Journey to Data Quality

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More on Data Sharing. When CDQ was formed, data quality was a nuisance.

Now, data quality is a strategic topic — how can you achieve success in AI, how can you automate transactions or gain better visibility into your operations if the underlying data quality is poor? CDQ has built a deep track record in data quality management over the years, sharing data excellence with customers in four ways:. Do you want to learn about the people behind CDQ or maybe even join us? Meet the team! We believe: Sharing is the best way to better data.

The struggle for data quality is real for asset management leaders

We are so committed to sharing, we even made it our brand-new logo! The Schwarz Group's i.

The main topics this time were data lifecycle, architecture and applications. Data Sharing means better data quality with less manual effort Shared quality rules, shared data sources and shared peer-validated records are three steps that allow you to approach data quality problems together with others, more effectively and with far less manual effort - because a problem shared is a problem halved! Presenting our virtual employee DQR Meet our virtual employee DQR of our international AI team of data quality rules: Its main task is to identify invalid legal forms from customer and vendor organizations within all data sets.

Get in touch with us We look forward to your message! Leading enterprises rely on our data sharing services Many well-known, international companies and corporations already rely on our innovative data sharing and data management solutions to improve their business partner data quality. Read here what our customers say about us and become a part of our success story! This blog explores common data quality challenges, and how to approach them.

The struggle for data quality is real for asset and facilities management leaders

A facility can only reach optimal performance if its data management plan supports alignment and transparency. The team identify asset uptime as one measure that is closely aligned with this objective. The uptime information is captured through a historical business process, but initiatives have been attempted or completed in the past to ensure the data is available in the Enterprise Asset Management System EAM. Asset uptime information is captured by entering data into work order records when maintainers or operators report or address failures.

Uptime numbers are not consistent, and customers report the opposite of what is shown in the EAM. Maintenance personnel are verbally reporting higher numbers than EAM data and are frustrated at criticism resulting from the recorded uptime numbers. Uptime reports are being generated by multiple personnel in the organization, but it is not clear to everyone how the measures are calculated or where the data is coming from that is presented.

Data quality is everyone's business — Managing information quality — Part 2

A quick look at the EAM data shows that some work orders where uptime or downtime is reported, do not have the correct asset identified. Many of the work order fields are not populated even though the records are completed, and no description of the failures or how they were addressed is present on the records. In this case, data quality and confidence are low, even though this measure is identified to be a Key Performance Indicator KPI for the organization. The organization in the above example is struggling to meet its strategic objectives because of poor data management.

It is hampered by four common challenges:. So, how can asset and facilities managers tackle challenges like these? First, and most importantly, process data needs to be made easily accessible. Once the data are available in clear, concise views, they can better support decision making by offering a clear insight into performance issues and trends.

Datavision

Data management includes defining which data are critical to the mission and operations of your organization. It means identifying sources, consumers, permissible values, and analysis capability to support decision making. Managing the data includes rules or business processes for capture or entry, removing duplication, validating values or presence before committing to the system, and standardizing data types and values to simplify these tasks.

Organizations need to know their mission and objectives first.


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Only then can they define what data is needed when and by whom. Setting a mission objective defines why are you doing what you are doing. Once objectives are known, data requirements are defined, and expectations are set, the process of validation, continuous monitoring, and improvement can commence. This should be done by providing a single source of clear and concise performance to everyone. Good performance happens when trust and transparency are present.

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