Build an analytics team

Business Benefits

Help professional growth and build a stronger analytics team.


Look for issues that might be holding your team back, like inefficient practices and lack of effective personal development.

Here are some of the most common issues:

  • Inefficient task distribution for each position. For example, if the senior analyst doesn’t have the time to teach and coach new employees and run managerial tasks because they are overwhelmed working with a client, that’s an issue. It’s also an issue if the junior analysts are studying all the time without getting tasks to get real work experience.
  • No measurement of efficiency for each team member. It helps to know how much time a particular employee spends in meetings, working on client tasks, or is busy with R&D.
  • Incorrect task time estimates. If you couldn’t estimate precisely the time needed for each task, it can potentially upset clients in case you need more time to finish things than promised.
  • Repeating mistakes. Whenever a junior analyst has to solve a complicated task for the first time, they make the same predictable mistakes. Correcting those mistakes is time-consuming.
  • Unintentional negligence. Sometimes, client emails would get lost, and you exceed the response time promised in your service-level agreement (SLA).
  • Speculative upsells. The data on how much you spend per task for a client, is not aligned with billing information.
  • Generic personal development plans. The same plan applies to every analyst, regardless of their strengths and weaknesses, thus they fail to grow.
  • Lack of knowledge transfer. Senior analysts are swamped with work and have no time to pass their skills and knowledge to junior colleagues, resulting in the slow growth of new specialists.

After you define and outline your issues, create a real-time dashboard for your analytics team and capture before-and-after metrics to measure the performance of individual members.

“You can’t manage what you can’t measure.” Peter Drucker.

  1. Start by identifying the sources of data. Gather data from the tools the team might be using, such as: Google Calendar, this data can help you understand how much time was spent on internal meetings and client calls. Gmail, can help to count email and response statuses to get info about analyst project, and overall correspondence with clients and the internal team. Task-management systems, can help to get data on workload and how each of the analysts manages their tasks.
  2. Pull the necessary data and define its structure. Use Google Apps Script to pull all data from those sources into Google BigQuery and translate data into insights. Use key fields like analyst, date, and project name to get a clear overview.
  3. Prototype the dashboard. Start with an MVP dashboard. Define the essential metrics that will answer your questions. Pick no more than 10 metrics and focus on critical KPIs. Ensure that KPI calculation logic is extremely transparent and approved by the team. Prototype on paper or with the help of prototyping tools, to check the logic.
  4. Build the dashboard. Use Google Data Studio as it’s handy and integrates easily with other Google products. In Data Studio, you can find templates designed for specific aims and summaries, and you can filter data by project, analyst, date, and job type. Use Apps Script to keep the operational data current, by updating it on a daily basis.
  5. Automate tracking instead of making people log time. But you shouldn’t make it look like you’re spying on the people working for you. Discuss all your tools and steps for improvement with the team.

Analyze data from your dashboard, looking at individual results as well as averages, and work with the team to develop solutions to issues that you identify.

Plan out what you’ll do if a metric rises or falls unexpectedly. If you have no idea why you should have such a plan for a certain metric, think over whether you need to track it at all.

For example, OWOX drilled down to specific roles and analysts, and found that the data looked quite different. Here, for example, we have data for Anastasiia, a senior analyst. On the left is the ideal, in the middle is the average, and on the right is her personal division:

Anastasiia’s time spent on client tasks was much higher than it should’ve been, and almost no time was spent coaching new employees. That could be for multiple reasons:

  • Anastasiia is overloaded with client tasks. In this case, they need to take some of her tasks and pass them to another analyst.
  • Anastasiia didn’t fill out the task management system properly. If this is the case, they need to draw her attention to its importance.
  • Anastasiia might not be a fan of her managerial role. They need to talk and figure it out.

In the end, they redistributed some of Anastasiia’s tasks and discussed the bottlenecks that were eating the biggest part of her time. As a result, her workload became more balanced.

Improve the task estimation process by classifying and clustering tasks, using tags in your task management system.

You can use insights from these tags as a baseline for estimating the time a specific task needs. When you have this information, it’s also useful for communication with clients and setting their expectations.

Some useful task tags might be:

  • R&D.
  • Case Study.
  • Metrics.
  • Dashboards.
  • Free, for tasks you don’t charge for.

When analysts create a new task, they can use tags to flag what they’re working on. Use a dashboard that shows the minimum, maximum, and average time spent on different types of jobs, as well as their frequency.

Write out detailed guides on how to perform repetitive tasks to help pass along knowledge and avoid mistakes.

For example, to create a report on cohort analysis, the guide can include:

  • Initial data.
  • Business objectives.
  • Limitations.
  • Patterns.
  • Self-checks.
  • What to pay attention to.

Use a special dashboard to monitor analysts’ adherence to your SLA commitments and avoid negligence.

You can use platforms like Slack to send reminders to team members when the dashboard shows that a team member is late, to respond to a commitment.

Analyze the efficiency of individual team members to identify where they lack skills, and instruct them to add that as a step in their personalized plan for self-improvement.

Ask analysts to give an estimated time they’ll need to complete a specific task in the task management system, and ask them to enter again how much time was actually spent after they finish the task.

For example, if you see that someone on the team spends much more time than average to complete a specific task, raise the alarm and speak with this member. If you identify they lack the technical knowledge to complete the task, add this to their step-by-step guide for self-improvement.