Excel-analysts
Virtuoso with all the spreadsheets, three-meter-long formulas, and conditional formatting. They neatly stack limitless files in different folders, naming each one with a phrase only they understand.
Strengths:
Careful and diligent. Send information to the customer only when they are really sure about it;
Have a good command of Excel tools, almost any task can be solved with its help.
Weaknesses:
Think there’s no point in learning new things if half the problems can be solved with two summaries and three vlookups;
prefer tables, lots of tables. Visualization, reserch, and analysis are more difficult, especially if research results need to be explained to non-analysts.
Recommendations: Excel analysts can be relied on for the most boring tasks. But it is time for them to switch to modern tools – databases and BI-systems.
Doubting analysts.
They start their conclusions with the words “approximately” or “most likely.” A common reason for this uncertainty is poor analytics infrastructure. A company has some ETL process crashing almost every day, or databases failing. So analysts can only say that conversions on branch A are 2-20% better than conversions on branch B.
Strengths:
Don’t mislead users and remind everyone that the data is inaccurate;
Honestly try to find the most accurate result and do everything to solve data problems;
Know everything about working with ETL processes, APIs, and database servers.
Weaknesses:
Get used to uncertainty about data and doubt results, even when working with complete data;
doubt the importance of what they are doing because they realize that incomplete analytics are hard to rely on.
Recommendations: push for infrastructure improvements with all your might. Talk about problems and push forward with goals to improve data collection.
Backlog Specialists
Their task lists are longer than the River Nile. They work from 8 a.m. to 8 p.m. Analysts prioritize, make priority lists, and then prioritize lists and still can’t get out of the perpetual backlog. Everyone around here knows that BI will answer any product question. That’s why you can find ETL process setup, building ML models, and collecting events across systems in the backlog.
Strengths:
Work without vacations or lunch breaks, trying to do as many tickets as possible. But the more tickets you do, the more they get thrown at them;
Are well versed in everything. They have the connections between all the tables saved in their heads. If you don’t understand why a country is written instead of the user’s email and the browser is in the country column, ask the analyst – he’ll tell you.
Weaknesses:
Because of the heavy workload, the backlog analyst doesn’t finish tasks – there just isn’t enough time to debug the processes 100%;
not enough time to offer ideas;
burnout. For the last year at the company, backlog analysts have been working out of their last legs. When they quit, they want to lie down and not see Jira for a couple of months.
Recommendation: this type of analyst should clarify the business necessity of all the questions that are asked. If you recognize yourself in this type, create a list of questions that the person must answer before giving you a task.
Analyst kings
Sprinkle ideas for product improvement, attend all the rallies and task tests, and if the decision to test or improve has passed them by, consider it a personal insult. Think they always have the last word and ask not to be assigned small tasks because they are built for more.
Strengths:
Are great at understanding the product, worrying about all metrics and imprudences;
Going into product managers where they can confirm their own hypotheses and see the numbers for themselves.
Weaknesses:
You have to look for a jun or midle to join such a specialist’s team for the “black job.” Because the analyst-king is a high-flying bird, and will not analyze 10 AB-tests a week;
Do not always hear other people’s opinions – there may be difficulties in communication.
Recommendations: listen and hear your colleagues. The product is made by the team. Even an excellent analyst needs help in development, design and other aspects.
Data Science analysts.
They have a huge technical background. Even if they need to divide 8,000 by 10, they turn on the Jupiter Notebook. Data Science analysts try to take on complex tasks on forecasting, segmentation, and models, but they keep getting asked to calculate budgets, find a couple of KPIs, or build a nice graph.
Strengths:
Versatile professionals who tackle tasks of any complexity. They have a wide range of hard skills and alternate between dashboards in BI systems and neural networks;
Solve most complex problems faster with programming languages.
Weaknesses:
Are willing to do all incoming tasks and often shy away from taking on more interesting projects worthy of their experience and expertise.
With Data Science analysts, you can solve complex problems. But you have to find common ground first.