Five Soft Skills Of Success For A Data Professional

Experts Column

Five Soft Skills Of Success For A Data Professional

With over 20+ years of Technology Leadership experience in Healthcare and Finance industries, Deval is a Petite Leader, Mother and Runner who uses Tough Love to bring the best out of herself, her teams and business.

Data and Data backed analytics, including AI and Machine Learning, are experiencing exponential growth. Businesses are deemed conservative if they have not yet started embracing new trends with data-supported Analytics and Machine Learning. Executives encounter rising pressure from competitors to differentiate based on these contemporary trends. According to LinkedIn's emerging jobs report of 2020, the number of 'data scientists' jobs increased by 37 percent over the last three years. Harvard Business Review proclaimed data science as the `hottest job of the 21st century'. This trend has also revealed a dire need for a robust infrastructure built over Cloud and Big Data platforms, causing a corresponding growth in jobs in these related areas.

With so many related job profiles and needs, the natural query that comes to mind is, `Who are these people?'. Job descriptions are vivid and vast. We expect Data Scientists to be Data Engineers, Data Engineers to be Data Analysts, and Data Analysts to be Data Scientists. Feedback often received is that we not only need them to be technical but also business savvy. As a leader trying to keep pace with the business while building quality data technologies to scale, I frequently wonder what to anticipate out of all our different but similar `Data Professionals'. The similarities that come to mind are not simply about being `business savvy' but are driven by a set of soft skills that come to mind.

Here I Have Tried To List A Few Soft Skills That I Look For In Every Data Professional, No Matter The Job Title:

A HIIT Mode Of Thinking & Acting

HIIT (High-Intensity Interval Training) is an exercise form where one peaks the heart rate to 80 percent of the maximum rate for 20 seconds and then brings it down to 60 percent of the average heart rate for 10 seconds while repeating this throughout the duration of the exercise. It is essential to keep a tab on the heart rate not to overdo anything. HIIT produces incredible endurance in the body and clears up the mind.

 There are numerous occasions when Data Professionals experience the need to peak their thinking to 10000 ft to understand the goal of their data exercise and then bring it down to 10 ft within data while SQL'ing  their sets. At this point, it is essential to be able to `see-through' your goal on your own once it is defined for you, similarly to how you `see-through' your heart rate in a HIIT session. Thinking high level and acting at a low level should become muscle memory. The most crucial aspect here is that this thinking at 10000 ft and working at 10 ft is best done by the same person. Repeatedly, we see failed use cases where Data Scientists rely on Data Engineers even for exploratory data moves. A HIIT mode of thinking and acting will ensure success and pace.

Comfort In Ambiguity

There is seldom a Data Set that will give perfect results. The size of the data does not matter for most simple use cases. Many business processes produce incomplete or inaccurate data and require making assumptions or cleaning it up before it can be helpful. Every Data Professional must understand the need to clean the data quickly and then make a few valid assumptions in conjunction with business and move on with analyses instead of going into analysis paralysis.

Dealing with business data is more `art' than science. Selecting data sets is an art as well. If you are doing Location Analytics, ask yourself - do you need to see perfect zip codes, or is the county name better? What is the grain of your data set that can sustain the assumptions you are making and answer your analysis's goal? Can you live with a few thousand rows with county names, or do you need a million rows with an exact address? I say it depends!

Resident Consultancy

From Leadership to Analysts, every Data Professional should be a `Consultant' in their minds, even if they are full-time employees. Consultants are results-oriented and savvy opportunists. For Data use cases, business leaders are not always available, hence seeking unexpected opportunities to meet, working top-down, working bottom-up ­ all sorts of adoption strategies should be adopted. These should not be limited to Data Leaders alone.

“As a leader trying to keep pace with the business while building quality data technologies to scale, i frequently wonder what to anticipate out of all our different but similar `data professionals'”

 The entire Data department should be on the lookout for opportunities to find new avenues to make that one small incremental outcome and continue their storytelling. A significant difference between real consultants and resident consultants is that the latter are true subject matter experts. With that skill, they can present without preparing for months together. They have the power to continue their storytelling and always tie threads.

Graceful & Curious Interrogation

Working with complex data from complex business events and operations could mean continuous clarifications and deeper inspections. Compared to App development, where requirements are usually robust, Data professionals often face the need to continue asking follow-up and curious questions to build a powerful story or trusted Data Science model. It can be tricky to keep everyone interested while asking. Here are a few tips –

 · Always good to have access to apps generating the data you are analyzing.

· Support your questions with your preliminary findings.

Persistence Over Perfection

 No Data Science models are perfect; some are better than others. No visuals tell the whole story initially, but they tell better stories over time. Seeking perfection is a downhill slope and one that stops innovation for Data. Walking out of presentations that lead to curiosity and many questions is always better than walking out with no questions but lots of applause! Persist on your story, persist in making your model better over time, and incremental results will be more action-packed and perfect!