DATA QUALITY

GETTING A GRIP ON YOUR PRODUCT DATA

Poor product data quality can create ripple effects throughout your business in the form of delayed product releases, incorrect data released to operations and the marketplace, delayed releases of required compliance data, and released products that underperform in sales because of lacking marketing content.

The importance of accurate and relevant product information is widely accepted, and is far from a new concept. But instead of addressing the actual issue of poor product information management with a formalized approach, companies often use up valuable resources simply combating the negative consequences.

The ability to manage hundreds of thousands of SKUs from a single location is critical for companies that face increased competition in an omnichannel world. SQLI can help you to define and implement a proactive data quality management program that will increase your bottom line results, as optimizing data will reduce costs early in the process.

CHOOSING YOUR PATH TO OMNICHANNEL COMMERCE

Working with SQLI as your partner will save you from many of the potential pitfalls of managing product data in a dynamic omnichannel commerce environment. We understand the principles of Data Quality Management and have a proven track record of implementing Best-in-class Data Quality Management solutions.

We can support you with commercializing product data from a single-source Product Information Management solution. This in turn greatly reduces costs, enables cross-functional collaboration, and helps eliminate the ripple effects of poor product data quality.

It is imperative that all your Data Quality Management initiatives set the stage for transitioning to the cloud. Starting with an initiative that standardizes product master data before you move your applications to the cloud is good practice.

HOW TO GET STARTED

SQLI can help your business with developing your Data Quality Business case, exploring all potential options and pitfalls together. This includes:

• Reviewing the types of risks related to poor product data quality, and specifying data quality expectations based on those risks.
• Clarifying what data quality means and defining data quality indicators.
• Defining data quality rules that can be used for assessment and governance.
• Identifying and measuring how poor data currently affects your business.
• Designing data quality improvement processes.
• Developing your data quality roadmap and building your information quality program