How to make sense of all the data I am using as a first-time manager
Ah, data! The eternal mystery that is all around us, yet the cause of many headaches. What is it about data that just turns some people off? Why do some get very discouraged each time they hear the word? In a world of ever-growing data analysis, where technology is basically creating digital resumes of everything we do and click, the study of this subject has never been more critical.
The role of people manager is one that works really well when paired with data. Now, that’s not the same as saying you need to be a great statistician to become a leader. You do, however, need to learn the basics and make the data work for you. In this article, we will go over the following aspects that will help you get a bit more comfortable with this topic:
- Why Data Matters
- Quantitative Data: Measuring the “What”
- Qualitative Data: Capturing the “Why”
- The Perfect Spot: Combining Numbers and Context
- Practical Checklist: Select Your Data Approach
Part I. Why Data Matters
Like many of you, when I first started out as a team lead, I had to deal with this topic. I wasn’t very good with numbers, and I kind of avoided the subject altogether. I enjoyed the more artistic side and always felt like the people person who loves to talk and meet people. Never did it cross my mind that I would end up working with data as a lead. You could say I was a bit scared of all the numbers and what I assumed was expected of me back then.
For the first-time manager with less of a data inclination and a more people-oriented view on things, the idea of becoming a data wizard is scary. Some people get turned off so much by this idea that they don’t even contemplate the role because “I am not good at numbers, so what’s the point?” In reality, this is a big myth, not really based on reality. Leadership is not really about the numbers – it’s about the people. Yes, we need data, absolutely, but again, we don’t have to be mathematical experts.
The first thing we need to do is better understand why data matters. It happens very often, especially in this role, that we rely on that “gut feeling.” It’s that inner voice that tells us how to react in a specific situation. On the other hand, we have the ever-present dilemma of “drowning in dashboards.” While your gut feeling can be of use from time to time, decision-making for a good leader is rooted in data. You don’t just make decisions without having proper context and understanding of the situation. Because we deal with both people and processes, we are surrounded by data. Every little aspect in your team is data. For example:
- How many people you have
- How many hours per day they work
- How many clients they manage
- What the total volume of work is in a day
- What the number of leaves in your team is this month
If we look closely enough, we can transform anything we have into data. Yes, the world is a big bucket filled with 0s and 1s. Keeping that in mind, we must understand that we need this data to be better, to help our team improve, and to get closer to that success we want so much. We can’t do anything without it, and people who tell you data is not needed for a good leader are not telling the whole picture.
Because we spend so much time working with operational issues, we need a proper way of matching these issues to the correct category of data so we know what to measure and how to improve it. If you don’t know how to study the Average Handling Time or your CSAT Score, how will you be able to improve them? You can’t just go to your team and say, “Hey, we need to fix the AHT.”
In a simpler way, numbers will tell you what is going on, while context will tell you why. Let’s say you look at a report and observe that the Average Handling Time in your team is higher than last week. You now know WHAT is happening. This is something you can then take to the team to see WHY it is happening. To get context, you need to do a proper drill-down, and the best tool for this is RCA. An overview of it is available in this article if you want: Root Cause Analysis (RCA): How to Stop Repeat Process Failures.
Part II. Quantitative Data: Measuring the “What”
When we think of quantitative data, or the WHAT, we talk about two distinct types of data: Discrete and Continuous. Why is this important, you might ask? Well, based on the type of data you are working with, everything changes, because each has its own set of rules and ways of working.
Discrete Data
These are your basic categories. Discrete data is data that can’t be broken down into fractions without losing its significance. For example: Pass/Fail, Yes/No, Man/Woman, Left/Right, High/Medium/Low, etc. When you look at these numbers, you can’t break them into fractions—you can’t say “half pass” and “half fail,”. Here are some examples from operations:
- Number of tickets
- Number of errors
- Number of employees
- Number of processes
- Number of customers
The only thing you can do with discrete data is count it, because these are distinct and separate values. They are best visually represented by using Bar Charts, Pareto charts, P-charts, and NP-charts.
Continuous Data
These are your basic measurements. This category can be broken down into fractions without losing its significance. For example: temperature in the room, duration of a call, height of a person, weight of a person, etc. These usually result after a measurement. Here are some examples from operations:
- Average Handling Time
- Cost per Unit
- System Latency
- Invoice Cycle Time
They are best visually represented by using histograms, box plots, I-MR charts, and XBar-R charts.
Depending on what category you are working with, things change. Here are the main differences regarding statistical power, operational focus, common metrics, and rules of thumb:
- Statistical Power (How much data you need to detect changes in your process):
- Discrete Data: Ranks LOW and requires a large sample size to actually detect process shifts.
- Continuous Data: Ranks HIGH and can detect process shifts with a much smaller sample size.
- Operational Focus (Where you can best use them):
- Discrete Data: Perfect for defect rates, compliance flags, and volume totals.
- Continuous Data: Perfect for variation reduction, process capability, and cycle time efficiency.
- Common Metrics (Where we see them most often):
- Discrete Data: SLA Compliance (Pass/Fail, Met/Not Met), Defect Count, Staff Attendance (Present, Absent, On Leave, Sick Leave, etc.).
- Continuous Data: Average Handling Time (AHT), Processing Cost, Delivery Lead Time.
- Rule of Thumb (What questions they answer):
- Discrete Data: Answers “How Many?” and “How Often?”
- Continuous Data: Answers “How Long?” and “How Much?”
Part III. Qualitative Data: Capturing the “Why”
This is all about the “Why.” Qualitative data captures what numbers can’t: characteristics, non-numerical feedback, and sentiment. This is what we use to make sense of the less easy-to-work-with aspects of a process. They also have a split based on how the information is structured: structured qualitative input versus unstructured input.
- Structured Qualitative Data is all about categorized or coded feedback (e.g., standard drop-down reasons, reason codes for escalations, binary survey responses). Out of the two, this is easier to work with as it can be easily aggregated into numerical trends.
- Unstructured Qualitative Data is all about free text – long, very difficult-to-work-with blocks of text that come from all sorts of places: free-form text responses, interview transcripts, direct customer feedback notes, etc. Nowadays, it’s a bit easier to work with as we have plenty of tools that can help translate whatever is going on in the text into a more analytical view.
Now that we have a better grasp of these qualitative data types, we must understand and answer the million-dollar question: when to use feedback versus numbers. You see, in order to fully master every little aspect of your process (and team), you must decide when you will more likely use feedback and when you will use numbers. Now, you should know that even if we say to use one or another, it’s never fully like that. I know, it’s a bit confusing, but in reality, it’s more about what proportion of each to use. One type will create the question in your mind, and the other will answer said question.
Here are some good, generally applicable examples of when to use Feedback over Numbers:
- When quantitative metrics flag an anomaly but cannot reveal the root cause (e.g., AHT spikes without a system outage). Feedback in this case will explain what is going on there and what the root cause is.
- Measuring employee sentiment, morale, or change readiness during a process transition. While some KPIs can highlight that something is wrong with overall team or employee sentiment/morale, feedback can actually help you do a proper deep dive and get to the root cause of the issue.
- Evaluating user experience (UX) nuances that standard success/fail flags miss. While you have a flag for success/fail, you don’t really know what is going on there. You gather feedback in this case to try to understand the situation.
Part IV. The Perfect Spot: Combining Numbers and Context
I mentioned above that we never use fully one or the other, and that it’s more about what proportion to use from each. This is that perfect spot we must achieve when it comes to combining numbers and context. Fully relying on numbers or context is one of the biggest mistakes a manager can make. When we just use numbers, we tend to transform more delicate situations (those where context matters more) into a sort of checklist. The best example of this is the annual performance review; yes, you read that right. While advocating for data-driven decisions, especially in this context, performance is not just about numbers. When you transform your employee’s evaluation into a sort of bank credit approval checklist, you lose sight of other, more important aspects: employee behavior, communication, way of working, and engagement level. You can’t tell from numbers if someone is a good performer but a very negative person who always picks fights and causes general unrest, nor can you tell if a person, while having average performance, is going above and beyond to generate great customer satisfaction (which isn’t always translated into numbers).
So how do we combine these two aspects? Well, there is a sort of framework you can use. This is something I picked up over my years of experience, and in principle, something that most tenured leaders tend to do:
- Spot the trend in Continuous/Discrete Data (e.g., SLA compliance dropped 12%).
- Categorize the issue via Structured Qualitative Data (e.g., 80% of breached tickets are tagged under “System Delay”).
- Validate the root cause via Unstructured Qualitative Data (e.g., team feedback reveals a specific software release caused screen lag).
This approach is easy to follow because it leads you from the GENERAL to the SPECIFIC. You start off with the overall issue (SLA dropped 12%), move into why this is happening (80% are tagged as system delay), and then validate this statement with feedback.
Part V. Practical Checklist: Select Your Data Approach
Here is a more in-depth checklist you can start using to help you select the right data approach:
- Identify the specific operational question you need to answer
- What it means: Don’t start by looking at what data you already have; start by defining the exact problem you are trying to solve.
- Why it matters: Collecting data without a clear question leads to bloated dashboards and wasted time analyzing irrelevant metrics.
- Operational Example: Instead of asking “How is the team doing?”, ask “Why did our resolution time spike during the 2:00 PM shift on Tuesday?”
- Determine if you are measuring volume/speed (Quantitative) or cause/sentiment (Qualitative)
- What it means: Map the question to the correct data category immediately so you don’t use the wrong tool for the job.
- Why it matters: Numbers won’t tell you why an employee is frustrated, and customer comments won’t give you your average handle time.
- Operational Example:
- Need to track how many tasks failed SLA this week? Use Quantitative (volume/speed).
- Need to understand why agents are skipping a mandatory step in the process? Use Qualitative (cause/sentiment).
- Ensure continuous metrics are logged with exact units (e.g., minutes/seconds, not rounded hours)
- What it means: Log continuous data at the highest level of precision available rather than rounding or grouping it into artificial bins early on.
- Why it matters: Rounding hides process variation. If five tasks take 12, 18, 22, 29, and 55 minutes, rounding them all to “1 hour” completely masks the performance gap and makes bottleneck analysis impossible.
- Operational Example: Record handle times as 04:32 (minutes and seconds) in your raw logs. You can always summarize or round data later for executive reporting, but you cannot restore lost precision when doing root-cause analysis.
- Standardize free-text inputs into structured categories where repeatable patterns exist
- What it means: Replace open-ended text fields with dropdowns or standardized tag lists whenever a recurring issue occurs.
- Why it matters: Unstructured text (“System froze”, “App crashed”, “PC lagged”) cannot be filtered or charted efficiently at scale. Converting common text patterns into structured tags allows you to run Pareto charts (80/20 rule) instantly.
- Operational Example: Instead of a blank notes box for ticket escalation, require agents to select a mandatory tag (System Lag, Missing Doc, User Error) alongside their detailed notes.
If you have any questions or suggestions, or if you would like me to do a more detailed article on a topic I have already covered, please feel free to reach out to me at contact@thecorporatebite.com or by using the contact form on the About Page.
Until next time, stay happy, healthy, and help others as much and as often as you can!