This article is written for CTOs who are looking for a way to implement digital transformation to their business. The Big Data market is always changing and evolving, therefore there are many contradicting opinions on how building a Data Science Team should be approached. Some sources claim one is no longer needed in 2021, others suggest you only need data scientists to run the ship. Based on our expertise and years of experience, we have compiled a guide to clear it out and advise on the best course of action.
Allow us to preface this by saying that two above mentioned opinions are not right. First of all, 2021 is the year when a team of data specialists is an absolute necessity as we are receiving enormous volumes of data every day and they are steadily increasing. Secondly, the belief that a data scientist is all that’s needed for successful digital transformation is very outdated and has proven to be damaging to businesses. Although there are projects that will require a team structure dominated by data scientists, there would still be other roles to fill.
With that said, we will skip any further introduction and dive straight into trying to answer the burning question: how to build the most effective data science team in 2021.
In the past years businesses have steadily begun recognising the impact of digital transformation. Not only is it capable of aiding in achieving long-term business goals, but also improving how the company is run by optimising internal processes and practices. Adoption of automation allows companies to relay time consuming manual labour on artificial intelligence, minimising time spent by eliminating inefficient practices can increase revenue, and overall, digital transformation seems like it can deliver nothing but success.
And this is not incorrect. However, there is one condition to be met: it has to be data-driven. Trying to implement digital transformation enabling changes based on assumptions are bound to fail and result in wasted budgets and no viable results. Before even getting on the route to digital transformation it’s important to grasp the concept of it being a data-driven mindset before a technical process. In other words, unless your entire organisation is capable of putting data before anything else, this may not be a good time to start the digital transformation journey.
If you are unsure where you stand in terms of adopting this approach, we highly recommend using our free tool to evaluate your digital transformation readiness.
As we have mentioned in the introduction, the times when data scientists would be seen as the only people required to handle all the data-related concerns have passed. Undoubtedly, they are one of the key players, but their skill set does not cover the entirety of requirements of a successful analytics project. For example, while a data scientist could build sophisticated systems and collect heaps of valuable data, they would not be able to present it in a way that would make sense to non-data savvy users. Doing that is crucial to ensure data findings are understood and actioned upon accordingly, otherwise this is a waste of time, budget and effort.
There are also other roles to fill, such as a machine learning engineer or data analyst. Although these roles are similar to data scientists, they’re not the same and deliver different skills to the project. Most importantly, these skills are not competing with one another: they complement each other and deliver best results.
A successful data science team not only collects and processes data, but also delivers it to the rest of the organisation. Explaining the insights collected and painting a bigger picture based on them reveals business opportunities that would otherwise not be spotted and allows us to identify potential threats that can then be addressed prior to them causing issues.
A functional data science team enables data-driven business decision making that not only entails business growth but also further insights and optimisation. All in all, working with a data science team enables digital transformation by providing clean data to action upon and constant stream of areas to be improved upon, as well as clear forecasts in regards to threats and opportunities.
Going back to a successful team structure that brings a variety of skill sets to the table, it’s worth mentioning that there is no one-for-all data science team structure. Simply put, every business has different goals, mission statements and operates within different industries. For example, companies that specialise in building data products will require bigger input from data scientists and machine learning engineers as their technical skills are to become the core of the project. If we are talking about a new player on the ecommerce market, for example, the roles of the data translator will be more important to understand customer behaviour patterns and how to address them correctly.
In other words, the structure of the data science team largely depends on goals your business is pursuing. However, there are key roles you must aim to fill, regardless of the focus of size of the team:
Understanbly, since each role comes with a unique set of skills, you can’t apply the same standards for each. In data science, look for someone with programming skills and statistics knowledge. A data analyst is, in its essence, a junior role to a data scientist and can be viewed as a data scientist ‘light’. Their skillset should be similar to that of a data scientist but not as advanced.
A machine learning engineer is required to excel in building systems that use data as a foundation, therefore programming, software engineering and system designs are a must for them. Data translator’s core skills are coding as communication as they must have a deep understanding of data delivered to them, the context of your business goals, knowledge of the industry, and capability of delivering it to people who cannot read raw data.
Finally, a data scientist manager is the generalist of the team. They take ownership of ensuring quality, smooth in-team collaboration, setting up and tracking schedules and managing finances. In other words, while they need to be well versed in data and all things other roles entail, their key skills are clear communication with the stakeholders and project management.
One skill that should be common across every role is understanding of the industry your business operates in. Lack of visibility of what key challenges are will result in poor judgement regarding both collection and handling of the data.
Although building a functional and results-driven data science team is a thorough and complicated process, it alone won’t deliver success. As said in the introduction, digital transformation is primarily a mindset. So, for the data team to be successful, you will need to ensure it’s firmly embedded in the company and included into workflows of every department. At the end of the day, understanding the importance of data should be spread across the entirety of your organisation and not limited to the stakeholders.
People are prone to be less receptive and actually opposed to changes when they don’t understand the purpose. Very often the process of digital transformation is stalled due to the company employees rejecting new processes, changes in existing processes or full abandonment of processes that are deemed ineffective.
This is why it’s extremely important to get the entirety of the business on the same page of understanding why data is the key to everything. Once the mindset of the organisation as a whole is set towards aligning their activities based on data as opposed to assumptions or processes that have simply become comfortable, the process of digital transformation becomes smooth and easy.
How to build and maintain a data-driven culture? Via educating different teams on how the data science team can work to help their objectives and performance. You can hold a few workshops with different departments or present them with viable results achieved through onboarding data-first approach to raise awareness.
Just as the title of this section states – the key to your success is establishing close communication between other departments and the data science team. This is slightly different from understanding the overall importance of data – this focuses on more personal, human relationships. In fact, this is more of the next step after your team adopts the data-driven mindset – establishing close, direct communication with the data science team is what will enable them to utilise the data they’re presented with better.
All in all, while the departments across your company may understand the importance of what the data science team does, they might not necessarily know where to apply that within their activities. Having a clear two-way communication between the two will make it easy for them to ask and receive guidance if such issues arise.
Finally, the most important bit that ties everything we’ve spoken about together is to unite your entire business with one goal: digital transformation. From understanding the importance of data to communication to working together in improving processes, the entirety of your organisation must have one long-term goal in sight: digital transformation.
Of course, individual teams will still have their personal targets to hit, however these should be assessed ahead of time too to determine whether they contribute towards achieving business goals or not. Having a mutual goal allows aligning all teams together, and that in turn delivers best results in the shortest time,
Let’s assume you have achieved what you set out to – be it complete digital transformation or growing your revenue through adopting it. Does this mean you are to disband the data science team now? Absolutely not.
As highlighted before, digital transformation is a mindset that drives progress, and the progress doesn’t stop. That means that although you have achieved your primary goals, there is always room for improvement. Your data science team will help you find new opportunities and areas of growth, and from there you can start moving forward. All in all, the journey never truly ends and all you’ve done up to this point must be optimised on a consistent basis. Not to just preserve the results but to carry on growing and scaling.
In 2021, data science teams are more relevant than ever. However, with so many resources on the topic available, it is easy to get confused in regards to what practices work and what don’t. Two things are known for sure: you can’t rely on having data scientists alone to perform as a diverse data science team team and that to enable digital transformation, a data science team alone will not be enough.
We have a lot to say on the topic of data science and how to be as efficient as you can when implementing digital transformation. If you would like to discuss it with us – get in touch.