Advice & FAQs from Founders Factory data scientist Ali Kokaz.
Search data science online, and you will find an unending trove of technical tutorials and articles, ranging from how to ingest spreadsheet data, to building a multilayer perceptron for image recognition. However, data science is much more than simply building a complex algorithm: it's also about empowering your business by creating a culture of data-driven decision-making.
Indeed, as Hal Varian, Google’s chief economist, said back in 2009: “The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”
Today, speak to any business leader and nearly all will say that data science is a critical focus for their organization. Yet the reality is they’re struggling -- recent research shows many firms are unfit for data, for a myriad of reasons including organizational capability, lack of talent, poor quality data and collection processes, to name a few.
So what does it take to build a truly effective data science function?
From understanding what it means to be a "data-driven" organization, to conducting successful data science projects, I’ve compiled the guide below using 16 FAQs I often face when helping businesses work through their data challenges.
1. Why should data science be a priority?
As Tim Berners-Lee, inventor of the World Wide Web once said: “Data is a precious thing and will last longer than the systems themselves.”
In a nutshell, data science is the process and ability to turn raw data into information and insights to inform your business decisions. Without it, you are making decisions blind, or based on opinions and assumptions, rather than facts.
Data science can also be used to help identify opportunities, meaning you can find extra user growth, or revenue streams, by understanding your customers and markets more deeply. You can also use data science to help automate or reduce the overhead of certain processes, like evaluating and processing loan applications for a challenger bank, meaning you can cut costs and set the business up to scale.
This is largely the reason why companies are now pouring money into their data storage, analytics and science capabilities to improve operations and decision-making. It is no surprise that some of the biggest winners of the last decade were essentially data companies, like Google or Facebook, as well as less specialized examples like ASOS, who heavily optimize their shopping experience through data. Essentially, those that fail to invest in this area will quickly be left behind.
2. What are the foundations of a data-driven organization?
“Without data you’re just another person with an opinion,” were the wise words of famous statistician W. Edwards Deming, which gets to the crux of what data-driven organizations are.
A data-driven organization is one that uses data to drive business decisions and processes, meaning they are informed when making choices, and decide things in a factual manner, rather than simply based on opinions and anecdotes.
For example, at my previous workplace -- a leading data management consultancy -- business decisions that needed to be made had to be backed up with data evidence, with projects prioritized based on data around how much impact they will have. That type of informed decision-making was pivotal, meaning we were so much more well-informed before undertaking work.
Creating a data-driven organization requires two foundations:
A major factor underlying these foundations is consistent vocabulary, terminology and semantics across the organization, and stressed importance on why good data is vital for this to work -- this is so that employees collect and store data properly rather than seeing it as another chore on their to-do list.
3. How can businesses align their data science function with high-level organizational goals?
This is pivotal to the success of a data department within any organization. There are a few steps I take within my department to ensure this happens:
4. What does good look like? Measuring the success of your data science team
A fundamental part of building an effective DS team is to set out how you’re going to measure success. This is where critical business KPIs come into play! It’s always important to make sure you measure the success of the data team directly in relation to business goals. For example, this could be the number of customers gained through data science projects or time saved through automation.
You could also measure the interaction of the business with the data outputs as a measure of success. For instance, how many people are using the dashboards and reports the team has built? What decisions are being made off the back of them?
Typically, part of the project-definition process is defining success criteria. When these are hit, a project can be seen as achieving its targets; hence using these as KPIs can also be helpful.
5. “A good DS project is one that produces the best quality product in the least amount of time and continues to yield sustainable results.” Is this true?
In many aspects, this statement makes a lot of sense. However, a good data science project to me is one that produces the biggest impact on the business, in the shortest amount of time, and continues to drive business impact moving forward.
Working with various businesses, I’m always most concerned with the impact a project has, rather than the accuracy, quality or performance of the model in a project.
I’d also like to caveat that with the fact that fastest is not always best. Taking slightly longer with a project to future-proof or productionize more efficiently can pay off more in the longer term.
6. What questions should I ask before starting a successful DS project?
As companies collect ever more data about their customers and their product usage behaviors, a rising challenge facing many businesses is how to analyze this data to derive useful insights.
Before undertaking any project, I always start with the questions below to inform planning and objectives:
7. Businesses often include ever-changing teams and projects unfamiliar with data science. Why is it important to establish a shared data science vocabulary?
I cannot overstate the importance of this! When I work with startups, one of my first tasks is aligning on terminology, but it should be established for any team for the following reasons:
8. Do you have a typical workflow you’d recommend for teams to use when approaching data science projects?
A well-defined workflow for data science applications is a great way to ensure that various teams in the organization remain in sync, which helps to avoid potential delays, financial loss, and especially projects going sideways without conclusive success or failure.
There are several suggested workflows currently in circulation, with many building on existing frameworks in other data fields, such as data mining. While there’s no one-size-fits-all solution to all data science projects, often components depend on the company and team objectives. In my experience, there are certain steps that should be ubiquitous in all data science teams, accompanied by common approaches. These include:
10. What are some of the ethical design challenges organizations face when building data products?
Data science and related fields of AI and machine learning are challenging assumptions upon which societies are built. The more data a business collects, the more powerful the organization is relative to the individuals. As a result, this presents a number of ethical challenges to be aware of when building data products, which include:
For further reading, it’s worth checking out Google’s numerous blogs on fairness.
11. Is it ever permissible to collect personally-identifiable data about people?
This really depends on the use case, but the majority of the time, no. Data for insights is only useful in sensible aggregation, and not on a personal level. Usually, a middle ground is reached where some PII is collected that has been agreed is useful (such as address) but not all.
12. How should I manage the tradeoff between democratizing access to all data (for insights) and securing trust with customers by limiting access to their personal (sensitive) information?
First and foremost, you should securely store the sensitive data separately and limit access to this through correct permissioning and requesting. The remaining informative data can be open, with identifying data being anonymized (using a random user_id, for example). You could also impose transparency of what the data is being used for, ensuring data is only used for the reasons stated by stakeholders or the business.
Other things you can do include policies to limit accessibility, by setting minimum granularity on dashboards, for example. You can revisit these policies regularly as the business grows.
13. What considerations are important when scaling a data science function?
Scaling a data science team effectively is more than just hiring great people. In my experience, there are multiple areas and things you need to consider and maybe alter, including:
14. When building a data science team, what are the most important skills and behavioral traits to consider?
When thinking about building a team, it’s vitally important to think about the overall skillset of the team, rather than simply what each team member brings individually. There are multiple methods and approaches you can use to define what the team needs to look like, but that’s a whole other guide! But what common skills/traits do I look for within any team member?
Some others to consider also include:
15. When recruiting data scientists, how can I assess core competencies like organizational fit, technical depth, and communication skills?
Organizational fit
When working, especially in a smaller business, you will spend a large amount of time with that person, it’s important to try and understand whether that individual will fit in with the rest of the team, but also if they will enjoy working there. I usually do this in the form of two chats -- one at the start of the recruiting process and one at the end.
The reason for splitting into two is I want to see how the candidate behaves around new people, and then how they perform in front of someone they are now more comfortable with. Does their attitude change? Now they are more comfortable at the end of the process, it’s a chance to see if they are naturally more introverted/extroverted. Does their professionalism change?
My questions also revolve around previous experience -- how did they act with previous colleagues? What do they say about previous employers? What did they enjoy? What did they not enjoy?
I also use this as an opportunity to understand more about their aspirations -- where do they want to be? What do they want to develop? What do they look for in a role?
For culture fit, I try to involve at least one other member from the team to see how they get on. An important point here is you need to find someone right for the team, an introvert in an extroverted team won’t work well and vice versa.
Technical depth
Typically, I’ll split this into two parts:
Here, I’m looking at how they approach a problem, hence a time-limited exercise means they cannot create the most complex solution, so they will have to make decisions on what to simplify. How do they assess these trade-offs? How do they communicate them? Do they identify and communicate caveats? How do they link the problem to the business? Do they try to understand the impact of the outcomes?
If I need to drill further into technical ability, I use this as an opportunity to discuss what they would have done if they had more time. What do they know about a specific topic? How in-depth is their knowledge?
Communication skills
I am assessing this throughout the whole interview process, especially through the take-home task stage. How do they present their work? What medium do they use? Do they cover all aspects of a project or a problem? Can they describe complex concepts clearly? In a non-technical way? Do they listen intently to my questions? Do they take time to think about an answer? Do they try to clarify questions?
I usually also reserve a few questions about how they got on with their teams and previous presentations and how did they build rapport with the business? How much contact did they have? Ask them to talk me through a good presentation they had.
Another aspect to pay close attention to is cues in their emails. How are they worded? Short? Long? Full of grammar/spelling mistakes? How formal?
16. As retaining data science talent becomes harder than ever in this competitive talent market, how can businesses help their data scientists navigate, grow and develop their careers?
This is a complex one, and will vary massively from one individual to the next, but managers still have a huge role to play in keeping staff happy. This is especially important in an area like data science, where employee churn is high, and roles are always available for superstar individuals. From my experience, there are a few areas I think about in terms of team retention:
Investing in a powerful data engine
As data science becomes an increasingly integral part of any business, navigating the evolving complexities of creating a powerful data engine has never been harder. Yet, shining a light on the common challenges faced by many firms shows that "good data science" requires a laser-sharp focus on fundamental data principles and ethics, and building a data-driven culture. Those businesses willing to invest the time and resources to become a truly "data-driven" organization will be positioning themselves for success in the years ahead.
Ali Kokaz is a data scientist at Founders Factory.
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