Building a foundational data quality strategy


Data Quality offering enables advanced data operations with speed and confidence

With data exploding at unprecedented speed in today’s highly digitized and IoT-powered environment, organizations of all types continue to invest in advanced technologies to modernize operations. But poor management and maintenance of that data can render it meaningless, costing businesses more in the longrun and leaving opportunities on the table – including the ability to drive value and enable intelligent action. A good data quality strategy would not just clean the data but also prepare it for what’s next.

One global manufacturer recognized potential quality issues in procurement data, but was unsure where to prioritize improvements or how to deploy an enterprise-wide data management program. Aligned Automation worked with the company on a step-by-step approach, framed by an agile development methodology, to work across teams and determine a technology framework, business rules, tracking and KPIs. From data discovery to data quality scorecards, the team enabled a measurable, scalable program benefitting the entire organization. With technology-agnostic capabilities and functional experts, Aligned Automation tailored the solution to actual desired business outcomes.



De-duplication opportunties in raw materials. 


De-duplication opportunities in vendor master. 


Reduction in operations costs by freeing up massive cloud storage previously occupied by duplicate and otherwise unnecessary data.

The need for change -
Expansive data created quality control challenges

The business had data from a variety of digital sources across their procurement organization, but lacked a sustainable operating model for maintaining fact-based and measurable data quality. Teams struggled to optimize operations and cut costs with large amounts of data – some duplicate – taking up valuable cloud storage space. Without established data quality, it was difficult to drive forward on further data improvement opportunities and achieve the differentiation that comes from harnessing the power data and analytics.

To tackle this increasingly common challenge, the team identified the following project objectives:

  • Enable scalable co-development across teams for focused data quality governance

  • Establish data quality technology framework

  • Develop a business rules engine for data quality

  • Create tracking mechanism for KPIs, opportunities, and end to end support profiling

Rapid project mobilization

The manufacturer and Aligned Automation data quality experts accelerated project organization and execution through collaboration with IT and business groups to set goals, prioritize focus areas, review milestones and incorporate feedback. Solution deployment through an agile methodology enabled rapid project mobilization and delivery of targeted outcomes across business groups.

The team focused on 6 main areas for their data management solution:   

The value of quality data

The solution enabled the manufacturer to see a historic snapshot of their data and data quality. From there, the procurement team could analyze gaps and understand changes in quality over time, providing an important benchmark and direction for future improvements. The focus of this first round was on de-duplication potential, eliminating redundant data to store only unique first instances.

Analysis demonstrated a potential 25% de-duplication opportunity for raw materials and more than 6% potential de-duplication opportunity in the vendor master. Deduping all these areas has the potential to free up massive amounts of bandwidth and cloud storage, reducing overall operational costs. 

Critically, this analysis is not limited to the procurement organization. The data management framework used by Aligned Automation is scalable to other organizational functions and data-intensive groups. Expanded use cases include everything from de-duping sales data and contacts to streamlining manufacturing.Maintaining quality data can be the difference between compliance and high fines, missed sales opportunities, or lost revenue from decisions based on incorrect information. By investing in data hygiene, manufacturers pave the way for better decision making, increased productivity, and long-term success.

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