Data is the lifeblood of any organization. It informs everything from business strategy to day-to-day operations. But, like water, data can become contaminated and unfit for use. As organizations increasingly rely on big data and intelligent automation to become data-driven enterprises, clean data will be critical to success.
Just as dirty water can adversely impact a person’s health, dirty data can affect the health of a company. For example, making strategic or operational decisions based on poor-quality data can impact competitive positioning and require financial restatements.
Water must go through comprehensive quality checks before being consumed. Similarly, data must be cleaned and processed before it can be trusted for decision-making. In today’s data-driven world, if we can’t trust our data, we can’t trust our decisions.
The three pillars of clean data
There are three pillars of clean data: data governance, data quality, and master data management. All three functions need to work collectively to improve the value of the data. Let’s take a look at each one.
- Data governance ensures usability by managing and keeping data in the right format. This involves setting policies, procedures, and controls for data management, monitoring data usage and enforcing compliance.
- Data quality: Data quality encompasses initiatives to ensure your data is accurate, complete, consistent and timely. If data doesn’t meet these standards, it's unreliable, and decisions based on it can be faulty. When assessing your data quality, we recommend considering the following:
- Completeness – Is all the data available?
- Consistency – Can we match data across datasets?
- Uniqueness – Is there a single definition of the data?
- Validity – Does the data match the rules?
- Accuracy – Is the data correct?
- Timeliness – Is the data available when needed?
If the answer is “no” to any of these questions, it’s essential to invest in the data quality infrastructure and resources needed to get clean data. Failing to make these investments will lead to the creation of shadow systems run by business users or the propagation of poor-quality data with unfavorable outcomes.
- Master data management ensures that data conforms to the pillars of uniqueness and non-duplication. It involves identifying unique data elements, eliminating duplicates and ensuring consistency across different systems and domains.
The challenge most companies face when trying to deliver clean data is focusing on only one or some of these efforts without joining them together and without the bigger picture in mind. Aligning these three functions organizationally and ensuring they are fully integrated underpins the ability to provide clean data.
The value of clean data in manufacturing
As manufacturing environments become more complex, forward-looking manufacturers see extraordinary benefits in using data to meet new market realities, balance competing priorities and advance digital transformation. They benefit from predictability in their supply chain to capitalize on the cost savings and minimize the environmental impact. And to build predictability requires clean data.
The journey to clean data isn’t without challenges. If you’re unsure how to begin or are not seeing expected results, consider stepping back and looking at the big picture. Carefully assess your data governance, data quality and master data management initiatives, recognizing that they all need to be tightly integrated.
CGI's approach to data-driven manufacturing combines strategy, governance and the human aspect of change to implement data solutions that help you realize your strategic vision. Visit our Data-Driven Manufacturing section to learn more.