Role and Responsibilities of a Data Analyst in an Automation Team
Having a great team is an important part of automation project success, and one of the central figures in this team is Data Analyst. Along with RPA and Machine Learning engineers, Data Analysts directly participate in the implementation of an automation solution.
So, what does a Data Analyst do for automation?
Basic role of a Data Analyst
In general, data analysis is a process of inspecting, cleansing, transforming and modeling data to discover useful information, inform conclusions and support decision-making. The basic role of a Data Analyst in an automation project is to help with preparation, cleaning, and analysis of machine learning training data, as well as business process data for robotic process automation and validation. This facilitates the development of the best practices for maintaining strong and useful data sets for production.
A Data Analyst needs to oversee selection of appropriate documents, then their processing, structure and input for model training. Because the overall success of machine learning implementation highly depends on the quality of input data, this role is crucial for projects’ success. The responsibilities of a Data Analyst also include:
- Collecting, building and analyzing large data sets
- Machine learning model training
- Writing comprehensive reports
- Developing and implementing a problem-solving approach.
Participation of Data Analyst in automation projects
Data Analysts participate in the automation project at the implementation phase, which is usually divided into seven stages.
- Data set collection. At this stage, DAs study the business logic of the use case and applicable documents and define the rules and corner cases.
- Model training. The ML engineer or RPA engineer leads model training and the Data Analyst analyzes machine learning results and defines rules that help to improve the model.
- Analysis of results. The Data Analyst needs to review model execution results and, if necessary, propose ways for improvement.
- ML improvement and retraining. At this stage, DAs work closely with ML engineers on creating custom model components.
- First final report. Data Analysts must share the final report of automation results and suggest improvements.
- Integration of ML results into RPA. At this stage, structured data made by machine learning is entered into the organization's internal systems.
- Final report. Data Analysts and business users see and understand the outcomes of automation in production.
To be able to perform these tasks, a Data Analyst needs to have certain technical and non-technical skills.
Skills of Data Analysts
In an Intelligent Automation project, a Data Analyst is not expected to possess coding skills but instead must have deep knowledge and practical skills around working with data. For a successful career in data analysis, a person should know mathematics, statistical analysis, and economics, as well as be familiar with analytics tools and monitors, such as Tableau).
Several non-technical qualities are usually expected for a Data Analyst role, including:
- Experience in data models and reporting packages
- Ability to analyze large data sets
- Strong verbal and written communication skills
- An analytical mind and inclination for problem-solving
- Attention to detail and patience.
How to become a Data Analyst
If you are interested in becoming a Data Analyst, Automation Academy offers a special ML Data Analyst learning path for partners and customers consisting of several online courses.
These courses take around two weeks to complete and cover all important topics required to become a certified WorkFusion Data Analyst: RPA and machine learning basics, data analysis, and using the AutoML component in automation.
After completing the learning path, you will be able to build, train and analyze large data sets for Intelligent Automation projects.
