Reasons to Centralize Your Data and How to Do It

Having a central repository of all your company’s data is critical for ensuring cross-functional access and elevating the data’s value. After all, data is your enterprise’s most valuable asset. Therefore, it is important to use master data management because it ensures the quality of your data, collected in one place to boost analytics, enhance data exchange and consolidation, and make compliance simpler.

Creating a single source of truth

Having a single source of truth can give you a competitive advantage by eliminating confusion and duplication. By centralizing your data, you can create a single source of truth for your business. A data warehouse gives your team the ability to view and access your master data from any location, thereby improving your business processes. It also provides transparency for your core team, stakeholders, and key decision-makers. This transparency is critical for corporate social responsibility (CSR) and team loyalty.

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Creating a place of trust

Centralizing your data is a strategic decision for an enterprise, as it enables cross-functional access and elevates its value. Data is the most important asset of an enterprise, and it should be treated as such. There are two important reasons to do so: to build trust in the data and to enable cross-functional access to it.

Building trust requires removing silos and elevating data’s importance. This process includes making decisions about governance and data tools, team structure, and responsibilities. It also requires socializing data to build trust.

Importing data from multiple sources

Importing data from multiple sources can be a very useful and convenient way to consolidate your data. It is also known as ETL. This process involves three steps: extracting data from the original source, transforming and combining it, and finally loading it into a final database.

Creating a data warehouse

Data warehouse design is a multi-faceted process. It involves combining data from many sources, removing redundant data, and ensuring data are organized in a consistent format. This is crucial for querying, which requires requesting specific tables from the data warehouse. If data is not organized properly, it can cause errors in the result of a query.

A data warehouse is a powerful tool that allows businesses to quickly and easily obtain data. It allows them to analyze data in a matter of seconds. For example, a data warehouse can be used to calculate the overall value of leads in a CRM system such as Salesforce. They can push Salesforce data into the data warehouse, create a schema for the data and run a query to find the highest-value prospects.

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Reducing redundancy

Data redundancy is a common problem in many businesses. It is when the same data is stored in multiple locations. Organizations increasingly move away from siloed data to a central repository. However, this can create inconsistencies and duplicate data. Reconciling this data can be a time-consuming process, and tracking the source of redundant data is essential.

Data redundancy can be accidental or intentional. Accidental data redundancy is caused by complex processes or inefficient coding, while intentional redundancy is done to ensure consistency and protect the data. Data redundancy can also cause data corruption, in which the same data fields appear repeatedly in different locations. This makes the data unreliable. In addition, data corruption can happen as a result of errors in the data storage system or database. In this case, employees will encounter an error message when they try to open a file containing the corrupted data.

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