As the demands regarding AI is rampant, the need to have a disciplined and co-ordinated analytics team is imperative.
The demand for AI in the industry is unsurpassed. Even before the COVID 19 outbreak affected the working of IT infrastructure, the organizations were looking out for recruiting good data analytics team. With the advancement of AI, it becomes imperative for organizations to not only harness good data analytics but also to reach out to skills that can be insightful towards data analytics.
A data analytics team usually consists of data scientist, data engineers, data designers, statisticians, research analysts, and business analysts.
For LinkedIn’s emerging job reports for the year 2020, Data Scientists and Data Engineers gained the top positions. Both skills are flooded with young talent, new ideas, and a yearning to collaborate and advance data analytics. However, often due to the absence of coordination, communication, and focus, data scientists and data engineers can fail to deliver the required results, leading to instability within the infrastructure.
Both Data Scientists and Data Engineers form the core of the data analytics team.
Data Engineers collect, manage, and store the data. They are responsible for data infrastructure, scale and quality to be analyzed by Data Scientists. They design, build, create, and integrate data from various sources, and write queries of the easily accessible data. The Data Engineers are more focused on designing data, thus preparing a large amount of big data to be used in making Business Decisions.
Data Scientists work on the usage of the data. They turn big data into legible insights with the application of Artificial intelligence and Machine Learning, to solve critical business problems.
Creating the Hub
A Hub represents a cross-functional team, including Data engineers and Data Scientists, which display a mixture of skills and perspectives. It is operated by Chief Data Officers. Building a strong data ecosystem, one that is agile and responsible, data scientists and data engineers must be assigned responsibilities according to the skills exhibited by both. As they work in the same neighborhood in the IT infrastructure, it will embed a sense of accountability, which will help them in setting up priorities and achieving goals.
A Hub also helps in nurturing the AI talent and creating communities, where the employees display their methods and ideas that works best for an organization. It also amplifies space for addressing multiple issues, along with the isolated business one.
The Hub organizes different AI models and interprets various data insights by deploying new AI capabilities.
Building Diverse Teams
Building a diverse team would consolidate a variety of opinions and ideas. It will allow data engineers and data scientists to apply different permutations and combinations of the existing algorithm to derive into a conclusion.
A diverse team enables Data Scientists and Data Engineers to apply AI tools, address the new challenges, and acknowledge the loophole in the existing algorithm. As mistakes tend to learn, it will facilitate the employees to correct minor issues that were difficult to notice earlier.
The diverse team can also include assigning executive projects that will enable the employees for incorporating a broader perspective, deploying and monitoring new AI capabilities by inputs from the frontline staff.
Explaining the importance of the Project
For any organization to work smoothly, it becomes necessary that the employees and the team must understand the necessity and importance of a new or existing project.
Making them understand, the aim, procedure, strategical process, and reason behind taking up a new or existing project would enhance their performance, thus rendering them to work towards a common goal.
Providing Space to Learn and Grow
Data Scientists and Data engineers are young talents that thrive in learning and applying the knowledge for building new products. Redundant work can make them unenthusiastic towards their skills, hence organizations must focus on a broader overview for implementing growth and learning.
This will also enhance in sharpening their AI tools, mature their AI capabilities, and make them more accountable, enthusiastic, and responsible towards the data ecosystem.
Managing Relationships to Learn
Managing Relationship between employees helps in reviewing past barriers. Developing a portfolio of initiatives by managing relationships would enhance the prioritization of projects, provide a long term view, and would help in taking decisions for combined projects.