The Role Of Key Players In Data Quality

It’s easy to talk about improving data quality in an organization. You can have all the meetings, slide decks, and memos you want. But action is different. Action requires key people in your organization to step up and start making a difference. So who should those key players be?

Well, put simply, everyone. Building a culture that fosters data quality is the job of everyone in your company, from the reps reporting everything in the CRM, to sales managers ensuring everything is right, to sales ops personnel using this data to drive the entire company forward. Each of these 3 key roles has to work independently and in collaboration to build the process of data quality and accountability.

Sales Reps As Instigators

The role of the sales reps in the data quality culture is making sure every ounce of pertinent data is captured, quickly and correctly. Again, this is one of those things that sounds wonderful in theory, but often fails in practice.

Sales offices can be hectic, and remembering to change every dropdown, fill out every text field, or select every radio button in a CRM is a challenge for even the most diligent of reps. The point isn’t that it has to be perfect every time, but that reps are accountable for their own data.

An example of where data might be missing is in the reasons that an opportunity became closed-lost. Reps will want to move on to the next open opportunity straight away to maximize their time and efficiency. This data would seem to have little effect on their metrics, so it will be a low priority.

Here is a graph showing the most common reasons that opportunities were closed-lost.

It seems that timing is the most critical reason, with a loss of momentum and a lack of authority among the contacts also an issue. But, in this case, approximately 50 opportunities have no reason at all listed for their loss.

There is always a reason for the loss. Whether it’s something as common as timing, or something as exceptional as the rep accidentally insulting the client’s mother, there is always a reason. If this scenario occurs in an organization, it is a signal many things could have gone wrong:

  • Reps aren’t taking this particular data entry seriously enough.
  • Managers aren’t holding the reps accountable for their closed-lost deals.
  • Sales ops isn’t controlling the data entry enough, so that there is no option to leave this blank.

By making closed-lost reason a required field, sales ops can easily make sure that reps always have to account for their lost opportunities.

With the same overall number of closed-lost opportunities, but requiring a reason for each, the data will look dramatically different. If the majority of those missed entries were from need, then that could be brought into play as the most pressing issues for the team to address.

In this case, sales coaching and the entire sales process would need to be changed to account for prospects not seeing a need for the product. Without a required field or the reps being accountable for their data, this very serious issue in the selling process would be missed.

A culture of better data quality doesn’t only mean addressing missing data, it also means data that misrepresents reality and the actual quality of the data you already have. In the same scenario, it’s possible that reps are mislabeling opportunities as a timing issue, when really they have lost momentum. These two can be intricately linked, as accounts that are becoming quieter and losing momentum could easily be characterized as having a timing issue.

Additionally, reps might not want to admit an opportunity lost momentum as it puts the blame on their shoulders, whereas saying it was a problem within the prospect company can help shift the blame. If we take just 10% of the opportunities lost to timing and add them to lost momentum, we get a different view of the issues at stake:

Timing is still an issue, but it is now lost momentum that seems to be driving most closed-lost problems. Again, this drastically changes where the company should be expending its effort, focusing now on data-driven sales coaching to keep momentum going.

This may all seem like it is an issue for sales ops, making sure that the data is in correct and, in this case at least, forcing reps to do so if they aren’t already. But by making reps accountable for their own data, this problem is spread from the shoulders of one person or team, sales ops, throughout the whole sales team.

Issues like this should be raised by the sales reps themselves, especially once they are accountable for the data quality. It is their responsibility alongside management and ops to identify where efficient changes can be made that increase data quality. They should identify these barriers, work to resolve them, and make the team work better overall.


Interactive Pipeline Benchmarks

See how your company stacks up against others in your industry by exploring our
filterable Pipeline metrics.

Sales Leaders As Enablers

Though they might not be sitting at the CRM punching in the data, sales leaders still have a crucial role in making sure all the data is complete and accurate. The main role of management in the data quality culture is two-fold:

  • Working with reps to identify problems and provide solutions.
  • Coaching reps to keep data quality high.

Yes, they should also be making sure the data is accurate, checking the dashboards and using their experience to realize what’s missing. But, for data quality, the role is more as a facilitator rather than a disciplinarian. They should be helping reps get the best data rather than berating them for not having it.

The best way sales leaders can do this is through showing, not telling. By using sales coaching as the jumping off point for increased data quality, leaders can get better sales and better data out of their reps.

For example, everyone wants to shorten their sales cycle. A shorter sales cycle means more revenue in the same time window, and therefore more revenue throughout the quarter and year. So any efficiencies that can be made will go down well with everyone on the sales team.

The number one way to shorten your sales cycle is to remove badly qualified leads before they get into your funnel. By disqualifying early the problems that usually elongate the sales cycle—objections, issues with fit—will be removed.

For managers to find out what does cause the cycles to stumble they need accurate data. They need to know exactly what is hanging up customers at different points and what reps are currently doing to move them along. Then they can formulate the correct process to shorten each part of the funnel. But not without the data first.

By showing reps what will happen with just a few efficient advances within the cycle, leaders can get reps on board with increasing data quality, as well as providing the insights that the reps need to implement any new processes.

Here, a 10% decrease in each stage of the cycle leads to an overall drop in sales cycle length of 4 days. This means over a year, a rep can fit an entire new cycle in, increasing revenue for the company and compensation for themselves.

This is a case of a continual circle of reps giving leaders better data, and the leaders giving the reps better coaching. But only by using clear cut and practical examples, rather than just complaining about the data, can leaders start to build this into an ongoing process.

Sales Ops As Drivers

Sales ops are the people you’d most expect to be obsessed with great data. Their jobs rely on getting the very best quality data from the team so that they can report truthfully to upper management and provide the real data-driven insights that the rest of the company needs.

In this case, they are more than just a coordinator, pulling in data from disparate sources and collating reports — they should be seen as the hub for this collaborative process. They can tell reps and leaders where data quality is currently lacking and how it can be improved. They can also show easily what the difference between good and bad data looks like, acting as the driving impulse behind this culture change.

Again, examples are paramount for any sales ops person wanting to get their point across. Using clear examples that have a profound effects on the reps and the company enables them to drive these points home and make sure the process is adhered to.

One such example is push rates, how often reps push closing dates further into the future. Reps are likely to a) be optimistic with initial close dates, and b) not want to push close dates too far into the future when they do have to push. Therefore, close rates are some of the elements most likely to change over time, and the ones most unlikely to resemble true reality.

The above graph shows what happens when push rates start to overwhelm a company. If the push rate was 0%, then they would book $1M each month in revenue. But when the push rates start creeping up, more and more revenue is kicked down the road to the next month. In this case, the push rate for April is 40%. Therefore instead of booking $1M that month, the company is only booking $600K.

The big problem with push rates is that they compound. Now there is more revenue due to be booked in May, but if the reps couldn’t deal with $1M in April, they aren’t going to be able to deal with $1.4M in May. This means more is pushed, with the rate creeping up to 50%. The same goes for June. Now more is being pushed than booked, raising next month’s initial target exponentially.

This is a massive problem for a company, but one that can be avoided through better data and better coaching:

  • If reps input realistic close dates both initially and when they do have to push, then management can better plan for that amount of revenue in each month, either bringing on new hires, or spacing out future prospects.
  • When they have this better data, leaders can work with reps to start to identify why opportunities are being pushed to bring down the overall push rates.

By identifying where in the organization bad data quality is leading to serious issues, sales ops can help drive the whole team to making sure this kind of problem doesn’t occur. They can be providing exactly the right insights that could be difficult for managers and reps to identify themselves, pushing everyone forward.

Sales reps work as instigators of better data quality, sales leaders as enablers of better data quality, and sales ops as drivers of better data quality.

But what should be evident from the examples above is that none of these can work alone. It is a team effort, with everyone working in collaboration.

  • Reps need to tell leaders and ops of potential pitfalls in the current process, while implementing any changes or coaching they advise.
  • Leaders have to help push reps for better data, while giving them the coaching that they need to do just that.
  • Ops have to show reps and leaders where the better data quality can really help the team, while listening to their feedback about what does and doesn’t work in the field.

Even the C-suite should be involved. It is up to them to imbue the entire organization with a culture of great data quality. If the CEO makes data quality a priority, it will be a priority all the way down the line.