How can you standardize data when integrating multiple systems?

There are many benefits for data standardization. It not only fosters data interoperability and collaboration but also: 1. Promotes adaptability to changing technological landscapes - Standardized data should be in-sync with the latest advancements in data processing and analysis. This ensures that organizations stay up-to-date and can leverage new technologies and methodologies without having to completely overhaul its data integration processes. 2. Facilitates compliance with data privacy and security regulations - Standardized data structures and formats allow for more consistent and robust data security measures, such as encryption and access controls. It helps in safeguarding sensitive information consistently across various systems.

Let's keep this real. Most organizations have multiple applications. Multiple systems. Some even completely different busineses and business models. Too many organizations approach Data Standardization with the lofty goal of creating the ONE ENTERPRISE standard to rule them all. They also tend to take a boil the ocean approach with respect to the data elements they choose to start with. The best way to accelerate maturing of data standards in your organizations is to pick one as quickly as possible, and start using it. The process of maintaining data against that standard will influence how that standard "evolves" and matures. With the right data governance proceses in place, you can keep adding to your "standards". #DataFirst

You want users to understand your data. Standardization is one way to accomplish that. You ensure that data ingested meets certain defined standards. These standards are the documented in formal data dictionaries. This can be administratively intense but practically useful.

Standardization for data elements is very important and is one of the focus areas for the Data Governance practitioners. It helps for efficient data analysis, reduces the overhead, maintains consistency across systems, makes efficient integration & data sharing between systems. It also reduces the possibility of duplicates.

Data standardisation has many benefits, but one of the most important is that it helps businesses avoid making decisions based on inaccurate or incomplete data. Data Standardisation ensures that companies have a complete picture of their data, and it makes sure that the data is easier to process, analyse and store. Having a standardisation across data makes Data Governance easier to implement especially around Data Quality. Anything that makes the Data Management team's job easier, is a win. Less human resources focused on a problem, and more automation, means the team can focus on other things and help the company with their wider business strategy.

Simple trimming on values can remove unnecessary preceding and/or trailing blank spaces. Casting or any other data type converter can help change the data types and formats (representation of dates and timestamps, as an example) of values. Concatenation can also help achieve standardization. There are many tools available to standardize the data.

When it comes to unifying data from diverse systems, the methods you choose are pivotal. Here's a rundown of key techniques: - Data Cleansing: Like a digital detox, this process scrubs away data impurities, ensuring your data's integrity. - Data Transformation: Think of it as data gymnastics, flexibly converting data to the required formats with the agility of ETL tools. - Data Validation: The ultimate quality checkpoint, ensuring that the data not only looks good but is good, fit for your business purposes. Remember, these aren't just tasks to check off; they're part of a strategic approach to making data work harmoniously for your business success.

There are hundreds of tools and approaches to data standardisation, like the article says, you can use ETL tools or scripts, or purpose-built applications, but one thing that is important to consider, no matter the method you use, is the consistency of the data when it comes to formatting. Knowing the following before you settle on any method is crucial: - Requirement gathering. I know it sounds simple, but you need to know what you are looking at before you start - gather the requirements before you choose the tool or method. Have a plan. - Assess how you are obtaining data - no point picking a method if it doesn't support your data sources - Define data standards - you really need something to enforce, and your method needs to support it.