In the world of business, today, big data is employed by almost all industries to drive performance and competitiveness.
It is estimated that the big data analytics market will potentially reach $103 billion by 2023.
But minus the expertise of professionals who turn advanced technology into actionable insights, the data collected will be rendered meaningless.
So if you are a business looking for ideas to come up with an effective data science team, this article will equip you with all the knowledge you require.
What does a data scientist do?
There are different types of data scientists, but generally, you can divide them into two types.
The first type of data scientist helps to make sense of data through statistical analysis. The other type builds predictive models and algorithms the data products are powered by.
AltexSoft considers the skill sets below while hiring data scientists.
Critical skills to look for hiring data scientists
- Good communication skills.
- Ability to learn quickly to adapt to the changing nature of data science
- Skills to decipher a problem from business language into a hypothesis.
- Skill to do interesting things that are impactful at the same time.
- Intellectual curiosity.
- Experimentation mindset.
- Attention for in-depth technical work.
Should you build an in-house or outsourced data science team?
If you are a big company, building an in-house data science team will be beneficial, and it comes with plenty of advantages. You can be flexible, you retain your intellectual property, and you are self-reliant.
The cons of building an in-house team include high expenditure, the difficulty of assessing skills unless you are a data scientist yourself, scarcity of experts as the demand for top data scientists is exceeding supply, and it is time-consuming as well.
Small companies or startups have not many options but to outsource data science, though, in the long run, it is not advisable. The pros include being able to start your project right away, having external partners with verifiable experience, cost-effectiveness, and faster delivery.
The cons can include uncertainty that lurks about whether the outsourcing company has domain expertise, giving away control, and difficulty of finding the right team.
The type of data team you should build depends entirely upon your needs, expectations, and budget.
Communication is key
While building a team of data scientists, always ask questions that mirror your strategic business goals. It could be about attracting new clients, targeting VIP customers, or automating processes.
It will help you get your stakeholders, associates, and decision-makers involved with the things you are doing.
- What will bring out the best outcome, and what incentives will be included?
- How does the teamwork with stakeholders?
- How do you prioritize, approve, fund, and manage infrastructure investments?
- How do you allot costs? How are benefits understood?
- How business, legal, IT, and data will teams function without resulting in unacceptable risk?
Regardless of the approach, you must focus on communication, priority settings, and expectations.
Three data science team structures
- IT-centric team structure
This team structure can be built with a fully functional in-house IT department when you can’t hire a team.
The team will be responsible for managing tasks like preparing data, creating user interfaces, training models, and model operation within the corporate IT infrastructure.
- Integrated team structure
In this type of structure, you will have to build a data science team to focus on preparing the dataset and model training. The IT specialists will be managing the interfaces and infrastructure for model operation.
When you merge machine learning expertise with IT resources, it ensures steady and scalable machine learning operations.
You will need an experienced data scientist within the team. This structure will bring better operational flexibility when it comes to available techniques.
- Specialized data science team
This structure will incur the highest expense, but you can expect great results.
You can come up with an independent data science department to build an all-inclusive machine learning applications and frameworks.
A committed data science team will handle all operations, including data cleaning, model training, and building front-end interfaces. Even if all the team members don’t possess a data science background, they should possess a technology background and have expertise in service management.
This structure will help you sort out complex data science tasks such as researching, utilizing numerous ML models fitted to different aspects of decision-making, or diverse ML backed services.
How to avoid data science failure
One of the reasons why most of the big data projects fail is that they are not approached as projects that solve a business issue.
In many instances, data scientists are educating managers on analytics. Data scientists should instead enlighten their team on what takes place in the business around them. The team should also be taught how to adapt to fit the business.
Avoid data science failure by first examining the data you have at hand. The quality of data you have is more important than the volume. Unless you possess very high-quality data, it is better not to hire a data scientist yet.
You should also establish a data engineering team who can collect, store, and curate data, before hiring a data scientist.
A data science team needs both technical and critical skills to succeed. And it is created from the teamwork of skills and experience. When you find the right staffing, ensure to provide them with training and development opportunities to help them develop. Moreover, merge your data science team with the right technology to scale.
Data science is constantly evolving, and data scientists should be able to adapt to the changing trends. Upskill your workforce timely and hire staff with a range of skills. A data science team should include business analysts, data architects, data scientists, and data engineers. Also, remember to build trust with your clients as clients at times, request anonymous data due to security issues. It can impact the quality of the data solution.