What came first: the analytics or the data quality?
Like the chicken and the egg conundrum, it may seem there is no right answer. Your business can’t use analytics effectively without quality data to input, but you’ll never have clean data without analytics to expose the data errors.
However, the real problem is that there is no such thing as clean sales data. It’s a myth. Your data is dirty, and so is everyone else’s. In sales, a team of reps is entering new data into a CRM each day. Inevitably, some of that data will be slightly inaccurate — and that’s OK.
While you do need to collect sales data in order to analyze it, it doesn’t have to be 100% clean, pristine, perfect data. It’s how you handle the data going forward that makes the biggest difference in your ability to access accurate analytics. Your sales team must have a constantly evolving process in place to clean your data, improve your data, and drive better data performance.
In fact, the chicken and the egg question doesn’t really matter. What does matter is how you train your sales team to manage data quality today. With the right processes in place, you can take advantage of analytics even without the unicorn of perfectly clean sales data.
Who Owns the Data?
Today, a lot of people in tech are excited about machine learning because machines are much more reliable than people for data entry (though machines can still make mistakes too). The majority of the dirty data in your CRM today is probably due to a sales rep mistyping, leaving a field blank, or some other small mistake.
Those mistakes compound over time, and eventually become unmanageable. After months or years of data errors, you may feel completely overwhelmed by the prospect of trying to clean your sales data — but you have to start somewhere.
While it may take a while to go back and clean your historical data, today is the day to implement a new process for cleaning your data in the past, and going forward. The first step: who owns data quality on your team?
You need to have one source of truth.
At some companies, data quality is owned by sales; on other teams, it’s owned by marketing operations or finance. It really depends on your company’s organizational structure, and what works best for your team, according to sales operations expert, Lauren Kelley. She explained that as SaaS companies grow, this decision becomes even more vital.
“The SaaS model has many more moving pieces than a traditional product sales model,” she noted. “Those moving pieces need to be coordinated and you need to have one source of truth in terms of what the numbers are, so that the pipeline, the forecast, the billing, the invoicing, and the cash of the company are all coming from one source of data.”
Kelly recommends that sales operations owns data quality, but that the role sits on the finance team in order to integrate all the disparate systems. However, the important part is simply making the decision and giving one team the responsibility for data quality. Once you have sales ops in place on your team, set measurable goals for data quality. With the authority to enforce the rules across the sales team and goals to drive them, you’ll see a rapid push to improve your sales data.
Analyze Your Current Data
Now that you have a leader focused on data quality, it’s time to delve into the data. What’s in your CRM today?
It’s impossible to know what your team needs to improve until you understand the exact challenges you’re facing. Do you have major gaps in data? Missing fields? Inaccurate numbers, or information that isn’t in the right format? If so, that’s the first thing you need to improve. For Jonathan Bunford, Sales Operations Manager at Influitive, analytics shone a light on their database, revealing exactly where it was dirty.
“Analytics helped highlight the need for our data to be clean, otherwise our tracking and forecasting is unreliable,” he explained. “It’s easy to realize you have a problem when you find yourself tripping over duplicates within your database.”
Bunford closely examined the sales process on the team, outlining what reps did to input data into the CRM. He recommended that every sales team start improving data quality by training reps on the correct processes, and pushing stronger enforcement of data quality right away.
“Start small,” he advised. “We all struggle with unclean data within our database and if you try to clean it all at once you’ll likely find it too overwhelming. Start with one object and move forward at a steady pace.”
Analytics highlighted the need for our data to be clean.
The project may take months to complete, but eventually and incrementally, your historical data quality will improve. Make sure the sales ops team is slowly but surely working toward specific goals, and progress will be made.