Vasily Sabirov, Head of Analytics and co-founder of devtodev analytics service, often wonders what it means “to be a good analyst". Last year, Vasily worked a lot with different teams of analysts, took part in the hiring, adaptation and development of employees. He digested this experience and wrote an article about soft skills, which, in his opinion, are important for an analyst.
Knowledge of SQL, R, Python, mathematical statistics, the ability to work with Tableau and distinguish Bayes from Fisher and ARPU from ARPPU - all this is, of course, important for an analyst.
But lately, I’ve been increasingly thinking about the soft skills that an analyst should have. It was out of these thoughts that my article was born, and I need to emphasize that it is mine, - this is nothing more than a subjective opinion, one of many possible. I also admit that those skills that I will talk about can be applied not only to an analyst but also to any modern IT specialist.
What are soft skills
Soft skills are a complex of non-specialized skills that are responsible for successful participation in the work process, high productivity and are not related to a specific subject area. Unlike hard skills that we can relatively easily obtain and objectively assess, soft skills are something elusive, difficult to measure and change.
This topic has recently been quite popular, because soft skills, boosted in certain areas, turn you from an ordinary specialist into a sought-after specialist who is able to work productively in any team and feel comfortable in it. And it is exactly this skill that is important in the world where nothing is forever - workplace, colleagues, or even specialization.
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Analyst’s soft skills
Below I will list the most important skills that, in my opinion, any good analyst (and, in fact, any good specialist in any field) should have.
An analyst has to communicate with his direct superior, colleagues, as well as with many other people. Everyone expects that he will finish his job successfully and on time. To do this, he must be able to properly organize his working time and workspace. Here is what I mean:
- Predictable Availability. If we agreed to meet, the analyst should be there. If we agreed to discuss something today, we must be in touch.
- Messengers. It seems that even such a standard part of work as communication in messengers also requires a certain skill. If someone messages the analyst during working hours, they need to find time to respond and not to waste other people’s time on waiting. Often happens, that you are in a dialogue with a colleague, you have been actively chatting for about half an hour, then you write a message and your colleague doesn’t respond. But tomorrow they continue the dialogue like nothing even happened. How many hours are lost? And the problem is not only in the loss of one's own and other people's time but also in the organization of the workflow itself: unnecessary mentions in work chats, abuse of @here and @channel on Slack, waiting for an urgent response in a general chat without mentioning the right person, etc. Here, too, there is a certain etiquette that helps to make the work more effective and less stressful. And, right, never use voice messages.
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- Meetings. Planning and holding a meeting is also not as easy as it sounds. Ideally, it is better to draft discussion points in advance, and perhaps even give some homework before the meeting. When inviting someone, you should clearly indicate the method of communication (so that the colleague will not wait for you on Skype when you hold a meeting on Slack). The next important thing is a follow-up: after the meeting, it is necessary for all participants to synchronize the understanding of its result, turn the meeting into a list of tasks with names and dates and send it to them.
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What does all this have to do with the analyst? The most direct. I believe that the analyst is the most important source of ideas in the team, which means that communication in messengers and organization of meetings may well fall within the sphere of his influence.
The previous paragraph is, in part, also about team play. The analyst, perhaps, doesn’t need to be a great networker and can remain introverted if he wants to. But when we solve a common task, it is important to communicate with each other.
- Ask if you don’t know. Suppose an analyst writes a report and comes across an idea that is not clear how to interpret. It would be wrong to interpret it at their discretion (as it occurred to them, or as it is easier). What to do then? Simply ask. Conclusions made on the basis of incomplete data may turn out to be false, therefore the work of the task manager is to provide the analyst with all the data, and the analyst’s task is to ask if something is unclear.
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- Tell me if you need more time. Has it ever happened to you that you expect a result from a colleague to a specific date, but the day comes and there is no result? The colleague, at the same time, says that he did not have time/did not understand/got sick or forgot. This is a common situation and I think it has happened to everyone. Common advice: if you do not have time and you can not complete the task on time - warn the client in advance. And I regret to say that analysts often forget about this.
- Develop emotional intelligence. This is a relatively new concept, but lately, I hear about it more and more often. Wikipedia says that this is “the capability of individuals to recognize their own emotions and those of others, discern between different feelings and label them appropriately, use emotional information to guide thinking and behavior, and manage and/or adjust emotions to adapt to environments or achieve one's goal(s)”. What does it have to do with the analyst? First, we need to recognize that people are just people who have their emotions and can be in a completely different mood. Second, the development of emotional intelligence will help to better understand (and fulfill!) the task: what drives this person, what they need, why they need it (and if it is not clear - ask!). And third, emotional intelligence helps the analyst themself to stay afloat, better understand his current state and, for example, more accurately predict the task completion time.
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Proactivity and curiosity
Let's get back to who the analyst is and why he is needed. I do not get tired of repeating my favorite mantra: the main task of the analyst is to identify bottlenecks in the project and points of its growth. If the project has been working for a long time, if many iterations have already been done on it, and it is too late to pivot, if it is in the operation stage, then the analyst’s work can become a routine: analyze an update, study a promotion, evaluate traffic. And I believe that a good, curious analyst in this situation should not feel bored because their task is to identify bottlenecks and look for growth points. If one view does not fit - try another: levels, days in the project, payment behavior, countries, devices, languages, behavioral patterns. In the end, go looking for a solution in a competitive field - maybe competitor analysis will give us some fresh ideas?
And this is how we get down to proactivity. If the analyst only answers the raised questions then he is a bad analyst. If he constantly generates ideas and does not let his colleagues get bored, then he is a good analyst. The best comment a great analyst can hear about himself is that “he has ants in his pants”.
When working with analysts, I always tell them that analytical reports should have both answers to the raised questions and fresh ideas and recommendations, as well as new questions for decision-makers to think about.
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Critical thinking and skepticism
At the meetings, the analyst is responsible for the data. This does not mean that he will reject any points colleagues make. Also, this does not mean that the analyst cannot have his own intuition, yet intuition is based on past experiences.
But this means that the analyst must be able to turn off their intuition and even empathy. If we are not sure about something, we need to find a way to prove it. If the available data does not give us an answer, we need to plan an experiment. All these are the task of the analyst.
In this case, the analyst should be as unbiased as possible. The fact is that different data can be stored in one database, and besides, the same data can be interpreted in completely different ways.
Try to google evidence that the earth is flat. You will definitely find it, and it will even sound convincing. Try to find evidence that it is round - you will again find supporting arguments.
Based on these considerations (as well as on the movie “Behind the Curve” about people who are confident that the earth is flat) I propose the following exercise/ You can use it when hiring an analyst, or even when working with one. Suppose you are sure that the earth is flat, then the analyst’s task is to prove to you that it is round. They can use the Internet to prepare a convincing report with proper data and present it to you. And then do all the same, but vice versa: the analyst will need to convince you that the earth is flat.
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Understanding business goals and objectives
Novice analysts often do not see the trees for the forest and only work on the assigned local tasks, relying on limited data. Only with experience comes an understanding of how the product actually works and what levers you can use to increase its income.
The same applies to the simple execution of an analytical report. The analyst can package the same data in different ways, but in order to do this the right way, they must understand the customer’s mindset and their current state of mind. The analyst can generate a report only after he understands the motivation of the customer. For example, if we know that they have little time to get familiar with a report, then we will not present a table 300 x 300 instead of the report, but we will write out all the recommendations at the top of the report and give a link to the table (maybe the customer will have time for it later).
If the analyst has the ability to understand business goals, he is able to quickly filter out ineffective ideas that will not give a significant increase in income and change the focus to really important things and prove their effectiveness.
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An analyst with well-trained soft skills but no hard skills is not an analyst at all, but just a pleasant colleague and a dialog partner. The opposite case, an analyst with hard skills, but no soft skills are unpredictable and inconvenient in business processes.
So let's not forget that soft and hard skills should complement each other, and a good analyst should balance both of them.