All industries rely on data to make critical decisions, but data is only as good as its quality and relevance. Data quality is an internal company decision regarding company-wide data requirements, the person or department in charge of setting the requirements and designating the level of compliance necessary to meet the desired requirements. Data quality management is the process of oversight and accountability set in place to ensure that the desired level of quality is met and how to correct imbalances.
There are four pillars of data quality management:
1. Data Profiling
This is the starting point for data quality management. It entails looking at the quality of existing data, evaluating it and identifying problems in order to devise the standards of conformity set for data quality in the future. Problems might include data accuracy, duplication, relevance and level of completeness.
2. Data Quality
This part of the process consists of taking the information gleaned in the data profiling process and devising solutions. An example of this would be identifying a problem associated with duplication of data and data redundancy, then developing measures to counteract that problem in the future.
3. Data Integration
Data integration involves taking multiple files that contain the same information and finding ways to integrate those files so that they’re contained in one single, viewable database, while still maintaining their original placement. An example of this might be sales information about a single client that exists in both a regional sales and vendor files.
4. Data Augmentation
This step involves adding information to the existing data in order to further classify it or provide clarity. An example of this might be supplying additional product information or providing more detailed customer address and identification data.
Once you have data quality measures in place, it’s good to have a secure, accessible database storage solution. Data management services provide a high-quality and secure means of housing your data without the necessity for investing in the time and space requirements of constructing a data centre in-house. The advantages to your business are:
Flexible locations in temperature-controlled facilities
The latest available technology without the necessity for attending to upgrades yourself
The expense of maintenance and upkeep responsibility lies with the service provider, including protection against power outages and data loss
This frees up data mangers to concentrate in the quality of the information instead of on data storage and access feasibility problems.