Top 5 Hacks to Successfully and Effectively Improve Data Quality

How would you best describe the use of data in the B2B world? Some say that data is fuel for a company that strategically plans and makes the right decisions. If fuel is lacking  power though, it’s likely for one’s revenue machine to under-perform. Interestingly, did you know that analysts spend anywhere between 20-60 percent of their valuable time trying to understand and fix poor data quality? For years, businesses have relied on data quality on a daily basis. It contributes to market trend analysis, streamlined operations, and improved buyer experience. For that reason alone, B2B brands must constantly keep data quality front and center. Similarly, in order to boost marketing campaign performance and strengthen data-driven strategies, it’s vital to focus on data quality. At the end of the day, data delivers useful information that B2B marketers can’t succeed without. On the same note, there are numerous dangers and risks when it comes to poor data quality and some of them include the following:

  • Poor decision-making
  • Missed business opportunities
  • Waste of resources
  • Inaccurate or misleading analysis
  • Reputation damage
  • Inaccurate sales predictions
  • Poor campaign ROI

ALSO READ: How AI Expands Prospect Data with Delicious Results

Top 5 Tips To Help Accomplish High Data Quality 

1. Standardize Data Entry Norms 

Standardizing data entry norms starts with setting strict guidelines for feeding data into a CRM database or other business systems. It’s important to  periodically review which data is relevant for your business and which is obsolete. By data profiling, aka profiling good and bad data, marketers can analyze data assets, measure data integrity, and even identify entities relatable across different databases. Overall, data entry norms improve data quality and data management practices.

2. Develop a Data Quality Dashboard

In addition, every business organization has its own specific set of data quality dimensions. Several of the most common consist of relevance, accuracy, consistency, completeness, and timeliness.

Furthermore, it’s crucial to measure data quality KPIs based on an organization’s data quality dimensions and data profiling findings. Operating a data quality dashboard offers deep insights into data measurement trends, issues, and data cleansing activities.

3. Integrate Sales and Marketing Data

Additionally, companies are known to  maintain data quality with sales and marketing integration. When both of these teams closely work together, accurate content can be delivered. According to Forrester research findings, organizations with sales and marketing alignment record an annual revenue growth of 32 percent. Whereas, it was reported that misalignment results in a 7 percent decline.

4. Embrace Marketing Automation

Next, it’s worth noting that higher data quality is synonymous with better conversion rates. A whopping 46 percent of marketers from both mid-size and large companies incorporate technology in their marketing stack with a shared goal of improving data quality. It’s important to not only organize data, but also make sure the data is fresh and updated. An ideal data management platform should have the required tools to properly monitor data quality. 

5. Assign Dedicated Teams for Data Monitoring

Last, but not least, one phrase to always remember is that data quality is no joke. Companies need dedicated teams whose primary responsibility will be about data management and monitoring. Nowadays, B2B marketers prospect and source accounts with intent data monitoring. To maintain top-notch data quality, a team of data professionals is a must-have.

Solid Data Quality Checklist 

Before diving in, make note of this checklist’s details and tips so you can make the right data quality moves for your organization.

  • Review data quality periodically to identify and correct common errors
  • Train and educate employees about data entry norms and data management practices 
  • Align sales, marketing, and data to target the right accounts
  • Implement duplicate detection tools to identify potential duplicates in your database
  • Describe data across all stages of the data lifecycle; good metadata improves the overall quality of data 
  • Include data quality activities within a data governance framework while also putting data policies and data standards in place
  • Begin with a root cause analysis to effectively solve each data quality issue
  • Data quality KPIs or data quality indicators (DQIs) must align with the general business performance KPIs
  • Data quality KPIs are related to data quality dimensions such as uniqueness, completeness, and accuracy
  • Incorporate a data quality firewall to keep data error-free and comprehensive

From buyer attrition to loss in market shares, at this point in time, numerous B2B organizations recognize several adverse effects of poor data quality. To dodge bullets like these, businesses should continue paying close attention to data quality. When you stay ahead of data hygiene issues, you can better position your brand for success. What will you do now to improve data quality in your organization?

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