Enterprise data quality problems can be categorized into three high-level groups of contributing processes:
- Processes that bring data into a database, manually or otherwise, which may either cause problems due to existing, incorrect incoming data or by errors within the extraction and loading processes.
- Processes that manipulate data already in the database, which can be routine or brought about by upgrades, updates and a range of ad-hoc activities.
- Processes that cause data to become inaccurate or degrade over time without any physical changes having been made. This usually happens when real life objects described by the data change while data collection processes remain the same.
All these processes are an important part of data processing and, therefore, cannot be cut off in an effort to avoid problems. The only way to maintain the integrity of data despite the given complexity is to make certain that all these processes work as intended without causing any problems. Below is a breakdown of the seven major causes of data quality problems within those three groups.
- Initial Data Conversion
Most of the time, databases begin with the conversion of data from another pre-existing source. Data conversion never goes as seamlessly as intended. Some parts of the original datasets fail to convert to the new database, while other datasets just mutate during the process. The source itself could also not be all that perfect to begin with.
More time must be spent on profiling the data, as compared to time spent on code transformation algorithms. The quality of data after a conversion largely depends on this factor.
- System Consolidation When combining old systems with new or phasing out systems, data consolidation is crucial. Problems may arise, especially when unplanned – resulting in hastened system consolidations.
- Batch Feeds
Batch feeds are large data exchange interfaces that happen between systems on a regular basis. Databases communicate through complex webs of batch feeds. Each of these feeds carries large volumes of data and therefore when one comes with a problem, bottlenecks occur that are only made worse by consequent feeds.
A tool that can detect process errors like this one, and stop them from causing performance problems will play a big part in upholding a database’s quality.
- Real-time Interfaces Real-time interfaces are used to exchange data between systems. With one combined database, necessary procedures are triggered that send data to the rest of the databases downstream. This fast propagation of data is a perfect recipe for disaster, especially if there is nothing at the other end to react to potential problems. The ability to respond to such problems as soon as they arise is key in stopping such situations from spreading errors and causing more harm.
- Data Processing Data processing comes in many forms, from normal transactions done by users to periodical calculations and adjustments. Ideally, all of these processes should run like clockwork, but underlying data changes and programs change, evolve and sometimes are corrupted.
- Data Scrubbing Data scrubbing is aimed at improving data quality. Cleansing of data was done manually in the early days, which was relatively safe. Today, with the added complexity of Big Data, new automated ways to cleanse data have arisen that work to make corrections by the volume.
- Data Purging With Data Purging, old data gets purged from a system to make room for new data. It is a normal process when certain retention limits are met and old data is no longer required. The problem that this process may cause in terms of data quality in terms of those instances where relevant data is accidentally purged. Errors in the database may cause that or the purging program may just fail. The application of infrastructure performance monitoring solutions like Xangati will ensure that such errors don’t’ disrupt business operations.
Xangati offers the breadth of knowledge and robust technology to correlate cross-silo intelligence, so it will provide deeper insights for organizations adopting hybrid-cloud infrastructures that need to control data quality, plan efficiently and optimize their infrastructure. To learn more about Xangati, visit our website.