decisions. If data quality deteriorates, so does stakeholder trust in the data, resulting in a shaky foundation.

Today's technology allows us to obtain input in a variety of ways. We have apps that track and record our health and fitness levels. Thanks to the Internet of Things and Artificial Intelligence, we see suggestions based on our actions, clicks, and surfing activity thanks to the Internet of Things and Artificial Intelligence. Technology in businesses collects real-time feedback on customer interactions, team engagement, and product performance.

A typical development process begins with corporate leaders and other stakeholders envisioning a new product, followed by a development partner giving data samples to begin development. So, sample data are generated using trial-and-error methods that are unlikely to predict what data will be generated when the product is incorporated into a real-time environment.

Data analysts and data scientists struggle with data quality issues. The data needs to be cleaned up and converted into something that they can analyze, a lengthy process. Additional sources and ever-growing volumes of data further complicate the process unless the process is fully automated. So, how can we solve this problem??

Some organizations look for ways to overcome the problem by purchasing real-world data (RWD). Due to the high cost of RWD, organizations are uncertain of the actual results of their investment beforehand, which makes this problem even more pressing.

With feedback loops, data quality teams can monitor, control issues that happen in Real-World-Data, and constantly improve the data testing framework to build a reliable product. Implementing feedback loops requires considerable effort. However, they help to detect an issue and aid in distributing the workload of addressing data quality issues by sharing the responsibilities between stakeholders at various levels across an organization.

Additionally, keep the following in mind.

Incorporate Sourcing Experts

It takes more time and resources than most can devote to create a list of domain experts across diverse enterprise datasets. A lack of human interaction slows down the resolution process and leaves those with less expertise searching in vain for answers to pressing questions. Machine learning tools make it possible to identify and rank these domain experts, enhance the quality of answers, and decrease the resources required to interrogate.

Address Data Quality Issues

It is critical to be attentive to the quality of the data used in the companies, as much relies on a foundation of reliable data. For nearly any business problem, data-driven learning enables users and machines to gain firsthand insights.

Organizations have been able to address poor data quality with the help of data integration and preparation tools and support decision-making processes with consistent and accurate data. The best way to accomplish this is by establishing effective feedback loops between those working with data and those contributing to producing it. Allied with this two-part data value chain, we can potentially improve processes to address the causes of poor data quality.

Control Relevant Data

While the department's views must be sufficient, a thorough analysis of the entire enterprise should provide better results. A department within an organization may rely on its database and sit in data silos. Having members and experts from related departments interact with each other across the organization is often a practical solution.

ETL is a lengthy process that often results in the deletion of data when part numbers in one database correspond to model numbers in another. Data sources that are hard for ETL systems to process can provide insight into isolated data.

Facilitate the Conversation

Facilitating conversation between those working on the front-end (data gatherers) and back-end (data analysts) would help to improve data quality at its source. If the critical team members in the data value chain understand their needs better, data quality can improve. Different roles can learn a lot from each other when brought together.

To achieve the intended goals, teams must know how each team benefits and be clear about how ongoing interaction can raise any issues, doubts, questions, and solutions wherever necessary. Similarly, there should be clarity about each participant's role and responsibility in the process, right from data collection, analysis, and reporting.

Persist the Discussion

After identifying issues and possible solutions, the team needs to implement the suggested improvement steps. Each team member should stay in touch with each other and provide regular updates to key stakeholders of the business. During the course, one might see significant process change such as reduced time in collecting data and reduced need for data cleaning workflows. Hence, constructive discussions about data quality can lead to improved and simplified processes for data value chain players across the organization to achieve further improvements.

Conclusion

Feedback loops substantially improve the data value team's expertise and knowledge. Whether or not the data is perfect, employing correct quality standards can improve insights and allow organizations to learn continuously. As a result, data value teams will have a more accurate image of real-time data in the long run. Thus, whenever a new project is proposed, the data quality team will have learned from previous experiences.