In the world of analytics, all practitioners are not created equal. There are drastic differences between analytics experts and those who dabble. Identifying the differences between the two is vital to the success of your business and that of your clients.
It can often be challenging to differentiate between those who truly know what they are doing and those who know the terminology but lack the ingenuity that analytics requires. That is especially true when it comes to identifying strong candidates and when selecting consulting partners. The three key characteristics to look for when assessing analytics partners are at once seemingly obvious and not often found together. They are curiosity, humility, and a tinge of healthy skepticism. Each is important, but when combined they can become an unstoppable force for your analytics–and other–practice.
Analytics experts need to be curious and knowledgeable
Curiosity may have killed the cat, but it also enabled man to walk on the moon. In the world of analytics, curiosity is necessary to garner the insight needed to inform business strategy. When putting together any analysis, it’s always necessary to ask two key questions: Why? And how? Understanding why something works and how it works are what makes an expert in any field.
For example, the best mechanics understand how each element of a car functions, how those parts work together, and why they are needed for the car to run smoothly. Analytics is the same, but instead of car parts they have data, software, technologies, and systems.
While many people may have a basic knowledge of how to use something, understanding why it works is what separates the experts from the pack. It’s easy to show someone how to do a task, but the minute something unexpected happens, the ability to adapt and troubleshoot is crucial.
The power of the innately skeptical
Someone who understands the inner workings—the why—can immediately grasp what’s causing the issue, or at least know where to start investigating. That expertise unlocks efficient and effective troubleshooting with proper tool selection and combinations. When the strengths and weaknesses of a tool are understood, analytics experts can select and combine them to achieve the optimal solution.
A major pitfall for many analysts is that they are too trusting of their own results. Analysts must question the validity of their data or insights through a discerning lens. For example, an analyst could pull and look closely at a customer’s record to determine if the data points make sense when pieced together, which could help identify potentially invalid results from a sample set. Using the simple technique of trying to tell a story with the data can be helpful in discovering coding bugs and faulty assumptions—preventing catastrophic loss of time and bad project deliveries.
This not only prevents bad analysis but can also unlock key insights. When an analyst stumbles across something that doesn’t make sense and then spends the time to figure out why, they are able to uncover hidden insights that would otherwise have been missed. Insights are often like wild animals—they don’t want to be found by humans. You don’t walk out your front door and get to witness breathtaking scenes of wildlife interaction. For those who seek this, you must search for it and be patient. The same is true for business insights. Any time an analyst is looking at a result in analysis and mutters the words “this doesn’t make sense,” and then takes the time to investigate, you can be sure that they are about to find out something worthwhile.
The need for humility in analytics
When you think you have nothing else to learn, you are setting yourself up to fail. A major pitfall that many analysts experience is becoming an evangelist for a single tool and being unwilling to learn anything new. Becoming an expert in a single tool isn’t by itself a negative thing, but that expertise needs to be tempered by the understanding that a single tool cannot be designed to do everything perfectly. All tools have limitations, and a single-tool approach will become a detriment, regardless of how well you can use it. Being overly committed to one tool can also lead to designing solutions that are overly complex or inefficient because that particular tool likely wasn’t designed to handle every use case, even if you can manage to make it work.
In analytics, humility springs from two primary beliefs: that a single tool is inefficient to accomplish all tasks; and that you don’t know everything you could know. A beautiful aspect of the analytics space is that there are constant advancements in technology, understanding, and application. Being humble enables analysts to have a passion for continuous learning and embracing new things. This doesn’t mean new technologies should always be adopted and implemented, but rather that being open to considering viable alternatives leaves room for the agility needed to address each use case with the best-fitted tool.
Humility enables one to learn new things, but this new knowledge needs to be put into perspective with what you already know. Analytics wisdom becomes paramount in evaluating how each new tool or insight can be used, and in what way it will be most effectively applied. Sometimes a new technology doesn’t fit within the scope of what the business is trying to do or is too costly to be realistic. Other times, only certain components are needed to enhance the current solution. Being open to the possibilities will help ensure more positive outcomes.
Together, these key traits help mold curious, capable, and forward-thinking analytics practitioners who are better equipped to solve business and client challenges. Inquisitive and humble minds not only enable better, more creative, and impactful solutions, but also help lay the groundwork for more agile teams and a more resilient culture.