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Setting up Your Data Scientists for Success: Insights from a Data Science CEO
Estimated reading time: 5 minutes, 33 seconds
Introduction
As the co-founder and CEO of Brainnwave, I have seen the role of data scientists evolve over the past decade. From a relatively new concept to a well-defined and critical function within organizations, the demand for data-driven decision-making has only increased. However, good data scientists are expensive and scarce, which means that organizations need to think carefully about how to set them up for success. In this post, I will share my personal insights on the key considerations that organizations should keep in mind when setting up their data scientists for success.
The Role of Data Scientists
As the demand for data-driven decision-making has grown, the role of data scientists has become increasingly important. They are responsible for analyzing and interpreting complex data sets to derive insights that can help businesses make informed decisions and achieve better outcomes. However, to ensure their success, it is essential to create an environment that supports data scientists and allows them to focus on their core responsibilities.
The Right Support is Critical
One of the challenges we consistently see in our clients is how to put the right support around the data scientist. They are often hired into small teams, and in many cases, they are a team of one. Hiring a data scientist without properly supporting them is like buying a high-performance sports car and expecting it to win races without providing it with the necessary fuel, maintenance, and driver training. Just as a sports car requires a team of professionals to keep it running at peak performance, a data scientist needs a supportive team and infrastructure to deliver valuable insights and drive business success.
Without proper support, data scientists will struggle to access the data they need, struggle to integrate their insights into the company’s decision-making processes, and face difficulty in implementing solutions based on their findings. In this scenario, the company may end up with a highly skilled data scientist who is unable to deliver the expected results due to a lack of support and resources.
Data scientists need support from other professionals in the company, such as IT staff, data engineers, and business analysts, to ensure that their work is aligned with the company’s goals and that they have access to the necessary data and infrastructure. Only with the right support can data scientists achieve their full potential and deliver meaningful insights that drive business success.
Key Considerations for Success
If you have recently hired a data scientist or are thinking of hiring one, there are several considerations to set them up for success:
- Hiring a team of data engineers who specialise in building data pipelines, managing the governance and integrity of the data, and ensuring that it remains up-to-date and structured in a way that data scientists can develop analytical models.
- Providing cloud infrastructure that can wrangle and store data and leverage the latest in machine learning and advanced analytical models.
- Deploying strategists who can help identify how the solutions can be adopted by the business and how the business must change to take advantage of the new insights.
- Hiring front-end software engineers who can visualise the outputs and feed them into a business process where decisions can affect change in the organisation.
Hire, Outsource or a Mix of Both?
In an environment where data scientists are becoming an increasingly important part of an organizations decision making strategy, they have also become a much more expensive and scarce resource.
| Advantages of Hiring a Data Scientist | Advantages of Partnering With a Specialist Data Science Company |
|---|---|
| In-house expertise and knowledge of the company’s data and business processes. | Access to a team of experts with a wide range of skills and experience in data analysis and management. |
| More control over the data analysis process and the ability to prioritize projects based on company needs. | A cost-effective solution, as the provider has already invested in necessary technology and expertise. |
| Greater ability to customize solutions to meet the company’s specific needs and goals. | Access to cutting-edge technology and analytical tools that may be too expensive for a company to acquire on its own. |
| Ability to build a long-term relationship with a data scientist and develop their skills and expertise to better align with company goals. | Scalability and flexibility to meet changing data needs as the company grows. |
| Ability to integrate data analysis into the company’s decision-making processes and drive innovation within the company | Experience doing this in many different organisations and can bring best practices to ensure successful change management. |
Partnering With a Data Science Company
When we founded Brainnwave, our goal was to be a partner to organizations that are embracing the new enthusiasm for data science and AI-driven solutions in a truly holistic way, what we call: Decision Intelligence. We recognised that organizations need more than just a “good data person” to realise the full potential of data-driven decision-making. That’s why we built our platform, Mosaic, to provide the most valuable technology components needed to deliver data-driven decision-making.
Conclusion
As the CEO of Brainnwave, I have seen firsthand the importance of setting up your data scientists for success. By partnering with a data science company like ours, organizations can fill the gaps in the data value chain and enable data scientists to focus on their core responsibilities. By considering the key factors outlined in this blog post, businesses can ensure that their data scientists have everything they need to deliver valuable insights and drive business success.
Moreover, working with partners like Brainnwave can help companies get maximum return on the investments they make hiring data scientists. By providing a comprehensive range of technology and services, we can help businesses realise the full potential of Decision Intelligence without the need to invest in building their own teams and infrastructure.
Building data science platforms today is analogous to building CRM systems in the 1990s. Back then, organizations all set out to build their own bespoke CRM systems, investing millions before starting to work with partners and specialists like Seabel Systems and then eventually organisations like Microsoft Dynamics and Salesforce. Today a business would hire sales professionals that know how to use these tools and focus on the core competence of driving sales in the organisations. In the same way, an expensive Data Scientist would achieve far more by working with a specialist partner to provide the infrastructure and focus on their core competence of driving new insights relevant to their business.
This allows organisations to allocate resources more effectively, focus on their core business operations, and drive innovation in their respective industries. Ultimately, setting up your data scientists for success is a crucial step towards achieving business success in today’s rapidly evolving AI landscape.
