Data science is the key to digital transformation: a course many businesses are taking to improve their performance. However, hasty decisions when incorporating data science into an existing business without a plan can backfire and bring more harm than good.
Tying Data Science Team into Your Strategy
Few articles ago we’ve spoken about building a data science team that would deliver the change your company seeks. We have also looked into an essential skill set for data scientist, emphasising that they must understand the industry your business operates in. However, this flows both ways: your business must understand the role of data science prior to adopting it.
Most companies struggle to incorporate data science teams into their workflows due to the inability to see how their activities relate to one another. Of course, there are other offenders such as limited access to quality data and technological gaps, but they are not our focus today.
In this post we will share six tips on how to ensure successful integration of the data science team within your business, enabling digital transformation instead of delaying it.
The chain reaction starts with you. Even prior to beginning a hiring process for the positions on a data science team, you need to get a better understanding of the field. To be more precise, you need to understand that data science is not an overnight solution to your organisation’s problems.
It is a method that allows you to make better business decisions. Simply put, there won’t be immediate returns on investment. Depending on your goals, it can take months if not years to see the results but that’s because data science is an approach and not a strategy itself. You can access your digital transformation readiness on our website.
A frequent mistake that puts businesses off data science is blowing the entire budget on it and expecting immediate ROI. When it doesn’t happen, they abandon the process which results in lost money and drawbacks in achieving digital transformation.
Prior to turning to data science, you need to evaluate your current standing. To provide the team with a firm footing and quick understanding of what your company is about you need to have at least some data.
Much like a flywheel not fueled by wind, data science won’t move unless it has data to work with. Look through your current processes: even if you’re at the early stages of data fluency, there is certainly some data you can pick up on and document. For example, using Google Analytics you can gather data on how many visitors has a certain page on the site pulled in the past month. Then compare that to the marketing efforts carried out that month – how is it different from the time period when there was no activity?
Historical data is a good place to start for the data science team. To save time and to increase their effectiveness, collect some data prior to onboarding the team that will be working with it.
Similarly to how you need to understand the purpose of having the data team as part of your organisation, your colleagues do too. However, the understanding you need to provide will differ based on their position in the company. Think of their goals within the company: what are they trying to achieve as an employee? And tailor your approach addressing just that.
For example, the management and stakeholders care about generating income. Explain to them how data science enables spotting business opportunities and averting risks before the threats even arise. Put emphasis on how data-driven business decisions are more impactful than those based on assumptions and the overall growth acceleration that can be achieved through data science.
The end-user, such as the sales department, for example, will need to explain how the findings of the data science team will impact their performance. Ask them about the challenges they are currently facing and suggest how the implementation of a data-driven approach will assist in overcoming them. Introduce the concept of having a data science team to different parts of the company addressing their specific needs. This way there will be no uncertainties about the value it brings.
A common mistake that causes the failure of 80% of analytics projects is the data science team structure. Most companies fixate on prioritising data scientists and neglect other essential positions that need to be filled: such as a data translator.
This is a common misconception that a top data scientist should be capable of filling in for the entire data science team. While they do have a unique and pretty multidimensional skillset, it does not meet every requirement a successful data-driven approach needs. In fact, the lack of the aforementioned data translator on the team will make the results driven by a data scientist less impactful. What’s the point of analysing the data if learnings can’t be actioned??
A functional data science team should be built to include five roles of diverse skills. To learn more about what they are, click here.
Let’s assume you have acted on the last two tips: you have a great data science team and everyone in the company understands the value they are getting. Understanding the concept is different from putting it to use. In other words, your team might not know how to utilise the value provided by the data science team.
This is why you should establish communication between them. The data science team, unlike your explanation, are capable of pointing out where exactly their findings are applicable. However, they are unlikely to be the ones to reach out – the other teams will need to approach them with specific questions first. Creating this type of communication not only improves the overall performance of all teams across the business but also elevates the workplace environment.
This tip is fairly similar to the one above, but not the same. Let’s be honest: data is not the most exciting topic in the world. While your organisation may understand its importance and have a solid communication established with the data science team, they are not really interested in data.
The key to digital transformation and the reasoning behind hiring a data science team is to have the entire business aligned in a data-driven approach. In other words, everything done in the company should be based on data. To achieve that, you need to create a data-driven culture that will, in turn, nurture a data-driven mindset.
You can achieve that by holding regular workshops with the data science team and other departments. Additionally, it’s a good idea to highlight any growth areas that have occurred thanks to a data-driven approach. In other words, you need to instil the idea in the entirety of your business that data is the core of any decision making. Once that understanding is there, data-driven culture will flourish organically.
Embedding a data science team to your business is a process that cannot be overlooked. Failure to integrate data science within every aspect of the organisation will result in wasted budget and frustration.
At the end of the day, you are hiring a data science team to optimise and improve your business processes. But for that to be successful you need to ensure the rest of your organisation understands them as well as how the data will affect their individual performances.