Data Quality Strategy

Data quality is extremely important today but no one does anything about it. Many

organizations need to have data quality improvements at the project level. However, there is a high possibility that your organization has failed to solve data quality with an appropriate approach that has a solid opportunity to improve both the quality efficiently and effectively. There are three areas to improve data management – architecture, governance and quality. However, the organization needs to have ambition, will and resources to succeed at all three. These three pillars are typical of achieving a flexible, well-organized, governed data layer that can give out trusted, timely and accurate data to report and operational decisions.

The success of the data implies many aspects. For example,

Data should be assessed to be an important infrastructure asset;

The organization should understand how data should support the business strategy;

Staff should accept that they can create and manage the data they own;

Managers should agree that data is super important.

All these aspects can imply that data plays an important part in the organization.

There are different metrics for how to assess the data assets. The typical formula is:

Imagination + Thinking + Planning + Wisdom. The answers to this formula can depend on different industries. “What does good look like?” can differ a lot. For business strategy, it can be a wonderful customer experience in navigating product offerings easily and logically. For data managers, it can reduce the time to manage from four months to two weeks. For the product team, it can launch a new global product line in Europe. The understanding of the answers can vary a lot. Another question “What data do we need to achieve?” also require high-level quality objectives. Thus, a data quality strategy is extremely important.

Data management requires governance management, metadata management, data quality assessment, data profiling, data cleansing, data standards, and data integration. Many important data management activities are underlined through data quality strategy. Data quality strategy provides consensus guidance for what the data management should do.

We can improve data quality in two phases. One is to analyze data quality. Look for problems in the current data process and find what would be better if the potential problems are fixed. Determine the participants who will lead the data analysis of the data pipeline. Identify interviewees for the dataset, so that the interview can conduct in a detailed way. Create a questionnaire based on the key issues of the data system. Conduct interviews, analyze results, verify problem description, very goals, gaps, and conclusions. Finally, we need to summarize capabilities and recommend the data quality analysis. Another phase is the data quality strategy. This involves vision and principles for data quality, goals, objectives, and business benefits. Data quality strategy also needs to involve a sequence plan. It needs to prioritize the capability building and focus of the data sets, quality objectives for major data stores and formal organizational structures. After these two phases, we can easily identify which improvements we can change.

To make sure the accuracy, relevancy, completeness, timeliness and consistency of the data, data quality strategy is extremely important.