Improving Data Quality: Safety in Numbers
In many ways, data is the lifeblood of a B2B organization.
In particular, both Sales and Marketing invest heavily in data to help them find the right prospects, get them the right messages, and equip them with the best information possible leading up to a sales call. Without the right information, you risk missing a key opportunity, pitching the wrong person, or worse – missing on your messaging due to crossed buying signals.
Finding several data sources isn’t a challenge, but quality across sources is often broad ranging. In many ways, data that is incomplete, exhibits low match rates, or lacks density across your target accounts can be more dangerous than no data at all. As a result, many of the end-users of this data that depend on it for campaign success or even quota achievement have been losing faith. The quality simply isn’t there.
The issue, in many cases, is that organizations are relying on just a few key data sources across Sales and Marketing. Gaps in the data can mean complete radio silence for large cross sections of an account base. Often times it can be very easy to get rich data on large global organizations, but the data quickly becomes sparse once you start including smaller or more regional businesses.
For marketing and sales professionals, data quality is paramount. What is the best way to overcome the challenges around data quality so many businesses face? Generally speaking, better data leads to better predictive analytics. The more inputs, the stronger the data.
It would be challenging for any one company to assemble such a breadth of data sources to achieve such levels of quality, but at Lattice, we have the luxury of building an unparalleled data cloud through economies of scale. By relying on dozens of different third-party data providers, scouring the web and social media sites to fill gaps, and then matching and enriching with our customers internal data, we can assemble a much more complete set of buying signals for companies of all sizes and industries. We are never relying on just one, two or even three data sources, but using sophisticated algorithms to match data from hundreds of sources back to unique accounts to give the complete picture of the true buying signals for your leads and customers.
According to SiriusDecisions, organizations with strong data will realize a 66 percent more revenue than companies with average data quality. Here are some best practices from Experian for ensuring data quality within your marketing and sales organizations:
- Define the data quality project plan – Michelle Kavalchuk of Experian cautions marketers to understand where most data quality issues manifest to ensure checks can be put in place to minimize the quality issues.
- Standardize contact data – Determine the format for data capture and ensure that all information is entered in the same fashion, at point of entry.
- Validate data accuracy – Ensure accuracy and cleanliness by using various tools.
- Identify duplicates – Use a tool to identify duplicates and have a plan in place to address them.
- Append data – Match the data up with reliable third party sources only after the internal data is sanitized and standardized.