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How do you manage data conversion scope, timeline, and budget effectively?

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Data conversion is the process of transforming data from one format or system to another, often as part of a migration, integration, or consolidation project. Data quality and validation are crucial aspects of data conversion, as they ensure that the data is accurate, consistent, and usable in the target environment. In this article, we will discuss how to measure data quality and validation in the context of data conversion, and how to manage the scope, timeline, and budget of your data conversion project effectively.

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Rathna V.

Enterprise Data Strategy, Governance & Architecture, Risk and Quality Management| Data and Analytics | MDM | AI/ML |…

1 Data quality dimensions

Data quality is a multidimensional concept that can be assessed according to various criteria, such as accuracy, completeness, timeliness, consistency, relevance, and integrity. Depending on the nature and purpose of your data conversion project, you may need to prioritize some dimensions over others, or use a combination of metrics and methods to evaluate them. For example, you may use data profiling, data cleansing, data enrichment, and data auditing tools to analyze and improve the quality of your data before, during, and after the conversion process.

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Rathna V.

Enterprise Data Strategy, Governance & Architecture, Risk and Quality Management| Data and Analytics | MDM | AI/ML | ERP | CRM
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I agree, In my experience data profiling is the first step in the process to understand the current state of the data. This includes understanding the data population, and mandatory and optional attributes. It provides insights into patterns, and distinct values populated. This also helps with the data validation rules, and transformation rules needed to convert into the new system.

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2 Data validation techniques

Data validation is the process of verifying that the data meets the specifications and requirements of the target system or format, and that it has been converted correctly and completely. Data validation can be performed at different stages and levels of the data conversion process, such as source, mapping, transformation, loading, and post-conversion. Some common data validation techniques include data sampling, data comparison, data reconciliation, data testing, and data monitoring. These techniques can help you identify and resolve any errors, discrepancies, or gaps in your data conversion process.

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Rathna V.

Enterprise Data Strategy, Governance & Architecture, Risk and Quality Management| Data and Analytics | MDM | AI/ML | ERP | CRM
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One thing to add to the list is "Data Usage Testing", some times technically it matches however in the usage context it does not fit.

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3 Data conversion scope

Data conversion scope defines the extent and boundaries of your data conversion project, such as what data sources, entities, attributes, and relationships are included or excluded, how much data needs to be converted, and what are the quality and validation criteria and standards. Data conversion scope can affect the complexity, duration, and cost of your data conversion project, so it is important to define it clearly and realistically, and to manage any changes or deviations from it carefully. You can use tools such as data inventory, data mapping, data modeling, and data governance to help you plan and document your data conversion scope.

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Rathna V.

Enterprise Data Strategy, Governance & Architecture, Risk and Quality Management| Data and Analytics | MDM | AI/ML | ERP | CRM
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This is a very important aspect of overall conversion, if you cannot scope, it can quickly add up resources and time. Scoping will help the team focus on the right things that matter.

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4 Data conversion timeline

Data conversion timeline refers to the schedule and milestones of your data conversion project, such as when each phase or task of the data conversion process needs to be completed, what are the dependencies and risks involved, and how to track and report the progress and status of your data conversion project. Data conversion timeline can affect the efficiency, quality, and success of your data conversion project, so it is important to estimate it accurately and realistically, and to manage any delays or issues proactively. You can use tools such as project management, data migration, data integration, and data quality software to help you execute and monitor your data conversion timeline.

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Rathna V.

Enterprise Data Strategy, Governance & Architecture, Risk and Quality Management| Data and Analytics | MDM | AI/ML | ERP | CRM
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I agree, there needs to be proper scheduling by evaluating the scope, process, people, and technology. One needs to add proper time for data cleansing as well. This is the long pole in the conversion process.

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5 Data conversion budget

Data conversion budget refers to the resources and costs of your data conversion project, such as what tools, technologies, and personnel are needed or available, how much time and money are allocated or spent, and what are the benefits and returns of your data conversion project. Data conversion budget can affect the feasibility, quality, and value of your data conversion project, so it is important to calculate it accurately and realistically, and to manage any overruns or savings effectively. You can use tools such as cost-benefit analysis, return on investment analysis, and data value assessment to help you justify and optimize your data conversion budget.

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How do you manage data conversion scope, timeline, and budget effectively?

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1 Data quality dimensions

2 Data validation techniques

3 Data conversion scope

4 Data conversion timeline

5 Data conversion budget

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