In 2020, data is a goldmine of information, and if you can collect and analyze the right data sets, a lot can be achieved in a short period of time.
As companies around the world, start recognizing and collecting more data points from their customers, it is crucial than ever before to have a data analytics team, which can not only process and analyze the collected data but also emphasize sharing key insights which will assist you in advancing your business.
LinkedIn, the number one job search portal reported that 2020 saw a 25% increase in professionals who are seeking a Big Data Career in data science and analytics.
While this clearly indicates that the importance of data scientists is on a steady rise, it also indicates that companies need to better analyze the capabilities of each individual domain to choose the right man for the job.
How to Choose and Build the Right Data Analytics Team for Your Company?
One of the first and most crucial aspects to understand and embrace is the fact that in 2020, data scientists come with a variety of different skill sets, and thus it is essential to recognize each of the skills and categorize them into the functions best suited for.
While building an analytics team for your organization, you can follow either of two different approaches.
- The Direct Method of Segmentation
- The Indirect Method of Appreciation
The Direct Method of Segmentation
The concept of the direct method of segmentation is based on the ideology that each data scientist depending on their skill set can be grouped into either of three different designations and then hires can be made based on deciding which skill is required first.
- Data Engineers: Data Engineers are the crux of any data analytics team you want to design. The main skill sets you should look for in a data engineer include, ETL (Extraction, Transformation, and Load), Data Warehousing, data processing, and other similar roles.The fundamental job of a data engineer can be summarized as preparing the data for further analysis by data scientists and analysts, who form the rest of the team. They generally have a degree in Big Data Analytics Training.
- Data Analysts: Using the data prepared by data engineers, analysts extract critical information and decisions which are helpful in solving problems and contribute to advancing business decisions within the organization.
- Data Scientists: Data scientists form the last hierarchy of the team and are mainly responsible for crafting and perfecting algorithms using either Machine Learning or Artificial Intelligence to make compelling decisions from unstructured data sets. While a data scientist can easily be tasked with the responsibilities of both analysts and engineers, in big teams these designations are separated for better utilization of time and resources.
The Indirect Method of Appreciation
The indirect method of appreciation is based on the concept of recognizing people who have a broad range of skills, but also in-depth knowledge in a few key areas. This method of hiring can be understood using the “T-Shaped” skill concept, where the horizontal bar of the T represents the broader knowledge set of the hires, and the vertical bar represents the specialized knowledge in key areas.
The overall aim of this methodology is always to find the right set of people, who have the expertise and the knowledge to get the work done in a timely manner.
Conclusion
Building the right data analytics team for your business can not only contribute to its immediate success but also long-term growth. Thus always make it a point to invest the right amount of resources and figuring out which methodology of hiring works best for your business.
