Key skills of Business Analyst

Key skills of Business Analyst in analytics projects

Business data analytics is an area of study that targets effective business decision-making as opposed to using the rigorous technical know-how through which data is analyzed. Several business analysis tools, techniques, competencies, and key skills are used in business data analytics to direct analytics initiatives within the life cycle of an analytics initiative.

A business data analyst should possess industry and functional expertise as well as a deep understanding of the meaning of data in the business context. An analyst bridges the gap and partners with both the technical and business stakeholders, hence efficient communication with both groups is necessary. Statistical, mathematical, analytical, data mining, and machine learning algorithm knowledge is required to be able to identify data sources, prepare data mappings, perform exploratory analysis, and identify the optimal model for the data based on business needs. Strong visualization skills are also important to communicate data-driven insights and change recommendations to stakeholders in a simple language. Once, the change is approved, implementing the change and managing the entire process involves interaction and approval from many stakeholders.

To summarize, a business analyst should have a broad understanding of five areas: Business, Statistics, Machine Learning, Programming, and Math. A business data analyst plays a crucial role in an analytics project right from framing the business question to managing change implementation in the organization.

While creating and implementing an organizational level strategy for analytics, a business analysis professional can use these underlying competencies and skills.


A. Business Knowledge

Business knowledge is an in-depth understanding of the business functions and the specific areas under analysis.

Business acumen
Business acumen helps to appreciate the importance of fundamental business principles and best practices across industries to review the organizational strategy. It can be useful in the below scenarios:
⦁ Performing stakeholder identification and facilitating discussions with business and technical teams.
⦁ Determining suitable data sets based on the current business situation.
⦁ Providing context to the data being validated and helping analysts determine if the data is accurate and complete.
⦁ Providing context to the data analysis and answering questions related to the business posed by the data scientist.
⦁ During the testing of the data approach and when profiling data.

Trustworthiness and ethics
Gaining the confidence of different stakeholders involved in the initiative will help:
⦁ Build trust and rapport with stakeholders who may be needed to gain access to data or participate in elicitation activities.
⦁ Handle data with legal implications, what acceptable data use is and what it is not, and what can be accessed or viewed and what cannot.

Industry knowledge
Industry knowledge is used to understand current practices and activities within an industry and similar processes across industries for organizational strategy. It can help to:
⦁ Identify the most relevant data.
⦁ Process information, review, and assemble the analytics results in an organized fashion.
⦁ Design surveys and developing good quality questions to support the overall research questions

Solution knowledge
Solution knowledge is useful while recommending various approaches based on technologies, analytics platforms, and future trends. It can help to:
⦁ Understand and communicate the current state.
⦁ Provide context and insights when developing a data collection approach.
⦁ Identify the most relevant data.

Systems thinking
System thinking helps to think holistically about interactions between people, processes, and technological aspects to suggest a specific strategy. It helps to:
⦁ Process information, review, and assemble the results in an organized fashion.
⦁ Recommend actions based on insights from the analytics initiatives.
⦁ Understand the people, processes, and technologies to suggest how best to make changes that are based on analytics results.

Adaptability helps to adjust the analysis approach as more data is uncovered, new insights are learned, or different levels of stakeholders are involved.

Organization knowledge
Organization knowledge is useful when considering the management structure and business architecture of the enterprise while designing strategy.

Methodology knowledge
Methodology knowledge is useful while developing an approach based on context, dependencies, opportunities, and constraints.


B. Collaboration, facilitation and communication skills

Effective collaboration with the correct stakeholders needs good communication skills. Facilitation skills are required to manage meetings, workshops, and other collaborations. It is useful in the below scenarios:
⦁ Gather as much information as possible through verbal and non-verbal cues.
⦁ Listen and understand abstract information.
⦁ Perform stakeholder identification.
⦁ Recommend a course of action when data is missing.
⦁ Highlight business rules for solutions.
⦁ Facilitating consensus among stakeholders.
⦁ Development of a well thought out communication approach with recommending actions.
⦁ Communicate clearly and precisely about the strategy across all the levels within the organization.
⦁ Communicate proposed procedural changes.


C. Interaction skills

Interaction skills help facilitate interaction between multiple stakeholders involved in the initiative and also can be a useful while:
⦁ Resolving conflicts between various parties involved.
⦁ Leading discussions to identify metrics and establish objectives.
⦁ Developing an implementation plan.
⦁ On-going interaction with the data scientist to determine whether the analysis results are helping to answer the business question.


D. Analytical thinking and problem solving

Analytical thinking helps to solve problems efficiently and effectively. It involves methodical thinking that helps break down complex problems into manageable components, come up with solutions, and make decisions.

Conceptual thinking
Conceptual thinking helps connect seemingly abstract, large, and potentially disparate information for arriving at a suitable strategy. It is helpful while:
⦁ Understanding the big picture and providing the context for the analytics work.
⦁ Formulating ideas about which data to use.
⦁ Making sense out of the large sets of disparate data sources under analysis and drawing relationships and understanding from the data.
⦁ Recommending actions for change.

Decision making
Decision making assists in informed decision making based on various criteria and has an objective evaluation. It helps during:
⦁ Facilitating discussions with those who approve the data collection plan.
⦁ Facilitating agreements on the types of changes to be made.
⦁ Leading discussions to identify metrics and establish objectives.

Visualization skills
Data storytelling and data visualization work together to enable clear, concise, and visually appealing communication. It is useful while:
⦁ Creating conceptual architectural diagrams that depict the data sources, data flows, and frequency of the data feeds facilitating discussions about data sourcing with stakeholders and facilitating approvals.
⦁ Effectively understanding the insight by using visualization and data story explaining the visualization.

Creative thinking
Creative thinking helps analyze various ideas and approaches to implement a specific strategy. It is helpful while:
⦁ Formulating ideas about which data to use.
⦁ Recommending actions
⦁ Process information, review, and assemble the results in an organized fashion.

Analysis skills
Sufficient information about the business domain is provided to the data scientist so an effective approach to data analysis is developed. Basic skills in statistics and a basic understanding of data science tools and technologies and data analytics.

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