There is a growing trend by businesses to sponsor IT-led data quality and governance initiatives as companies extend their end-to-end business processes and expand their business intelligence and analytic initiatives. Accompanying this trend has been a sharp increase in information technology (IT) methodology and technology-centric applications, solutions and initiatives to address data quality and management concepts.

Many of these efforts have met with mixed results. Technical methodologies, applications and solutions are limited because IT does not own the data. Rather, IT’s role should be advising and assisting the business to effectively manage data quality rather than insulating business processes and applications from poor quality data. IT does not have the ability to correct data or prevent data quality issues at their source.

This two-part series focuses on creating business leadership awareness around their ability to correct and stop externally accepted and internally generated data quality issues at their source. The following examples illustrate the types of data quality issues that frequently arise within the business and describe how leadership teams can leverage existing business techniques and organizations to resolve these issues.

Executive Sponsorship

The first and most fundamental step in addressing data quality issues is to educate executive leadership into the cause-and-effect relationship between poor data quality and ongoing business challenges, risks and liabilities. Resolving data quality issues will require the effort of the executive team to change how the business behaves and how it executes its day-to-day business processes.

Creating executive leadership awareness around data quality takes time. In many organizations, it may take more than a year of education and awareness before executive leadership invests into data quality initiatives. During this time, IT needs to closely partner with their business counterparts to create the awareness by demonstrating an intimate knowledge of the organizational roles and business processes that drive specific data quality issues.

In other situations, the realization of how data quality affects a business may occur very suddenly. For one company, the impact of data quality on their business operations was not recognized until it was responsible for causing a bomb scare because they incorrectly packaged one of their products. The quality assurance team determined the root cause of this issue was related to multiple data quality issues introduced within their business processes. The executive team spent months reassuring its business partners and customers while corrective actions were taken to prevent this issue from recurring.

When IT leadership sponsors data quality initiatives on behalf of the business, IT needs to co-present with its business sponsors real-life operational scenarios that demonstrate the cause-and-effect relationship between data quality and the negative impacts on business operations. Otherwise, IT faces the following risks:

• Business leadership perceives data quality as an IT “plumbing” issue
• Business perception of never-ending technology project and funding requests for data quality projects that never seem to resolve the data issues
• Unable to fully implement data quality services (e.g., MDM) as there is no definition for success

Legal/Contracting

Most people laugh when they are asked if they have engaged their legal departments in discussions regarding data quality. The main reason is the legal department creates the standard terms and conditions for vendors and suppliers related to the purchasing and payments of goods and services. In industries such as manufacturing, aviation, aerospace, defense, and government, vendor-provided data is usually the source of data quality issues.

Many vendors, especially small and medium businesses (SMBs), will provide product and product version information via email or excel spreadsheets. This external data is typically refined and expanded upon by your product development, procurement, sales, or Internet sales organizations to address their own or their customer’s specific needs. As you can see, there are challenges already:

• Potential for different organizations to be unaware of a product or vendor relationship
• Vendor may not know when or to whom to provide product version updates
• Vendor does not know what additional product information is required beyond a part or SKU number
• Your internal departments are unaware of each other’s needs to augment vendor data
• Your receiving department may not have known to communicate its information needs on how to handle and manage these products as they pass through your business (e.g., receiving, handling and storage of hazardous materials or sensitive materials)
  

The business needs to perform its due-diligence before the terms and conditions are revised. IT can facilitate the discussions with the affected internal organizations to identify, document and standardize the information needs across your business for information around product, customer, etc. This allows IT be viewed as a partner to the business and helps IT create or refine the enterprise data model that captures all of the business information required to be successful. This enterprise model serves as the foundation for the information required by your business as well as identifies the potential data gaps within your business processes and systems.

Business Ecosystem

After the business understands their data and data quality needs and understands their client and business partner capabilities, the business has the opportunity to shape its “business ecosystem” by introducing concepts and/or expectations regarding data quality, data exchange, and data correction. This is usually a very sensitive discussion with the sales or procurement organization because the initial belief is that this will either risk or delay the contracting process or create additional work. However, the use of business ecosystems is not a new concept. One only needs to look at the automotive and electronics industries for examples of highly integrated and collaborative business ecosystems.

Does your business have a formally recognized ecosystem of vendors and partners? Is that ecosystem managed based on value and service quality or are they evaluated through a rear-view mirror based on historical volume, year-over-year pricing, or business size (e.g., veteran owned, SMB, etc)? By introducing data quality and electronic data integration concepts, your business now has the ability to further refine and differentiate who your preferred partners, vendors and clients are based on how well they help you accelerate the velocity and efficiency of your end-to-end business processes.

To further develop your business ecosystem, one can use contractual “carrots” that allow for special volume agreements, pricing incentives, and expanded products/services acquired or provided. These incentives can be piloted with your most trusted and mature partners to create the internal confidence and external trust as contracts are renewed or renegotiated. Conversely, contractual penalties could include contract setup/maintenance fees for having to manually manage and augment their business information within your systems, withholding payment for incorrect goods received, or charging warehouse/storage fees for incorrect goods waiting to be returned that are related to poor data quality.

When it comes to modifying the business processes to support contractual incentives around the ecosystem, IT can once again act as a facilitator to start transforming the business processes, structure underlying data, and expand your data exchange environment to support a business-driven ecosystem. Similarly, it allows the sales, procurement, and quality assurance organizations to act as a feedback loop to identify ongoing data enhancements to improve, streamline, and automate key business processes.

Sales

Sales organizations can be the root cause for originating data quality issues as well as perpetuating those issues. For originating data quality issues, one needs to look at what the sales team needs to do to record a new customer or a sales order. Executives should be very concerned if their sales team spends most of their time manually creating orders on spreadsheets or paper forms, manually creating invoices, or managing the ordering and delivery processes.

Leadership teams need to review how business systems are used to support the sales process and the underlying data to determine if they are perpetuating data quality issues within sales operations. Many companies, through business-unit driven initiatives, create and maintain silo systems. For example, a sales organization uses their CRM system for the entire sales order process while another business unit that is responsible for Internet-based sales ties their information directly to their ERP system. Both business units rely on product and customer information, but is the data the same? Are the sales teams aware of Internet transaction trends that may affect or influence their customers?

Leadership has a number of options to improve data quality around customer, vendor, and product information. Large and diverse organizations should first perform a cost-benefit analysis to determine the best road map to address near- and long-term needs and the associated funding. Depending on organization priorities, challenges, complexities, and transaction volumes, options could range from:

• Creating electronic/interactive forms that allow for part number and pricing to be validated and descriptions to be automatically populated
• Introducing repositories that are the authoritative source for specific data (e.g., customer, vendor, part, product, lot, etc) and a mechanism to synchronize that “master” data with other applications across the enterprise
• Consolidating business units, processes and application platforms to unite customer and product information
  

Summary

Business leadership is ultimately responsible for the quality of the data used and shared throughout their enterprise and within their business ecosystem. Business leadership is the primary change agent for eliminating data quality and integrity issues at their source. In the second part of this article, we will address how to identify and resolve and prevent data quality issues in the procurement, engineering/product development and quality/risk assurance organizations.

Recent articles by Craig Izydor

• Data Quality is a Business Issue: Getting Back to Basics, Part 2