The race for data throughout the B2B sales world has increased the efficacy of many organizations, but one of the negative consequences of this new dynamic is that the quality of the data now often takes a back seat to quantity. Sales leaders now have access to CRM and sales management solutions that can handle nearly any data input you can think of, and it’s natural for them to think that the more inputs they are able to populate, the better they will be able to understand and analyze the intricacies of their particular sales process. The fact that these data points are only optimally effective if they are complete and accurate is being left on the cutting room floor.

According to a case study published in the Data Science Journal, there are five overarching categories that quality indicators fall into: availability, usability, reliability, relevance, and presentation quality. By understanding how these indicators impact sales processes, and by developing an organizational culture where data quality is everyone’s responsibility,

Understand the source of the disconnect

In any serious assessment of the state of your data quality, the first goal should be investigating why you are failing in one or more of the five key indicators. While sales managers and executives, the marketing department, and even accounting share in the sales data collection effort, it’s apposite to examine the day-to-day efforts of the sales reps, since they likely have the most hands-on time with CRM entry.

The thing about many sales reps is that they got into B2B sales because they excel at, and enjoy the selling experience. They understand that their best asset is their ability to forge a unique connection with a client and effectively communicate a value proposition. Even if they understand on a high level that sales data is important for the organization, they may see time away from core sales activities as hindering their productivity.

Weave the fabric of your data gathering strategy into the sales process

Sales reps need to be coached to expect that personal productivity and organizational productivity go hand-in-hand, and that taking the time to record complete and accurate data positively impacts both. While ensuring the efficacy of the data collection process may result in a slight decrease in some traditional sales productivity metrics, high-quality data improves the odds that the leadership team will be able to make sound strategic decisions that will impact the performance of everyone in the division.

This, of course, begins with an investment in coaching from the sales leaders. They need to be prepared to go beyond issuing a directive stating that data quality is a primary concern for the department, and take the time to walk reps through why it’s so important and how it impacts the organization as a whole.

Aim for specificity and accountability

The idea of improving day quality for increased productivity has been around for a while, but too many managers’ sole takeaway from this concept is that they need to instruct their team to do a better job at it. If the sales reps don’t have access to specific reports and detailed plans for how their activities are being used, it’s unlikely they’ll internalize the concept. Telling your team, “You need to complete all data fields with accurate information” sounds overwhelming. In contrast, explaining that, “It’s important for us to know exactly how many customers per week disconnect following the first sales call, because it results in X lost opportunities per month” provides concrete context that is more easily internalized.

Once you have established the specific criteria and provided the appropriate context, you can then feel comfortable holding the reps accountable for their efforts in conjunction with fairness and transparency. These are the core steps involved in building a respect for data primacy into your sales processes, so that future onboarding becomes easier, and employees are well-prepared to train new hires on data collection protocols.

Even one field can dramatically impact your growth projections

One of the most significant ways in which data quality impacts the sales effort is through lead generation. Better lead generation makes everyone’s job easier, and analysis of sales pipeline metrics indicates that this is one area where small differences in even one data point can seriously impact your numbers.

When forecasts are made off of incorrect data, it trickles down through the entire organization. Budgets are reevaluated, cash flow expectations fluctuate, and the sales team bears a greater burden to correct the downturn through unrealistic goals. This demonstrates that the road to data quality isn’t necessarily completed in one journey; upgrading the quality of even one metric can make your organization operate more efficiently and snowball into more effective data collection across the board.