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Business Intelligence Roles and Responsibilities

The job role of business intelligence or data analysts can be both rewarding and challenging. They are responsible for using data and analysis to inform business decision making. BI analysts work with large data sets to identify trends, spot opportunities, measure performance, optimize business operations, and they often use data visualization and reporting tools to present their findings in an easy-to-understand format.

The specific duties of a BI analyst can vary depending on the organization, but common tasks might include:

  • Gathering data from various sources
  • Cleaning and organizing data to prepare it for analysis
  • Analyzing data using statistical and computational techniques to uncover insights
  • Identifying key performance indicators (KPIs) and tracking them over time
  • Presenting data and findings to stakeholders using data visualization and reporting tools
  • Collaborating with other team members to develop and implement BI solutions
  • Supporting decision making by providing data-driven recommendations
  • Identifying areas for improvement and optimization based on data analysis
  • Monitoring the data and maintaining the BI system to ensure it remains accurate and up-to-date

BI analysts may work in a variety of industries, including finance, healthcare, retail, and manufacturing, and they may be involved in a wide range of activities, such as market research, customer relationship management, and financial analysis. They also often work closely with other team members, such as data scientists and business executives, to ensure that data is being used effectively to inform decision making.

Types of Business Intelligence roles

Career opportunities for BI roles have grown exponentially to meet the increasing demands of digitally transformed industries, businesses, functions, and processes. Organizations are looking to fill 4 major BI roles in a BI team with overlapping responsibilities: BI Analyst (junior) and BI Consultant (senior) who spend more time in step 1 and 4 of the BI process, BI Developer (junior) and BI Engineer (senior) who spend more time in step 2 and 3 of the BI process. When there isn’t a BI team and separation of duties, you will need to do all 4 steps and cover the entire BI process.

BI teams combine business analysis, data analysis, data modeling, and data visualization into one end-to-end BI process to deliver various BI services. For example:

  1. Building pipelines. Define KPIs. Develop Dashboards. Automate Processes.
  2. Determining when to use what charts to visualize measures by dimensions: line (trend of time series), pie (part-to-whole relationship),column or bar (comparison), histogram (distribution), scatterplot (correlation).
  3. Rollup and drilldown, slice and dice, sort and rank, present findings, recommend actions to monetize an opportunity (before window closes) or fix a problem (before it gets bigger).
  4. Presenting a 360-degree view of a business, from different angles (product, marketing, sales, operations, finance) and at different levels (analysts, managers, directors, VPs, C-Suite).

BI roles and responsibilities can vary depending on the organization and the specific BI project, but generally include the following:

  1. Business Analyst: Responsible for gathering and analyzing business requirements, creating functional and technical specifications, and developing BI solutions that meet the needs of the organization.
  2. BI / Data Analyst: Responsible for collecting, cleaning, and transforming data from various sources, and then analyzing the data to uncover insights and identify trends.
  3. Data Engineer: Responsible for designing and implementing the data infrastructure and tools that will be used for data integration, data warehousing, and data management.
  4. BI Developer: Responsible for creating and maintaining reports and dashboards that are used for reporting and analysis. This includes designing the layout, creating calculations, and developing the visualizations.
  5. Data Governance: Responsible for implementing and maintaining policies and procedures that ensure the accuracy, completeness, and security of the data used for reporting and analysis.
  6. BI Architect: Responsible for designing and implementing the overall BI architecture, and ensuring that it aligns with the organization’s goals and objectives.
  7. BI Manager: Responsible for leading the BI team, managing the BI project, and ensuring that the BI solutions meet the needs of the organization.

BI / Data Analytics process

A Typical BI Process

A typical BI process includes 5 steps:

Requirement analysis: How to make money in a business? Core processes, key decision areas, goal and objectives, strategies, tactics and operations are the key components that dictate the success of an organization. Requirement analysis is the process of identifying and gathering the information and data needs of an organization. The goal of requirement analysis is to understand the business needs and objectives of an organization and to identify what data is required to support these needs. The requirement analysis process typically includes the following steps:

  • Identify stakeholders: Identify the individuals and groups within the organization who will be using the BI system and who will be impacted by the data and insights generated by the system.
  • Define requirements: Define the specific data and information needs of the stakeholders. This may include things like sales data, customer demographics, inventory levels, and so on.
  • Identify data sources: Identify the various sources of data that will be used to support the requirements, such as databases, spreadsheets, and external APIs.
  • Analyze and prioritize requirements: Analyze the requirements to ensure they are clear, concise, and achievable. Prioritize the requirements based on their importance to the organization.
  • Create a requirements document: Create a document that clearly outlines the requirements, data sources, and any constraints or limitations of the BI system.
  • Validate requirements: Validate the requirements with the stakeholders to ensure they accurately reflect the organization’s needs and objectives.

This is the first step in the data analytics process, where the goals and objectives of the analysis are defined. This includes identifying the target audience, the specific business questions that need to be answered, and the type of data that is needed to answer those questions. This step is important to ensure that the final visualization is tailored to the needs of the audience and addresses the specific business questions at hand.

Data sourcing: Data sourcing refers to the process of identifying and collecting data from various sources to support the analysis and reporting needs of an organization. Data can be sourced from internal or external sources, and may include data from databases, spreadsheets, transactional systems, social media platforms, and other types of data sources. Once the requirements have been defined, the next step is to gather the necessary data. This can include collecting data from internal sources such as databases and spreadsheets, as well as external sources such as social media, public data, and APIs. The data may also need to be acquired from different systems and platforms, so data integration and data quality checks are important. The process of data sourcing in BI typically includes the following steps:

  • Data discovery: Identifying the various data sources that are available within the organization, and assessing the quality and relevance of the data for the BI needs.
  • Data integration: Integrating the data from different sources into a centralized data repository, such as a data warehouse or data lake. This may involve tasks such as data cleansing, data transformation, and data mapping.
  • Data validation: Validating the data to ensure it is accurate, consistent, and of high quality. This may include tasks such as removing duplicates, filling in missing values, and standardizing data formats.
  • Data security: Ensuring the security of the data by implementing appropriate access controls, data encryption, and other security measures.
  • Data monitoring: Monitoring the data to ensure it remains accurate and up-to-date, and to detect any issues or anomalies in the data.

Data sourcing ensures that the organization has access to the right data to support its analysis and reporting needs. By collecting, integrating, and validating data from various sources, organizations can gain a comprehensive and accurate view of their business.

Extract-transform-load (ETL): Once the data is collected, it needs to be prepared for visualization. This step involves cleaning and transforming the data to make it consistent and usable for analysis. This can include removing duplicate or inaccurate data, filling in missing values, and correcting any errors in the data. It also includes integrating the data from different sources and ensuring the data is in the appropriate format for the visualization tool. The specific steps in the process can vary depending on the organization and the type of data being used, but generally include the following:

  • Data acquisition: Collecting data from various sources such as databases, spreadsheets, and external sources.
  • Data cleaning: Removing duplicate or inaccurate data, filling in missing values, and correcting any errors in the data.
  • Data transformation: Changing the format of the data to make it consistent and usable for analysis. This can include normalizing data, aggregating data, and creating calculated fields.
  • Data loading: Loading the cleaned and transformed data into the BI platform for reporting and analysis.
  • Data validation: Verifying that the data is accurate and complete before it is used for reporting and analysis.
  • Data modeling: Creating a logical and meaningful representation of the data to support reporting and analysis.
  • Data governance: Developing and implementing policies and procedures to ensure that data is accurate, complete, and secure.

Data Visualization: With the data prepared, the next step is to create the visual representation of the data. This step involves using various data visualization tools and techniques to create interactive and informative visualizations that can be used for reporting and analysis. This can include creating charts, graphs, maps, and dashboards that help communicate the insights and patterns in the data. Some of the typical steps in the process are:

  • Data exploration: Exploring the data using tools such as SQL, R, and Python to gain a better understanding of the data and identify any patterns or trends.
  • Data modeling: A data model reflects the business model in a digital world. Data models are a powerful tool for understanding the business model, as they provide a visual representation of the relationships between different data entities. One of the most commonly used data models in BI is the Entity Relationship Diagram (ERD). An ERD is a graphical representation of the data entities, attributes, and relationships within a system or organization. ERD uses primary key and foreign key to create one-to-many relationships between dimension tables and fact tables.
  • Data visualization: Creating visualizations such as charts, graphs, and dashboards to make the data easy to understand and share with others. These visualizations should be designed to be easily understood by the intended audience and should be accompanied by explanations and context. The goal is to make the data and insights as accessible as possible to the stakeholders, so they can make informed decisions based on the data.

Data Analysis and interpretation: The final step in the data visualization process is to analyze and interpret the data. This includes identifying patterns, trends, and outliers in the data, as well as answering specific business questions. This step also involves interpreting the results of the analysis and determining what they mean in the context of the organization’s goals and objectives. The insights and findings should be translated into actionable recommendations for the organization. The process typically includes the following steps:

  • Hypothesis testing: Testing hypotheses about the data to validate the insights and findings.
  • Reporting and dissemination: Creating reports and disseminating the insights and findings to the relevant stakeholders.
  • Monitoring and maintenance: Monitoring the data and maintaining the BI system to ensure it remains accurate and up-to-date.
  • Actionable insights: Identifying actionable insights from the data and making recommendations for how the organization can use those insights to improve its performance.
  • Iteration: Continuously monitoring and refining the analysis and interpretation process by updating it with new data as it becomes available, and feedback from stakeholders.

It’s important to note that the analysis and interpretation process is not a one-time event, but rather an ongoing process that is constantly updated with new data as it becomes available, and feedback from stakeholders.

BI/Data Analytics tools

Business intelligence professionals use a range of tools and technologies to gather, store, access, and analyze data to inform decision making and support strategic planning. Some common BI tools include:

  1. Data visualization tools: These tools are used to create charts, graphs, and other visual representations of data to make it easier to understand and analyze. Examples include Tableau, QlikView, Looker, and Microsoft Power BI.
  2. Data storage and management tools: These tools are used to store, organize, and manage data, often in the form of a data warehouse or data lake. Examples include Amazon Redshift, Google BigQuery, and Microsoft Azure Data Lake.
  3. Data integration tools: These tools are used to extract, transform, and load data from a variety of sources, such as transactional systems, marketing databases, and social media feeds. Examples include Talend, Informatica, and Apache Nifi.
  4. Data mining and analysis tools: These tools are used to analyze data using statistical and computational techniques, such as machine learning and predictive modeling. Examples include R, Python, and SAS.
  5. Reporting and dashboarding tools: These tools are used to create reports and dashboards that present data in a visual format and allow users to track key performance indicators (KPIs) and other metrics. Examples include Crystal Reports, IBM Cognos, and Oracle Business Intelligence.
  6. Collaboration and communication tools: These tools allow BI professionals to share data and analysis with stakeholders within the organization, and may include features such as real-time collaboration and data sharing. Examples include Google Docs, Slack, and Microsoft Teams.
  7. Data security and governance tools: These tools help BI professionals to ensure that data is secure and compliant with relevant regulations and policies. Examples include data masking and encryption tools, as well as data governance platforms.

BI/Data Analyst Skills

There are several key skills that are important for BI professionals to possess in order to be successful in their roles. These skills include:

  1. Technical skills: BI professionals should have strong technical skills, including programming languages such as SQL and Python, data modeling, and database management. They should also be proficient in using BI tools and technologies, such as data visualization and reporting tools, data warehousing and database management tools, and data mining and analysis tools.
  2. Analytical and problem-solving skills: BI professionals should be able to analyze complex data sets and identify trends, patterns, and insights that inform decision making. They should also be able to apply critical thinking and problem-solving skills to develop solutions to business problems.
  3. Communication skills: BI professionals should be able to effectively communicate their findings and recommendations to stakeholders within the organization, including executives, managers, and other end users. They should be able to present data insights in a clear and concise manner, using visualizations and other tools as necessary.
  4. Interpersonal skills: BI professionals should be able to work effectively as part of a team and collaborate with a variety of stakeholders, including IT professionals, data scientists, and business leaders.
  5. Business acumen: BI professionals should have a strong understanding of business operations and how data can be used to inform decision making. They should be able to translate technical concepts and insights into business terms that are understandable to non-technical stakeholders.
  6. Project management skills: BI professionals may be responsible for leading data analysis projects, coordinating the work of other team members, and maintaining project schedules and budgets. They should be able to effectively plan, execute, and track projects in order to ensure that they are completed on time and within budget. This may involve using project management software, such as Asana or Trello, to manage tasks and resources, as well as communicating with stakeholders and team members to ensure that projects are progressing smoothly. BI professionals should also be able to identify and mitigate risks and issues that may arise during a project, and be able to adapt their plans as needed to ensure project success.
  7. Attention to detail: BI professionals should be detail-oriented and able to work with large amounts of data accurately and efficiently.
  8. Adaptability: BI professionals should be able to adapt to new technologies and methodologies as they emerge, and be willing to continuously learn and develop their skills in order to stay current in the field. They should be able to work with a range of tools and technologies, and be able to quickly learn and use new ones as needed.
  9. Attention to detail: BI professionals should have strong attention to detail, as they may be working with large amounts of data and need to ensure that it is accurate and complete.
  10. Creativity: BI professionals should be able to think creatively and come up with new and innovative ways to use data to drive business value. They should also be able to come up with creative solutions to challenges and problems that may arise in their work.

Check out other career guides

Our Career Guide provides all the resources you will need to help you get started in navigating data careers. We include free resources, guides, and tools that will help you get started.