Every new hire imagines stepping into a new job as an opportunity to bring his or her area of expertise to bear on a key problem for the company. The reality for new hires of analytics-related roles — or truly, any role — is they'll use multiple skills, not just their area of specialty, to make analytics useful for the hiring organization.
The skills required to work with data and analytics solutions are easy to identify. For example, an organization relies on Adobe Analytics — thus HR will list that solution as a desired skill in potential hires. But the world is awash with analytics software, from packaged solutions to open source programming. Moreover no one person will embody all the technical skills required for analytics. Management is starting to recognize that maintaining analytic advantages require constant training on specific solutions.
Hiring managers are starting to shift their attention to other skills professionals should have. With that in mind, what other skills would be useful when working with analytics solutions?
4 'Soft Skills' Any Analyst Needs
One skill is curiosity. Curiosity fuels the work ethic needed to investigate metrics and data beyond face value. For example, curiosity can drive an analyst to question if outliers in a dataset make sense or if anomalies deserve further inspection. Analysts must be curious to identify the right next steps that lead to a model or to refine an analytic report.
Curiosity connects to a second skill, discernment. Discernment is the ability to know when data and information associated with that data can be used in an immediate analysis. Good discernment will leverage technical skills to a degree, but it is also deciding how problem solving based on those skills best serves a given dataset and business operation. The skills for applying data to prove a concept is often different than those needed for a production activity. Discernment also plays a hand in good time management, deciding what tasks with the given data and analytic solution can be best addressed and which ones will take time to develop. This is critical in deciding how to best approach regressions and machine learning.
Another skill, creativity, is a close cousin of curiosity. Creativity discovers new ways to gain insights from data visualizations or an analysis, and then crafts next steps to make that vision a reality. I once overheard a conference presenter say that an analytics professional can be a "jack of all trades, master of one.” Creativity combines those diverse trades to inspire working with teams to deliver data needed for an analysis or for establishing the story behind data visualizations.
Finally communication is an obvious skill any analyst must have. Communication is at the heart of storytelling and is key for imparting the highlights of an analysis or data visualization. But analysts should communicate with adaptability and empathy in mind. Data is increasingly appearing as a stream of information, impacting models and assumptions. That implies that no context associated with data is set in stone. Analysts must be skilled at adapting their explanation of analytic results to these contexts, as well as speaking to the interests of decision-makers. Analysts must consider the mediums in which they communicate explanations and updates, such as a dedicated Slack channel to share questions among remote stakeholders. A medium broadens or limits the ability of people to receive messages.
The savviness of the stakeholders must also be considered when choosing a communication medium. A good question to ask is, “How familiar are they with the platforms from which the data is being generated?” The answer to this question can impact what responses are possible or even how the reporting is interpreted. I recall two assignments where I explained how Twitter works to managers because the metrics were included in their analytics dashboards. In those instances I showed the features that seemed most relevant to their usage and explained how communication on the platform relates to the metrics being measured. In short I adapted my explanations to their needs, and showed empathy by hearing their concerns and tying the response to their knowledge.
Related Article: How to Build a Successful Data Analytics Team
Technical Skills Alone Won't Translate
Managers must be ready to seek analysts who have diverse skillsets, rather than overemphasizing technical skills. A professional with a well-rounded skillset will bring more value — sharing new, relevant perspectives on a given analysis and inspiring cooperation from teams impacted by the analysis. Introducing a variety of perspectives can be especially important when considering the impact of diversity and inclusion on customer services supported by machine learning analysis.
The person with the right combination of skills will get more accomplished, and help set the right tone that will build the most competitive team possible.
Learn how you can join our contributor community.