Democratizing data science.
While HBR declared "Data Scientist" the sexiest job of the 21st century, let's admit that the prevailing view is that it's a geeky, highly-technical field.
Psychologists know that humans are categorical—we use categories to understand the world more easily—and this tendency to categorize has caused us to separate "data scientists" and "business-people."
For example, aspiring data scientists often imagine their work to involve scripting in Jupyter Notebooks, creating isolated analyses, and building models, rather than seeing their work as fully interdependent with the rest of the organization.
By adopting a more business-focused mindset, data scientists can increase their odds of being promoted, and more importantly, add more value to their organizations.
Similarly, business-oriented professionals can adopt a more data-driven mindset, even if they're non-technical, using no-code analytics tools like Apteo.
In a nutshell, strategic thinking helps to determine what tasks are valuable for you, for the company, and for other employees to spend their time on.
That's a far different approach from just expecting to be given data science tasks that are valuable. Instead, the onus is on you to take initiative and find value.
Strategic thinking can be rocket fuel for your data science career, so let's dive in.
Strategic thinking is the polar opposite of the default mode of thinking.
We grow up learning appropriate and expected behaviors from our families, our schools, our religions, and the media we consume. This process of socialization is a useful and necessary human feature to make us functional members of society.
However, being told what to do and how to think our whole lives makes it difficult to rewire ourselves in our careers.
Without the capacity to think for yourself, your career as a data scientist will naturally stagnate, as you won't find ways to add organizational value yourself.
This makes strategic thinking a critical, life-long skill.
One effective way to cultivate strategic thinking is by constantly questioning assumptions.
Most organizations have many ingrained assumptions—things that make employees say "that's just the way we do it here." Even if they're not verbalized as such, some ingrained assumptions may include:
By observing and uncovering false assumptions about data and data science, you can find opportunities to improve.
Ideally, you'll do this upon entering the organization, while you still have a fresh perspective. In any case, finding creative solutions and uses for data science requires that you take the time to reflect, giving yourself mental space, instead of continuously jumping from task to task.
We all know that businesses need a strategy. They need a business plan for everything from their offerings, to expansion plans, to the budget.
However, strategic thinking is needed for teams and individuals as well, who are the ones that implement the corporate-level strategy.
Further, you want to align your strategy as a data scientist and the projects you work on with the organization's strategy.
You might be interested in a cutting-edge application of AI, while your organization just needs a decision tree to identify high-value customers.
By aligning with corporate objectives, your work will get noticed, positioning you for promotions and a better career overall.
Putting yourself in the shoes of your employer by considering the best business-case for choosing an AI project, for instance, will give you a leg-up over data scientists waiting to be told what to work on.
Finally, you should apply strategic thinking to your own data science career, by asking yourself where you want to be in five years.
There are so many opportunities for data scientists, with hundreds of companies hiring in different fields and a shortage of tens of thousands of analytics professionals, that you need goals and a strategic view for your career.
"If you don't know where you're going, any road will get you there."
Practically, strategic thinking has a direction: From top to bottom.
Strategy starts with the long-term big picture vision, and trickles down to the exact details of how that vision will be accomplished.
For instance, your vision may be to make your subscription business truly data-driven, while details may include identifying what attributes impact customer spend, and implementing churn analysis to reveal and prevent customers from leaving.
The key to strategic thinking is clarity in choosing what you're going to spend your time on, and what you don't have time for.
Perhaps your business is spending too much time on reporting, so you plan to automate reporting and free up time for working on churn analysis.
Maybe you need buy-in from your boss before you can work on a new project, so you need to show a quick win. You might use a simple analytics tool that lets you do churn analysis in minutes, getting support from your employer.
Whatever your broad KPIs and goals are, getting clear will let you know if you're making progress, or if you're stagnating. In the same way that you need goals for your own career, you need goals for your organization.
Another piece to keep in mind is tactics. Tactics are specific actions that help you accomplish your goal, which could be things like setting up a weekly call that aligns your stakeholders on your data science project, blocking out time in your calendar to consider areas of opportunity for data science, and so on.
If you want to make your organization more data-driven, maybe the tactics would involve introducing each employee to no-code analytics that they can use, regardless of their level of technical skill.
Data scientists, like most people in tech, are typically very logical and rational. In other words, they want to see that input "A" leads to output "B."
However, strategic thinking isn't so clearly defined. Nonetheless, it's a valuable exercise to set aside time for strategic thinking, as creative thoughts don't pop up when you're in the weeds, working on a project, but rather when you're thinking at a high-level.
Instead of making strategic thinking an afterthought, it should be a central part of your day, your job, and your career as a data scientist.
The more time you spend on strategic thinking, the more clarity and direction you'll have in your work. Of course, thinking and execution have to be balanced, but unstrategic execution, not over-strategizing, is typically the problem.
Practically, here are a few tactics that aid in strategic thinking:
Strategic data scientists aren't just concerned with their day-to-day tasks, the quarter, or even the year. They look ahead at trends that may impact the organization down the line.
Here are just some trends data scientists should keep in mind:
Accessibility & No-code
No-code analytics tools are emerging that make the field a lot more accessible, and thus make AI even more ubiquitous.
Data privacy will continue being a hot topic, with more states introducing data production laws, while calls for a federal data privacy law are getting louder and louder.
Since AI is fueled by data, if access to training data becomes more restrictive, you’ll need more creative solutions to stay competitive.
Ethics on the fore-front
The rise of various social movements has put the spotlight on discrimination around the world.
As a result, biased AI algorithms won’t go unnoticed, and the negative ramifications of a biased algorithm will break businesses.
AI models, like the text-generation GPT-3 algorithm, are becoming incredibly accurate, and meeting or even surpassing human-level accuracy.
As most companies faces problems related to the coronavirus, we’re seeing new use-cases for AI and data science emerge.
As this WEF article reports, AI is being used in creative ways to fight COVID-19, including NLP to crunch research data, track the spread of the virus, and detect clusters of symptoms.
These are just some of many trends impacting the industry. Most people don't bother seeking out this big-picture information, but are then surprised when disruption happens.
By staying on top of trends, you won't be surprised, but actually ahead of others in the field, with a capacity to immediately find ways to add value to your organization.
Earlier, we looked at how our tendency to categorize has made us separate "data scientists" and "business-people."
However, as we've seen, strategic thinking is such a critical and valuable skill that all data scientists should cultivate it.
Taking small steps today, from brainstorming strategies to blocking out time in your calendar, will put you on the road to being a strategic data scientist.