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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: its also about empowering your business by creating a culture of data-driven decision-making.

Indeed, as Hal Varian, Googles 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 thats 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 theyre 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, Ive compiled the guide below using 16 FAQs I often face when helping businesses work through their data challenges.

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.

Without data youre 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.

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:

A fundamental part of building an effective DS team is to set out how youre going to measure success. This is where critical business KPIs come into play! Its 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.

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, Im always most concerned with the impact a project has, rather than the accuracy, quality or performance of the model in a project.

Id 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.

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:

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:

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 theres 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:

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, its worth checking out Googles numerous blogs on fairness.

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.

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.

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:

When thinking about building a team, its 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 thats a whole other guide! But what common skills/traits do I look for within any team member?

Some others to consider also include:

When working, especially in a smaller business, you will spend a large amount of time with that person, its 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, its 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 wont work well and vice versa.

Typically, Ill split this into two parts:

Here, Im 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?

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?

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:

Data science is a fast-moving field, and many data scientists feel left behind at work if not continuously developing and learning. Set aside regular time for the team to discuss and pursue development opportunities, it can be as simple as setting some time aside every Friday for members to pursue something extracurricular.

One critical thing I have experienced is that a lot of teams have training budgets to allow for courses but do not set aside time for the team members to train in those learned skills. Allow your team time to hone these skills, in addition to paying for attending courses.

Also, feedback is a two-way street. Allow your team to be able to give you feedback, too, so they can inform you how best to manage them and get the best out of them. The one point I never change, however, is where I give this feedback, its always in private, and its always constructive.

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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