Are you facing the “Spreadmart hell”? It’s common when several teams maintain the same spreadsheet for data sets. However, it may cause problems with your data accuracy. This may not be the only cause, but what is the reason for such inaccuracies? Is your new CRM or ERP system failing to deliver?
For any business, accuracy is critical when it comes to data management. According to research by O’Reilly, Most organizations have to deal with data quality issues due to multiple data sources. When incorrect data is entered into the system, it leads to many complications, including low productivity and legal matters.
It is essential to have a well-constructed data conversion process that ensures accuracy and prevents any such mishaps. In addition, a data conversion process helps in converting information into a more readable, understandable, and analytical format.
This guide will walk you through five data conversion strategies to improve accuracy and prevent business loss.
#1. Define the scope
Defining the scope of conversions is key to establishing endpoints and sources of information. It helps determine data sources, types of formats, and which information must be converted or migrated to the target system.
Here, you need to define the scope of conversion for mainly two streams of data,
- Master data accounts for all the essential information, and data conversions will depend on the phases of data implementations. For example, if you consider a payroll system, the data of employee demographics, pay scale, job hours, and others need to be defined.
- Initial data- The opening balance defines the data computed or captured to date. If we consider opening balances for a payroll system, it can help you with critical data on how much earnings, job hours, and other employee information is captured to date. This will also enable you to understand the data needed for payroll calculation year on year.
This leads to another strategy for data conversions, which considers the full-blown transformation.
#2. Lift, convert & Shift Strategy
It is a data conversion strategy where all the data from a legacy system is extracted, converted, and shifted onto the new system in a single go. Here, the complete swoop of data becomes an intensive exercise for the entire team. With such an intensive process, you need to have robust systems to support such conversions, and the risk of errors is also high.
However, with this strategy, you can reduce the effort of maintaining two systems simultaneously as the conversion is executed in a single phase. Therefore, it helps achieve the ROI quickly and leads to higher data recovery costs and errors. This leads to another critical data conversion strategy that you can use: “Sprinting.”
#3. Sprint strategy
When you compare the big bang approach to sprinting, the latter has a phased pattern of converting data into smaller sprints. Every data conversion phase has pre-defined endpoints that help articulate the exact data that is to be converted.
Due to the conversion process being broken into smaller sprints, the risk of data loss is reduced. Further, it also allows you to improve data management and conversion accuracy. The sprinting approach also ensures that the outcome of the entire process stays close to the desired goal.
However, there are two significant drawbacks: the time; you will need more time due to a phased approach. Second is the cost; execution of different phases takes more resources and increases the expenses. One way to ensure that you use the sprinting method and keep the data conversion costs to a minimum is through data mapping.
#4. Data mapping
Data mapping strategy involves tracking the trail of data from legacy systems to the target system. It includes tracking information like data fields, field type, length, and description. Here you need data conversion services to help you with data mapping documents.
With such a document, you will have critical data on errors during the conversion process and understand the current system’s shortcomings. Once you have the data, the next important step is to adjust the data governance policies.
#5. Data governance
Data governance strategy helps build controls for access to critical applications and avoids problems of duplications. Managing new data according to the target system and maintaining accuracy needs excellent data management services. Using this strategy with such services, you can ensure that the data is accurate and according to the new system’s requirements.
Converting your data according to the new environment, platform or system needs reliable strategies. These systems can be heterogeneous, and both sides’ data formats will be different, leading to complications. A well-planned data conversion process can help you mitigate such complex issues and maintain accuracy.