In terms of modern highly digital and competitive business environment, it is crucial to search, select, and analyze various types of data. Thus, marketing specialist and analyst who works with large data sets in one face is the third most wanted position. We have gathered 13 basic qualities one has to recall when searching for the position of market researcher/analyst.
Even if you possess 5-year experience and broad knowledge, still mind these recommendations to get paid higher. Also, this job depends heavily on the personality type of the candidate. The 13 traits listed below lead to a complete expert in the corresponding field. Here is my short review of each.
Curious and striving to learn – point-blank which might be helpful for almost any kind of position. It is the major feature which explains the difference between just a job and professional career. After all, the job is something you do to get paid while a career is something you’re living. It’s a separate scenario of your life which comes along with family, hobby, friends, and other aspects. There is no way to cultivate this feature. You either born with it, or you don’t have any desire and passion. That is sad so far as it means you need only money per living, with no spiritual development and self-actualization.
Unlimited desire to grow and absorb new skills – the thing is despite you can be really keen on the particular subject, you should perform enough curiosity and efforts to go deeper into the details.
Do not simply state 39% of growth in sales and skip to the next step – analyze and decide why 39% is exactly 39% (meaning where the number comes from).
Self-motivated and inspired – in other words, try to be proactive in your analysis. Don’t wait for your manager to ask, “Why?” or “Give me some good explanation why…” A person working as a professional data analyst should be extremely motivated to look for, collect, and observe/analyze definitions/terms to provide the received information with the due-diligence.
Flexible and ready to adapt – as some experts stress, despite you might possess a certain preconceived prejudice about how to interpret numbers and graphs, be eager to accept various visions/explanations, and try to configure your outcomes on the go.
Imagination is a “must-have” – being ‘imaginative’ is not only about presenting the data, but also analyzing it deeply. First of all, always forecast your next steps of how to deliver the main idea of the given information/database. Ask yourself where the consequences of a particular action come from before implementing it. For instance, you may wonder what will happen after breaking specific demographic according to one of the factors.
Skeptical – you may wonder, but it’s not an exclusively bad trait. However, you should be able to doubt your data accuracy. The irony is an important factor. It is the same as being objective. A true data analyst cannot be subjective and too much stubborn. On the other hand, he/she should not accept just any decision or proposal. Frankly speaking, no process of data/info gathering is flawless. Thus, frequently data analyst can benefit from moving one step before in order to obtain the answers to the critical issues based on the retrieved results/outcomes.
Do you know what playing devil’s advocate refers to? That is how every data analyst must be able to act. It’s up to you to give the research department a wrong adverse report. No matter what happens, it is crucial to provide the most accurate analysis.
Know what is really worth a penny – this point refers to realizing what makes sense and which activities are not worth your attention. There is no time to process each existing cross-tabulation in your study. Remove all small and insignificant tasks; always begin with the largest. Leave the most complicated assignments for the end: your co-workers might help.
Methodical – it means staying systematic. Every data analyst has to create own approaches and develop the methodology. If you did well in your statistics class, most probably you would be ready with own research techniques ASAP. A professional analyst can develop a logical technique and go through the entire check-list of what must be done with a data received.
Can place templates/samples – the skill which involves spotting tendency or layouts in the data set. Placing the data patterns needs an original eye and fresh ideas. As a rule, it does not take place until you notice it in a graphical format/shape.
Analytical brain – well, it’s pretty obvious, but still we would like to stress it. It is almost the same for the reverse engineering. It may be insignificant that 69% of the customers visited site’s order form to make a deal during the previous week.
Find out what percentage of those guests are people of different income. In case they belong to the middle-class, ask whether they are mostly men or women, children or grown-ups, American or British, etc. Are they married? Where do they work and how much exactly they are paid?
Synthetical – it’s all about simple engineering, not the reverse one. For conducting a research process and report writing, a data analyst must be able to match the pieces of various structures, sets of data, and themes. In addition, it’s about being able to develop a logical story out of them.
Can interpret the statistics any time – now this feature involves being aware of at least the data gathering process and related procedures. It should be all clear before running right into the analysis. The background is important to consider before proposing any solutions or alternatives.
Edified in the methods of data analysis – well, you study and absorb new knowledge as you keep on working on new sets and projects.
Besides, do you love exploring? What about your sincere curiosity and attention to details?
You don’t definitely have to yawn at your workplace. If you miss a thing, everything may turn upside down for the entire organization. It’s a huge responsibility. So, if you cannot multiply three-digit numbers in your head, don’t even think of this job – go and cry for your mama!
I think this list point to certain key elements/benchmark of what it means to be qualified researcher and forecaster. Is there anything else you can think of while speaking about the top attributes of the professional data analyst? If you do, perhaps it is your calling.