Data quality is essential to leveraging more value from your business’ data assets. Data analysts, managers and board members alike, must know and understand the quality of the data they are using to make decisions and to set direction for their organisations if they are to make the best strategic decisions.
Today, data is created and collected by every type of device used in businesses and homes. Data analytics has become a common term. Expectations for quality data continue to grow at an increasing rate. However, implementing a data quality strategy is not as simple as installing a tool or a one-time fix. Organisations across the enterprise need to work together to identify, assess, remediate, and monitor data with the goal of continual data improvements.
Taken from the report ‘Ten mistakes to avoid when implementing a data quality strategy,’ commissioned by TDWI (Transforming Data With Intelligence), we’ve summarised the most common mistakes which threaten your data strategy if you fail to acknowledge and address them effectively:
1: Assuming your enterprise data is clean and accurate
2: Assuming your enterprise data has only one business definition
3: Skipping the assessment phase
The best approach is to start with completing an assessment of your organisation’s applications and data. Business people, subject matter experts and data governance teams work together to first identify and rank the critical business domains, along with data elements deemed critical to each domain. The critical data elements of each business domain are profiled and analysed to determine their quality. Metrics are developed to provide a high-level view of the data quality for each business domain and associated critical data elements.
Profiling and evaluating data is a first step for the business and data governance teams to better understand what their data actually looks like, how it compares to other data values, and how to determine the quality of data.
6: Not including templates and standard processes as part of the data quality strategy
7: Not following the data quality road map
8. Building the data quality strategy in one large project
9: Viewing technology as the entire solution
10: Not continually monitoring and evaluating data
Author: Patty Haines
Original Source: TDWI (Transforming Data With Intelligence)