With ever-growing data volumes it becomes a challenge to manage your data. This includes making sure that the data is fit-for-purpose and that it is of the right quality. This is, however, what your business depends on to provide the best customer experience,, to avoid risk and regulatory compliance issues and to best support your day-to-day operations. Data Quality Management will help you identify data quality issues and correct them.
Data quality refers to the state of information. It can be measured according to 6 characteristics but on a higher level the definition of data quality is how well it is fit for the intended use. The better the data is fit for decision making, planning or operations, the higher the quality is considered. With the large number of data sources containing data nowadays, the consistency of data accross shared data assets is increasingly important. Solving data quality issues requires both good data governance and Data Quality Management.
Accuracy: how well does data reflect reality?
Completeness: does the data fulfill your expectations of completeness?
Consistency: does data stored in different places match?
Timeliness: is data available when you need it?
Validity: is data in the right format and usable?
Uniqueness: is the data you use the only instance?
Data Quality: start with good intent, manage with technology!
DATA QUALITY MANAGEMENT
Data Quality Management is about processes and technology that help deliver data to the organization in a way that meets the business requirements. Your business decisions are based on your data. The higher the quality of your data the better you can serve your customers, collaborate with your business partners and grow your business in a highly competitive market.
Implementing Data Quality in your organization requires a structured approach and starts with clear objectives, a DQ Framework with data quality policies and rules, and a proven approach (Plan, Do, Check and Act cycle).
Business benefits of Data Quality Management
Trustworthy and accurate insights for data analytics and reporting.
Ability to implement across the your organisation throughout your application landscape (design once, deploy anywhere).
Leading to better business decisions, higher customer satisfaction and improved compliancy.
Saving valuable time as less effort needs to be put into data cleansing and data preparation.
Ability to introduce machine learning and artificial intelligence as high data quality is a pre-requisite.