Data Quality in 5 Steps

As the new customer master data governance leader, I had my work cut out for me. My company, like many, was composed of separate entities whose order entry systems, processes and definitions were created independently or, over time, became unique. It was my job to make sense of this. I focused on imagining what our data could do once it was complete and trusted. It was all about enabling growth. 

This story begins when the CEO attempted to obtain better terms and conditions from a difficult customer. Making what he thought would be a reasonable and simple request, he asked his leadership team for the total dollar value of the last quarterly sales to this customer. The answer would take weeks to deliver.

Customer data is a complex, moving target. My colleagues cautioned me that my job could not be done. Each business had its own way of doing things and a strong desire to stay that way. Multiple versions of SAP both between businesses and within businesses further complicated by old legacy systems and a complex distribution of customer creation authority across the global landscape seemed impossible to tame. Little did they know I had Lean Six Sigma tools in my back pocket as I rolled up my sleeves and got to work.

Using a Six Sigma DMAIC (define, measure, analyze, improve, control) approach and a focus on Six Sigma Lean tools, the plan began to take shape. As DMAIC specifies, before you do anything, define the problem. Begin with a voice of the customer. Understand what the customer needs. Remember that it's not about good data, but what the business can do with trusted data from across the corporation. It is then possible to create the plan and act upon it. This will help to define your problem statement, the burning platform you can use to convince key stakeholder of the problem's urgency. 

At my company, the targeted audience for the voice of the customer was sales and marketing. Interviewing this group revealed benefits to be gained from a structure that transcended business unit boundaries. Knowledge of customer interactions would enable new opportunity identification, improve win rates from leveraging the relationship and improve customer retention. Sales and marketing could prepare for customer visits with the latest information, avoiding surprises at a customer site. Business leaders could identify the top customers at a glance by business, by region, by standard industry code and at an enterprise level. This would also help with mergers and acquisitions to reveal if a potential acquisition might be a competitor to an internal business. Reports on activity and dollars could be pulled from across the enterprise in one simple transaction. Discovery by chance could be changed to research by design. All this depended on trusted data. 

I needed to develop a process to deliver continued improvement. The customer data began to come into our central repository, which is named Customer Data on Demand or CD2. Dun & Bradstreet assisted in data cleansing and enrichment with family alignment and SICs. D&B matches each customer record to a DUNS number from their database and assigns a confidence code. A tolerance level was set at a confidence code of eight out of 10. Lower confidence codes were put into the low confidence bucket, and zero confidence records were categorized as a nonmatch. Low confidence and nonmatched records needed to be sent back to the business group to determine if a DUNS number match could be found with improved data. Three important metrics were identified: 

1. Quality, determined by match rate.

2. Completeness, determined by population of core fields.

3. On-time-to-request (OTTR), measured by file receipt into the central repository by due date. 

Next, I needed to establish scope and criteria. After recruiting team members from across the enterprise, we had our first meeting in Arizona in the dead of winter. In addition, numerous entities were represented in the Phoenix area, facilitating their participation. We spent three days developing our charter and scope and defining our mission, scope, objectives and associated measures. Our immediate focus would be as follows: 

1. Define core data and the associated rule. 

2. Find a tool to assist in data cleansing for those groups involved in legacy loading into SAP. 

3. Improve our match rate from 64 percent to 85 percent to support our value proposition. 

Trusted data was key to our value proposition, which was stated as data integrity, customer alignment and increased productivity through common process, as well as reliability and validity of reporting and analytics. We defined requirements for describing trusted data: 

1. Accessible: Data is independent of the originating source.

2. Available: Data is there when needed and of a defined and trusted quality.

3. Consistent: Definitions have been agreed to by all. 

4. Auditable: The origin is traceable.

5. Secure: Data is a valued corporate resource. 

Data ownership was a big issue. The naysayers continued to cry that our conglomerate culture prevented a coordinated effort to drive data quality. I realized that the main risk was human and identified the "what's in it for me" (WIIFM) syndrome. I offered the businesses end-to-end process control over their data. They had the purchase orders and knew the customer, sales and customer service teams, enabling them to control data quality and the subsequent match to the DUNS number. I started with a business that I believed would support the effort for a quick hit and a positive example, and I worked toward areas where I expected increasing resistance. A key enabler was my ability to provide the project management support, tools and training to enable the effort. 

I analyzed the data and presented each team with a Pareto view of transaction dollar values for their unmatched data. Within each feeder system, the subgroups were the top 100 low confidence and top 100 nonmatch records by transaction dollar value. I also checked the remaining customers for outliers to ensure that a major customer record was not missed due to the low dollar value. The vast majority of the time, top customers were also the top 100.

It was essential to label needed items in order to maintain transaction traceability. The file code from the original sending system and the source system customer number were tied to the record, providing an audit trail.