Data Consciousness

I had a colleague who would say "70% of data is made up 100% of the time." This would often come at the end of a sales performance meeting. He was the VP of Sales... I would chuckle but always left wondering whether his numbers were real or made up.?

Data tells amazing stories but can be deceptive when misused, misunderstood, or mis-collected. It's especially damaging for leaders who don't understand the background of 'the data.' To be an effective leader, data consciousness is an attribute you should seek to have, especially in today’s workplace. Data consciousness is knowing how to capture, navigate, and understand data without becoming compulsive or obsessive. Data obsession hampers performance by emphasizing metrics over desired behaviors (example below).

This is not to say that metrics should be done away with. Rather, primary company metrics be thoughtfully established and not obsessed over.

CLARIFICATION Before we begin, let's recognize that 'data' is a very inclusive word. For this article, we’ll focus on data a business user creates or interacts with (call/text logs, forecast numbers, contact information, revenue numbers, etc.). This data feeds company dashboards, performance metrics, and so forth.

Let us continue--of all the issues that can plague our data, there are a few especially fickle items:

• Interpretative wiggle room
• Data capture
• Assumptions to Make Data

Interpretative Wiggle Room

Let's start with an example from Joe's Hair Restoration Products:

Sales were sluggish and we needed a boost. We decided to send our most eager salespeople to work our booth at some upcoming conferences.??

After a few weeks, we had so many sales it seemed impossible for our care specialists to keep up. We excitedly reported our improved performance to potential investors and were pleased to see the newfound interest in our company.

Unfortunately, a few months later our Care Director raised some concerns--we had very high cancellation rates from sales we closed at conferences. She questioned whether many of them should be considered sales at all.

The issue: traveling sales reps were submitting deals missing vital information like the credit card number, the desired subscription package, and sometimes the person's name and contact details.

We quickly stipulated, to our chagrin, that a sale would only be counted if the customer had chosen a subscription package, provided viable payment information, and signed the subscription agreement.

The "wiggle room" in this example was the definition of sale and the requirements for a sale to be included in performance reports. Removing wiggle room around core performance metrics with clear definitions will help guide behavior and ensure better performance quality (eventually, requirements were built into the sales system for Joe's Hair Restoration).

Focusing on data that accurately represents the reality of your customer activity will greatly reduce uncomfortable conversations with investors and decrease unnecessary arguments in leadership meetings.

Is there a metric you live by? Are contributions to that metric clean and understood? If not, figure out how to clean it up and stabilize it.

Places where wiggle room can easily hide:

• New sales - not all sales are created equal and not all salespeople pitch with the same clarity. Lookout for metrics like this where the incentive for an employee doesn’t necessarily match the company'scompanies goals. I.e. A sales rep may be incentivized to close a deal even if it means skimping on the data they gather.
• Cancellations - if you're not tracking revenue churn a downgraded plan won't show as a cancel, but you'll have decreased revenue.
• Ticket Response Time - disputed between the customer and the company. The response "we've received your issue and will get back to you," can be counted by the system as a response if you're not careful.
• Inventory - In-flight orders, stocked quantities, whether returns can be resold, damaged goods, etc. make tracking difficult. Clarify product quantity statuses to improve customer and employee experience.

Data Accuracy & Completeness

This category has too many facets to cover in a single article, but let's talk about data capture practices and assumptions.

Assumptions

There are a few kinds of assumptions. We will focus on two types: assumptions to augment data and assumptions to fill data gaps.

Augmentative Assumptions

Big Boy Bean Bags and Sofas (name changed) ships custom bean bags and guarantees customer satisfaction with a 77-day, no-questions-asked return policy. Their vacuum-sealed shipping method makes it impossible for unsatisfied customers to ship their bags back. Instead, Big Boy has the customer donate the bag and offer a refund.

Big Boy wasn't tracking cancellations so they weren’t able to understand the risk they assumed with their 77-day return policy. After some discussion, they realized that if they used their refund data and crossed that with their order data they could assume the cancellation rate without making customer service fill out specific forms. As a bonus, they also learned how many days after purchase customers were cancelling, on average.

This is a great use of an assumption to augment data needs in reporting without adding user processes.

If you must make assumptions to fill data gaps, be sure there is agreement on that assumption.

Assumptions to Fill Data Gaps

Here's a simple rule of thumb--if you don't have the data, don't make it up nor use data from somewhere else, ever.

I've seen 30+ leads with different names but that share an email address and phone number. I've seen commission calculators use zip codes of unrelated records to determine territory because an order was missing a zip code. I've seen clothing sizes assumed because the system didn't require a size selection at purchase. A grocery store in North Carolina was listed as the scene of thousands and thousands of crimes because whenever a crime occurred somewhere without a clear address, the responding officer would list “100 Main Street” on the report.

If you must make assumptions to fill data gaps, be sure that you have agreement agreement on that assumption from the stakeholders and consumers of that data.

Data Capture

Data capture has some simple rules:

• Have the most informed individual enter the data. This is usually the customer, but can also be the Sales Rep, the Customer Service Rep, or a myriad of other roles that end with 'rep'.
• Don't enter data multiple times (once upon a time there was an online portal where customers could fill out their order. They would then hit submit and print their order... and mail it into the warehouse!).
• Avoid transmitting data verbally for someone to type on the other end of the line.
• Don't capture more data than is necessary.

Make it possible for the most informed individual to enter the data. You could provide customer portals, online stores, a mobile app, or a user-facing implementation interface.

Whatever it might be, avoid multiple points of data entry and avoid handwritten data transmittal (try and decipher the prescription your doctor hands you the next time you need a script filled and you'll know what I mean...).

Conclusion

In conclusion, if you want to be an effective, thoughtful leader that doesn't get bogged down in 'the data,' but rather be elevated by it, become data conscious--It'll change your life.