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6 Essential Traits for Data Analysts
6 Essential Traits for Data Analysts

June 3, 2021

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Data Analysts top the list of top 20 roles increasing in demand across industries, as per World Economic Forum’s Future of Jobs 2020 report. A lot of job postings in the market today for data analysts focus on the mundane that Data analysts “think” is essential: Excel, Power point, Problem Solving, Answering business questions, Data Analysis.

The reality is quite different in organizations. Business leaders consistently cite difficulties when hiring for Data Analysts, cites the same WEF report.

Here I list down some essentials traits, things that I think and believe a business analyst/product manager or anyone who wants to make sense with data they are working with, should keep in mind, and lean towards

1.Be a Data literate

Data literacy is an essential aspect of an analyst that is mot conveniently ignored. Having a basic understanding of the domain that generates the data makes it easy to have an understanding of the data. Research business scenarios that are behind your dataset, and understand what scenario generates what kind of data.

Are there missing values in a column for a reason? Is there incomplete data for a certain period? Having previous knowledge of the domain and the scenario that generates that data helps in bridging the gaps, and generating better insights.

2. Profile your Data

Someone who keeps their data at arms length might become a competent modeler or engineer or statistician, but there is no substitute for diving into the data if you want to be a good data analyst. 

Before you dig into your analysis, profile the dataset at hand. Spend time looking at the raw numbers and strings, ask yourself what those numbers and strings mean in the real world scenario and how they were gathered.

Go a step further and try to find no-so-obvious relationships between your variables/columns. Sometimes, the most obvious insights often stand out during profiling. The willingness to become intimately familiar with the data can be the difference between an ordinary and a great analysis.

3.Ask, Collaborate

Real world data that companies generate is nuanced, complex and ever changing. A rookie mistake most data people do is assume things in the data and move with analysis. Sure there are timelines to keep and bosses to keep happy, but having a coffee with a subject matter expert, or just an email to the next department asking questions around your data will help get some context.

Context helps in generating some mental images of the scenario, which then the subsequent data profiling can corroborate . Collaboration with the expert in the domain helps in understanding issues with measured data such as encodings, units, keys and labels

4.Go above & beyond the obvious

Having a larger picture of data always adds that extra sauce to your analysis. Most analysts make the mistake of burying their head in their own data, and they sometimes miss insights what the data surrounding it might be able to offer, and which might be significant in their analysis.

If you are a sales analyst, and there is price list information from Finance that is corelated to your analysis, and not part of your dataset, you would want to include that as a part of your analysis. For this, the first step is to identify that correlation, understand the source of that data and make that connection between the two datasets. Asking questions and collaborations with experts that we talked about previously helps in getting the birds eye view of the data or the processes as well. Have the guts, the intention, the creativity and the motivation to try something different.

5.Own up or Eliminate Inherited Muda

Most analysts in a new job, in a new role inherit lot of reports and analysis that get passed down from generations of other analysts who themselves have moved to new roles or have moved out of the company. Many times, there is not a proper handover with documentation around assumptions made, scenarios analyzed or worse, filters in the reports. These reports are most of the times not questioned by the one who inherits them, because they want to keep the light running for the sake of ‘business continuity’. This is another sinkhole and often causes Muda or waste within an organization and analysis.

As an analyst, be responsible and own up the report or the analysis that comes from you. Don’t fall into the trap of just pressing “Refresh” on a tool without understanding what you are refreshing. Don’t be a report runner. Question the status quo. Ask why this report is required or why an analysis is needed at a certain frequency? Can you eliminate it? Can you make it better, accurate and more useful? These are questions you need to be asking yourself and the recipients/stakeholders.

Just because a recipient used to get a report earlier doesn’t mean he/she need to still get it today, especially if they don’t own up to the results of that analysis or worse, don’t even look at it.

Inheritance also sometimes brings in bias in the analysis or the report, which if not properly communicated during the handover, can lead to disastrous results. Ensure that you understand and if possible get rid of bias from previous analysis, and do your own analysis in order to be comfortable with the numbers or the insights yourself, so that you can explain them when questioned.

6.Stop blaming the tool

So the saying goes, a bad workman always blames his tools. Lot of analysts tend to blame the tools that they work with, for not being efficient in their roles and jobs. “Oh the report is so slow”, “I wish there was this feature that makes It easy”. “ I can’t wait all day with this system”. “We need better tools”. They often don’t realize that a tool is just a means to the analysis, to the report. Sure, when it comes to automation or better efficiency, start of the art reporting or BI tools make the job of an analyst easier. However, no traditional BI tool can replace an analyst’s job. Analysts should not use tool as an excuse for their inefficiency in doing his/her job.

Analysts need to have a proper command of tools, and more importantly, need to keep themselves updated with changing environments. Just because they are well versed with Excel, they cannot expect a Data and Analytics team to setup the start of the art tool at their company to generate a feature to dump the data the excel. New age data analysts need to have at-least one tool in their arsenal that they are proficient with, and can do one or many of the important aspects of data analysis, which are collection, profiling, organization, interpretation, visualization. SQL is a must have skill set, in my opinion.

Employers significantly vary in their understanding/requirement of data analyst skillsets, but that that shouldn’t stop an analyst from skilling and reskilling in tools. Having a leg on understanding the technical side of data helps an analyst to better articulate requirements to the technical teams, and also understand and appreciate the complexity involved in setting up these systems.

A good data analyst does not use the support of data to tell a story she/he think is, but rather tell the story that the data reveals. Along with core competencies such as critical thinking and problem-solving, if analysts can inculcate and/or improve upon the above mentioned traits, I strongly believe it will help them to be successful and an invaluable resource for their teams and organizations.

Snehith Allamraju

Photo by Kaleidico on Unsplash


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