Who’s in Your Dream Data Team? – The Optimal Data Team Structure
Most companies today understand the importance of harnessing data and transforming it into actionable insights that add value to the business. However, many companies are still stuck when it comes to putting this into action. How do you mobilize a data science team? And what is the optimal data team structure? We have the answers.
You can’t run a successful data team without data scientists. Data scientists analyze and interpret complex digital data to assist in business decision making. Although data scientist is still a relatively new role, data scientists have existed in many forms for several years now. With the age of Big Data and the current data boom we’re experiencing, the number of data scientists has rapidly increased. This is primarily due to two factors.
Firstly, the global datasphere (the amount of data we are creating, capturing, copying, and consuming globally) is increasing exponentially. As a collective, we will generate more data in the next three years than we have in the last 30 years . More data means more data experts like data scientists are needed to crunch through the data. The data backs this up; the number of data scientists has doubled in the last four years .
Secondly, our ability to analyze data has vastly improved over the last decade and even the previous few years. Data analytics tools, techniques, solutions, and processes are now highly refined and as a result, generate significant benefits for businesses.
The data scientist role includes the following responsibilities, and more:
- Creating services that automate business processes. For example, risk scoring in finance, pricing policy optimizations in insurance or improving quality control processes in manufacturing
- Extracting hidden insights from data to support people in decision making. For example, cancer detection in CT images, or improving urban environments by utilizing traffic dynamics and satellite image analysis
- Working closely with the business to identify issues and patterns in data and proposing solutions
- Using machine learning tools, statistical techniques, and artificial intelligence to create solutions
- Testing and determining the best data mining models for different projects
- Creating clear reports.
What Skills Are Essential for Data Scientists?
If you are recruiting data scientists for your business, you should look for candidates with the following skills:
- A strong background in programming, statistics, and machine learning
- Excellent problem solving, critical thinking, and analytical skills
- Exceptional communication and presentation skills. Data scientists must be able to translate their findings to the business in a way that supports and encourages better decision making
- Good attention to detail
- Drive, resilience, and creative thinking skills. Data scientists must be able to work with minimal supervision but continually lead data science initiatives.
Data engineers are a crucial part of any data science team. Data engineers’ main focus is creating infrastructure that enables effective data collection, processing, and storage. They need to be able to develop scalable solutions that ensure high-quality data capture, among other things. People often use the terms data scientist and data engineer interchangeably, but they are distinct roles. Data engineers can be thought of as software engineers, but with a specific focus on data. They are primarily tasked with transforming data into a format that can be easily analyzed, and they do this by maintaining, developing, and testing infrastructure. They work closely with data scientists and are mostly responsible for architecting solutions for data scientists. Put simply, data engineers enable data scientists to do their job.
Beyond the above, data engineers do the following:
- Create and maintain data pipeline architecture
- Deploying machine learning solutions into production environments
- Write the code for data solutions with help from data scientists and DevOps
- Build optimal data extraction or transformation solutions using SQL, AWS, and other technologies
- Keep data segregated and secure
- Documenting source to target mappings.
What Skills Are Essential for Data Engineers?
- Strong software development background and a deep understanding of databases and algorithms
- In-depth knowledge of the data development process (must be able to integrate and separate data feeds to transform, map, or produce new data products)
- Understanding data warehousing solutions, data modeling, predictive modeling, ETL tools, statistics and math, and machine learning
- Strong communication skills.
The above roles capture the core responsibilities within a data team; however, the list isn’t exhaustive. It’s always a good idea to combine the data science team with a diverse set of professionals. For example, professionals with MBA and statistics backgrounds are a great addition to any data team because they have strong transferable skills. Professionals from these backgrounds excel in defining business problems and developing solutions by using data. Similarly, a resilient specialist with a PhD in physics or a related mathematical discipline will also have strong technical problem-solving skills and be able to create elegant solutions to complex problems.
Business Intelligence Specialists
Business intelligence specialists define Key Performance Indicators (KPI) based on business requirements. They also use dashboards to create insightful visualizations to help business decision-makers understand the data and KPIs. Put simply, business intelligence specialists convert cold data into a format that is easily understandable by people, no matter their background. Data is only useful if it is understood, and it especially must be consumable to key decision-makers.
The top business intelligence specialist skills are:
- Analytics skills
- Understanding of business requirements
- Knowledge of ETL, SQL, Tableau, Looker
- Good grasp of visualization methodologies (how to make data understandable and digestible at a glance, effective use of color, and so on).