Data is the new oil. Today many companies are able to maintain the data in huge numbers. This is data is not another record for the organizations. It is the main key for their growth in future. Without data it nearly impossible for a business to sustain in this era of startups and business. Data is required to analyze the past performance and make required amendments to grow their business.

Many of us might have worked with data while making small scale projects in machine learning or deep learning and know the true value of it. But in big organizations and companies data is really huge. It requires for a well planned ecosystem maintained to handle it. In this article we will be discussing about the different roles in this huge data ecosystem.

1. Data Engineer

Data engineer is a person who is responsible for developing and maintaining the data architecture. He/she must make the data available for business operations and analysis. A data engineer must separate OLTP(Online Transaction Process or the real database) and maintaining a data warehouse or OLAP(Online Analytical Process, basically copy of the database for analysis). Any queries that required to run must be implemented on OLAP and not on OLTP. It required the effort of collecting the data from various resources. After this they will have to clean and transform the data. Data repositories must be designed to store the data.

Skills:

• Sound knowledge in system design and architecture.
• In depth knowledge of relational database management system and non relational database management system.
• Good programming skill.

2. Data Analyst

One cannot just look inside a huge dataset and derive insights. Data is understood much better in visuals rather than in numbers. Main role of a data analyst is to convert the numbered data into plain language and insightful visuals. They clean the data for deriving insights, identify correlations, find patterns and apply statistical models. They are the one who answers the questions regarding past performance of the organization. Basically they depict the performance of the business in the past.

Skills:

• Well versed with spreadsheets and popular BI tools such as tableau and power bi.
• Good analytical mindset as their insights are the foundation for the future decisions.

3. Data Scientist

Data scientists monitor and combine the work of data engineer and data analysts for actionable insights. They apply machine learning or deep learning to create robust predictive models. They are the one who are answerable to the questions that decides the growth of the business in future. A good data scientist must always come with a good suggestion derived from the past analytics to shape the better growth of business in the future.

Skills:

• Good knowledge in Mathematics, Statistics, Programming, machine learning and deep learning.
• They also need to have good domain knowledge as well.

4. Business Intelligence Analyst

BI Analysts recognize the work of data scientists and data analysts to look at the possible amendments that can be done to their business and take actions accordingly. Their main focus is on the market force and the external influences that run their business. Provide different solutions for their business by monitoring data on different business functions.

Skills:

• Good management skills
• Must have both technical and finance/sales related knowledge
• Must execute the proper actions based on the insights and suggestions.

Summary

• Data Engineer converts raw data into useful data.
• Data Analyst uses the data to generate useful insights.
• Data Scientist predict the future using the data from the past.
• BI Analyst uses these insights and predictions to derive the decisions that grow their business.