The challenges of messy data – one error, millions in costs
The vast amount of data surrounding the business world is both a blessing and a potential curse. Messy data is more than just a jumble of information; it can directly impact business outcomes. Errors in master data can lead to business disasters, and wrong decisions can cost the organization millions.
Messy data requires continuous attention and correction, and manual fixes, in turn, consume time and resources that could be more productively utilized. The traditional way to respond to messy data is to create a ticket for the IT department and hope they can resolve the situation. However, this often leads to time-consuming manual corrections that are prone to human error. Incomplete or erroneous data can cause confusion and communication barriers, acting as a toxin to organizational efficiency. For example, incorrect product information can lead to incorrect pricing, directly affecting business profitability. Duplicate customer records complicate customer relationship management, and poor-quality data can become costly in the form of manual fixes and communication breakdowns.
MDM prioritizes high-quality data, ensuring that decision-making is always based on reliable and up-to-date information. It establishes a clear foundation for cleaning up messy data by creating a single “truth” of the organization’s key information, such as customer, product, and supplier data. High-quality data supports decision-making, improves customer satisfaction, and increases business efficiency.
Who messed up the master data?
Do you have someone working with you who creates erroneous data in the systems, which others then have to clean up, employing everyone? Poor data creates a long chain of corrections to clean it up. The following example illustrates how investigating a single human error can take surprisingly long and incur greater costs than anticipated.
Lanttu’s colleague Kaali creates a new product record in the system and accidentally enters the product’s product information incorrectly. Nauris, who is responsible for assortment management and product pricing, tries to link the new product to their own category but struggles to create the link between the product and the category class. Nauris grapples with the challenge for 15 minutes before seeking help from their colleague, with whom they spend the next 15 minutes unsuccessfully trying to solve the problem. Nauris sends a message to Lanttu about the issue and asks them to look into the challenge. Lanttu notices the error in the product information, and asks Kaali to correct the product details. After that, Nauris can proceed with updating the assortment management for the product. In total, it took approximately 75 minutes for all involved parties to correct this single error.
Messy data and overcoming it: MDM with low-code tools
The complex data streams of companies can be either an asset or a burden depending on how data is managed. Messy data poses a challenge, and low-code solutions, combined with master data management, open a new chapter in data management efficiency. Cleaning up messy data is no longer an impossible task, as low-code tools provide organizations with the opportunity to manage and organize master data skillfully and accurately.
Lanttu.io has developed a low-code tool on the Power Platform for master data management. MDM helps design efficient processes for data maintenance, and low-code tools enable streamlining and automating these processes. MDM establishes a solid foundation for long-term information management within the organization. Low-code tools provide a fast and efficient way to tackle messy data issues by automating data cleansing processes and enabling the creation of applications that identify and rectify errors automatically. With these tools, it becomes easier to analyze complex data sources, identify gaps and errors, and address redundancies.
Master data cleansing – from messy to clarity
Cleaning up messy data is a challenging but essential task. Master data management provides organizations with the tools and approach to guide them towards clearer data streams. This is not just an investment in data management but a strategic decision towards better and more efficient business operations. MDM optimizes information management processes by reducing the risk of errors and ensuring a unified data source for the organization. It places high-quality data at the center, including mechanisms for data quality monitoring, correction, and maintenance, ensuring that your organization benefits from accurate and up-to-date information.
By streamlining master data with best practices, you can ensure that your organization benefits from accurate and reliable information. The task of cleaning up messy data through MDM involves several steps:
- The first step is to identify and map out messy data, including identifying deficiencies, errors, and duplications from various data sources.
- MDM helps define key areas of master data, such as customers, products, and suppliers, providing a clear view of what information needs to be managed and maintained.
- MDM helps in designing efficient processes for data maintenance, including defining responsibilities, streamlining update processes, and monitoring data quality.
- It’s essential to train staff and ensure internal communication about the process of cleaning up messy data. MDM provides the tools and structure to increase awareness. Staff training and effective communication are crucial in the process of cleaning up messy data.
Conclusion
The vast amount of data in the business world serves as both a resource and a potential challenge. Incorrect data can lead to erroneous decisions, significantly affecting business outcomes. Messy data, which creates a jumble of information, has become a central business problem, impairing decision-making and business efficiency. Dealing with messy data requires constant attention, and traditional manual correction methods have proven to be time-consuming and prone to human errors.
Transitioning from chaos to clarity ensures that the organization’s information flows smoothly and efficiently. Master data management with low-code tools offers an effective and automated approach to managing messy data, thereby improving decision-making and long-term business efficiency. The task of cleaning up messy data involves several steps, including defining master data, designing efficient processes for data maintenance, staff training, and ensuring internal communication.
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