If your CEO asked Customer Service, Technical Support, Marketing and Sales to provide a customer list with contact and history information, how likely would each organization bring the same list, with the matching addresses and history with correct and same spellings? If you answered not likely you’re not alone. This example illustrates a common data quality issues.

Companies are collecting customer data in multiple sources for example from call centers, websites, and billing in multiple databases from CRM, to sales, to accounting systems, and then trying to effectively use this data. There’s no doubt there are tremendous benefits in being able to analyze more data, faster, is the ability to take advantage of competitive opportunities more effectively and efficiently.

Various studies suggest that harnessing the power of data, in any form, is one of Marketing’s biggest challenges.  The ability to truly use data to foster “smarter” decisions is often hindered by the vast amounts of data, data located in disparate systems, the quality or lack of quality of the data they are using, and the amount of time it takes to “work” the data into something useful.

Three Essential Data Sources

Data Quality Effects the Quality of Your Decisions

While it may not be possible to quickly and easily resolve all of these issues, or to address data quality for everything, there are three data categories that are too valuable to leave unaddressed.  These are the data sources marketers use to develop the right content, produce the right touch points, and employ the right channels.

Once you’ve mapped the buyer’s journey and created the personas, there are three key data sources marketers need to successfully deploy for any tactical demand generation marketing program:

1. Contact data: This includes names, titles, roles, email, postal, and phone information, and traditional demographic data, such as industry, region, company size, and could also include things such as technology platforms, etc.
2. Sales data: This includes transaction level data such as product history, buying history, payment history, etc.
3. Engagement behavior data: This includes channel preferences (online, phone and other offline channels, such as events), touch point engagement (website, help desk, inside sales, outside sales) and content preferences (white papers, videos, demos, etc.)

These three data sources provide the valuable information every marketer needs in order to create and implement successful programs.  Without this type of data, it is much harder for marketers to do proper targeting and personalization.

Five Criteria of Data Quality

Many definitions exist for data quality. One of the most common definitions of high data quality is, “it fit for their intended uses in operations, decision making and planning.” When it comes to data quality we believe it needs to meet these 5 criteria.

• Completeness – the data value reflects all of the information it was designed to capture or convey, validity
• Reliable – the data comes from a reliable source or process
• Consistency – the same source or process produces the same data and the same data values for a given event or object should be reflected across the enterprise
• Timeliness – data should be sufficiently current for use, and
• Accuracy – the correct value should be recorded at the point of inception and this value is retained across the enterprise

Why Data Quality Matters

This may seem rather obvious but bad data leads to bad decisions. When it comes to make fact-based decisions, the importance of data quality cannot be stressed enough. The effectiveness of any decision is based upon the quality of the data used to make the decision. As a result, companies are starting to see merit in having strong data quality strategy. Building an effective data quality strategy can help with many of the problems associated with poor data quality by ensuring issues are identified, corrected and ideally prevented, providing a foundation for reliable and consistent data use across the organization.

Data Quality Requires a Data Strategy

Building a data quality strategy begins by developing a data quality profiling and data cleansing plan. Because a company’s data is a strategic asset, a data quality strategy should not be buried in the ranks of IT. It belongs at the executive level. Start with these 10 steps to build your data strategy.

1.Secure the leadership team’s commitment to a data quality strategy.

2.Avoid boiling the ocean. Select one specific business imperative and evaluate how the data impacts the ability to move this imperative forward.

3.Drill down to all the data elements associated with this priority.

4.Identify all the data quality issues in the data elements.

5.Identify the root causes of the poor data quality what processes need to be addressed so the data quality issues won’t return.

6. Create a single business rule for all data collected.

7. Define the information chains that feed into all the databases that support this priority.

8. Establish a master data management system and data stewards.

9.Monitor record completeness, accuracy, percentage of duplicates, etc in order to maintain data quality.

10. Be proactive, revisit and adjust processes as data conditions change.

Understanding and managing data quality can be a daunting task but an essential one for every company.  It is imperative that your organization understand the quality of  your data, how your data is currently stored and how these systems and data are currently being used. We’d be honored to help you this effort. Let’s talk about how we might be of service.

Comments are closed.