Three Essential Components of a Successful Data Science Team
A Data Science team, carefully constructed with the right set of dedicated professionals, can prove to be an asset to any organization,
By Jack Danielson.
A Data Science team, carefully constructed with the right set of dedicated professionals, can prove to be an asset to any organization. It's a fact that success of any project is dictated by the expertise of its resources and data science is no exception to this golden rule of thumb. Professionals with diversified skill-sets are required to successfully negotiate the challenges of a complex big data project.
For your data science project to be on the right track, you need to ensure that the team has skilled professionals capable of playing three essential roles - data engineer, machine learning expert and business analyst. The presence of these three groups, working together for a common goal, would result in proper analysis of relevant information for predicting the behavior of consumers, in line with the business objective.
Let's now find out the responsibilities associated with each of the components. We will also shed some light on the profile of the desired candidates who can be trusted to do justice to these three roles.
Component 1 - Data Engineer:
The role of a data engineer is at the base of the pyramid. Data engineers constitute the foundation of a data science project and they are trusted with the responsibility of capturing, storing and processing the relevant data. Data Collection, Data Warehousing, Data Transformation and Data Analysis - these are typically handled by a team of data engineers.
They are the professionals who play with the tools and frameworks, like Hadoop or HBase, in a distributed environment to ensure that all the raw data points are captured and processed correctly. The processed data is then handed over to the next group of people, the machine learning experts, for taking it further.
Data engineer is predominantly a technical role. The ideal candidate does not need to be very academic but must possess technical competency on the back-end frameworks and tools used for capturing the data points.
If you are pro with Hadoop, MapReduce or HBase, then the role of a data engineer would perfectly match your profile. Besides technical acumen, analytical skills are also required to become a successful data engineer. A data engineer should be flexible to learn new tools according to the changing business needs and always be willing to upgrade to specialized techniques related to data analysis.
Component 2 - Machine Learning Experts:
Machine learning experts play the role of a link between the data engineers and the business analysts. They are primarily responsible for building data models and developing algorithms to draw conclusive information. Their job is to ensure that the derived information is well researched, accurate, easy to understand and unbiased.
Candidates with statistical background, having a deep interest in quantitative topics, are usually preferred for the role of machine learning experts. The ideal professional must have a solid understanding of data algorithms and data structures in specific, and software engineering concepts in general. Capability of handling computational complexity can be considered as an added bonus.
Component 3 - Business Analyst:
Data exploration and data visualization are the two most important responsibilities associated with the role of a business analyst. Business analysts work with front-end tools related to the core business and interact with the higher management of an organization. They further analyze business-level data provided by the machine learning experts to find out insights related to the organization's core business interests.
Another important responsibility of a business analyst is to coordinate with the data engineers and machine learning experts to make them understand the business objectives and identify possible focus areas. The ultimate responsibility of a business analyst is to produce actionable insights based on the processed data and help the company leadership in their decision making process.
Business analysts should have expert level knowledge on the underlying business data and source systems. The ideal candidate should have an eye for details and must possess exceptional analytical skills. Moreover, solid understanding of the organization's business model and the ability to think out of the box are two important qualities that all business analysts should definitely have. It is also important to have sufficient technical skills to come up with precise dashboards for representing business data in a structured manner.
While it's good to have the right people to fill in the right slots, that alone may not be sufficient for attaining success. It holds true for almost any project that relies on a team. Whether you want to start a blog with multiple authors or wish to build an online business with a team of partners, it is important for all the involved parties to think beyond the individual scope and work as one single coherent unit. When that happens, rest assured, success will automatically follow suit.
Bio: A passionate freelance blogger and internet marketing expert, Jack Danielson has more than 3 years of experience as a marketing guru, social media expert and search engine optimization consultant. Follow him on google plus.
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