A business intelligence consultant, also often referred to as a business analyst, BA, or BI, is a professional who gathers, analyzes, and uses data to help companies make better decisions. They develop and use BI tools, dashboards, and reporting systems and make sure that their employers monitor key metrics, identify trends, and improve their performance.
A BI consultant works at a crossroads of several roles – they combine skills in data analytics, IT, and business strategy and often work closely with stakeholders to make sure that their data insights align with business goals.
What’s the difference between a BI consultant and a business analyst?
Even though the roles are often intertwined and used as synonyms, there’s a difference between a BI and a BA.
If you need someone to build and manage BI systems for reporting and analytics, → You need a BI consultant.
If you need someone to analyze business problems and optimize processes → You need a Business analyst. Here’s a fast recap:
Even though the difference is there, much depends on the historical practices of each particular company. In some cases, you would find one person taking both roles.
History of the BI consultant role
The role of a BI consultant started in the late 1980s and early 1990s when companies began using data warehouses and tools to help with decision-making. As advanced BI software like SAP, Oracle, Tableau, and Power BI appeared, the role shifted from simple IT reporting to playing a key part in business strategy. In the 2010s, big data
Big data is a massive amount of information that is too large and complex for traditional data-processing application software to handle. Think of it as a constantly flowing firehose of data, and you need special tools to manage and understand it.
Big data definition in simple words
Big data encompasses structured, unstructured, and semi-structured data that grows exponentially over time. It can be analyzed to uncover valuable insights and inform strategic decision-making.
The term often describes data sets characterized by the "three Vs": Volume (large amounts of data), Velocity (rapidly generated data), and Variety (diverse data types).
How does big data work?
Big data is processed through a series of stages.
Data generation → Data is produced from sources, including social media, sensors, transactions, and more.
Data capture → This involves collecting data and storing it in raw format.
Data storage → Data is stored in specialized data warehouses or data lakes designed to handle massive volumes.
Data processing → Raw data is cleaned, transformed, and structured to make it suitable for analysis.
Data analysis → Advanced analytics tools and techniques, like machine learning and artificial intelligence, are applied to extract valuable insights and patterns.
Data visualization → Results are presented in visual formats like graphs, charts, and dashboards for easy interpretation.
What are the key technologies used in big data processing?
Big data processing relies on a combination of software and hardware technologies. Here are some of the most prominent ones.
Data storage
Hadoop Distributed File System (HDFS). Stores massive amounts of data across multiple nodes in a distributed cluster.
NoSQL databases. Designed for handling unstructured and semi-structured data, offering flexibility and scalability.
Data processing
Apache Hadoop. A framework for processing large datasets across clusters of computers using parallel processing.
Apache Spark. A fast and general-purpose cluster computing framework for big data processing.
MapReduce. A programming model for processing large data sets with parallel and distributed algorithms.
Data analysis
SQL and NoSQL databases. For structured and unstructured data querying and analysis.
Data mining tools. For discovering patterns and relationships within large data sets.
Machine learning and AI. For building predictive models and making data-driven decisions.
Business intelligence tools. For data visualization and reporting.
What is the practical use of big data?
Big data has revolutionized the way businesses operate and make decisions. In business, it helps with customer analytics, marketing optimization, fraud detection, supply chain management, and risk management. But that’s not all!
Big data in healthcare
Analyzing data helps identify potential disease outbreaks and develop prevention strategies. It became an important tool for virologists and immunologists, who use data to predict not only when and what kind of disease can outbreak, but also the exact stamm of a virus or an infection.
Big data helps create personalized medicine by tailoring treatments based on individual patient data. It also accelerates the drug development process by analyzing vast amounts of biomedical data.
Big data for the government
Big data can help create smart cities by optimizing urban planning, traffic management, and resource allocation. It can help the police to analyze crime patterns and improve policing strategies and response times. For disaster-prone regions, big data can help predict and respond to natural disasters.
Essentially, big data has the potential to transform any industry by providing insights that drive innovation, efficiency, and decision-making. That includes
finance (fraud detection, risk assessment, algorithmic trading),
manufacturing (predictive maintenance, quality control, supply chain optimization),
energy (smart grids, energy efficiency, demand forecasting), and even
agriculture (precision agriculture, crop yield prediction, and resource optimization).
What kinds of specialists work with big data?
The world of big data requires a diverse range of professionals to manage and extract value from complex datasets. Among the core roles are Data Engineers, Data Scientists, and Data Analysts. While these roles often intersect and collaborate, they have distinct responsibilities within big data.
Data engineers focus on building and maintaining the infrastructure that supports data processing and analysis. Their responsibilities include:
Designing and constructing data pipelines.
Developing and maintaining data warehouses and data lakes.
Ensuring data quality and consistency.
Optimizing data processing for performance and efficiency.
They usually need strong programming skills (Python, Java, Scala) and be able to work with database management, cloud computing (AWS, GCP, Azure), data warehousing, and big data tools (Hadoop, Spark).
A data analyst’s focus is on extracting insights from data to inform business decisions. Here’s exactly what they’re responsible for:
Collecting, cleaning, and preparing data for analysis.
Performing statistical analysis and data mining.
Creating visualizations and reports to communicate findings.
Collaborating with stakeholders to understand business needs.
Data analysts should be pros in SQL, data visualization tools (Tableau, Power BI), and statistical software (R, Python).
Data scientists apply advanced statistical and machine-learning techniques to solve complex business problems. They do so by:
Building predictive models and algorithms.
Developing machine learning pipelines.
Experimenting with new data sources and techniques.
Communicating findings to technical and non-technical audiences.
Data scientists need strong programming skills (Python, R), knowledge of statistics, machine learning, and data mining, and a deep understanding of business problems.
In essence, Data Engineers build the foundation for data analysis by creating and maintaining the data infrastructure. Data Analysts focus on exploring and understanding data to uncover insights, while Data Scientists build predictive models and algorithms to solve complex business problems. These roles often work collaboratively to extract maximum value from data.
Along with this trio, there are also other supporting roles. A Data Architect will design the overall architecture for big data solutions. A Database Administrator will manage and maintain databases. A Data Warehouse Architect will design and implement data warehouses. A Business Analyst will translate business needs into data requirements. These roles often overlap and require a combination of technical and business skills. As the field evolves, new roles and specializations are also emerging.
What is the future of big data?
The future of big data is marked by exponential growth and increasing sophistication. These are just some of the trends we should expect in 2024 and beyond.
Quantum computing promises to revolutionize big data processing by handling complex calculations at unprecedented speeds.
Processing data closer to its source will reduce latency and improve real-time insights.
AI and ML will become even more integrated into big data platforms, enabling more complex analysis and automation.
As data becomes more valuable, regulations like GDPR and CCPA will continue to shape how data is collected, stored, and used.
Responsible data practices, including bias detection and mitigation, will be crucial.
Turning data into revenue streams will become increasingly important.
The demand for skilled data scientists and analysts will continue to outpace supply.
Meanwhile, big data is not without its challenges. Ensuring its accuracy and consistency will remain a challenge and an opportunity for competitive advantage.
and cloud computing
Cloud computing is the delivery of computing services, including servers, storage, databases, networking, software, analytics, and more, over the internet (the cloud) to offer faster innovation, flexible resources, and economies of scale. Cloud computing enables users to access and utilize various IT resources and services on demand without needing to own or manage physical hardware or infrastructure.
Five key characteristics of cloud computing
On-demand self-service. Users can provision and manage computing resources as needed, often through a self-service portal, without requiring human intervention from the service provider.
Broad network access. Cloud services are accessible over the internet from a wide range of devices, including laptops, smartphones, tablets, and desktop computers.
Resource pooling. Cloud providers pool and allocate resources dynamically to multiple customers. Resources are shared among users but are logically segmented and isolated.
Rapid elasticity. Cloud resources can be rapidly scaled up or down to accommodate changes in demand. This scalability ensures that users can access the resources they need without overprovisioning or underutilization.
Measured service. Cloud usage is often metered and billed based on actual usage, allowing users to pay for only the resources they consume. This "pay-as-you-go" model offers cost efficiency and flexibility.
Service models of cloud computing
There are three primary service models of cloud computing: IaaS, PaaS, and SaaS. Let’s break them down.
IaaS
Infrastructure as a Service provides virtualized computing resources over the internet. Users can access virtual machines, storage, and networking components, allowing them to deploy and manage their software applications and services.
Description: IaaS provides users with virtualized computing resources over the internet. These resources typically include virtual machines, storage, and networking components. Users can provision and manage these resources on demand, giving them control over the underlying infrastructure.
Use Cases: IaaS is suitable for users who need flexibility and control over their computing environment. It's commonly used for hosting virtual servers, running applications, and managing data storage.
Examples: Amazon Web Services (AWS) EC2, Microsoft Azure Virtual Machines, Google Cloud Compute Engine.
PaaS
Platform as a Service offers a higher-level development and deployment environment. It includes tools and services for building, testing, deploying, and managing applications. Developers can focus on writing code while the platform handles infrastructure management.
Description: PaaS offers a higher-level development and deployment environment that abstracts much of the underlying infrastructure complexity. It includes tools, services, and development frameworks that enable users to build, test, deploy, and manage applications without worrying about the infrastructure.
Use Cases: PaaS is ideal for developers who want to focus solely on coding and application logic without managing servers or infrastructure. It accelerates application development and deployment.
Examples: Heroku, Google App Engine, and Microsoft Azure App Service.
SaaS
Software as a Service delivers fully functional software applications over the internet. Users can access and use software applications hosted in the cloud without the need for installation or maintenance. Common examples include email services, customer relationship management (CRM) software, and office productivity suites.
Description: SaaS delivers fully functional software applications over the internet. Users can access and use these applications through a web browser without the need for installation or maintenance. SaaS providers handle everything from infrastructure management to software updates.
Use Cases: SaaS is widely used for various business applications, including email, collaboration tools, customer relationship management (CRM), human resources management, and more.
Examples: Salesforce, Microsoft 365 (formerly Office 365), Google Workspace, Dropbox.
These three cloud computing service models represent a spectrum of offerings, with IaaS providing the most control over infrastructure and SaaS offering the highest level of abstraction and simplicity for end-users. Organizations can choose the service model that best aligns with their specific needs, resources, and expertise.
How are cloud services hosted and delivered?
Public Cloud. Services are offered to the general public by cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Resources are shared among multiple customers.
Private Cloud. Cloud infrastructure is exclusively used by a single organization. It can be hosted on-premises or by a third-party provider. Private clouds offer more control and customization options.
Hybrid Cloud. A combination of public and private clouds, allowing data and applications to be shared between them. Hybrid clouds provide flexibility, enabling organizations to leverage the scalability of public clouds while maintaining sensitive data on private infrastructure.
Multi-Cloud. Companies use services from multiple cloud providers to avoid vendor lock-in and exploit each provider's strengths. Multi-cloud strategies often involve managing resources and applications across various cloud environments.
Cloud computing providers
These are some of the most popular and widely recognized cloud computing providers.
Amazon Web Services (AWS)
AWS is one of the largest and most widely used cloud service providers globally. It offers a vast array of cloud services, including computing, storage, databases, machine learning, and analytics
Notable services: Amazon EC2 (Elastic Compute Cloud), Amazon S3 (Simple Storage Service), AWS Lambda, Amazon RDS (Relational Database Service).
Website: AWS
Microsoft Azure
Azure is Microsoft's cloud computing platform, providing a comprehensive suite of cloud services, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS).
Notable services: Azure Virtual Machines, Azure App Service, Azure SQL Database, Azure AI and Machine Learning.
Website: Microsoft Azure
Google Cloud Platform (GCP)
GCP offers cloud services for computing, data storage, machine learning, and data analytics. Google's expertise in data and AI is a standout feature of GCP.
Notable services: Google Compute Engine, Google Kubernetes Engine (GKE), BigQuery, Google Cloud AI Platform.
Website: Google Cloud
IBM Cloud
IBM Cloud provides cloud computing and AI services with a focus on hybrid and multi-cloud solutions. It offers a variety of cloud deployment options, including public, private, and on-premises.
Notable services: IBM Virtual Servers, Watson AI services, IBM Cloud Object Storage, Red Hat OpenShift on IBM Cloud.
Website: IBM Cloud
Oracle Cloud
Oracle Cloud offers cloud infrastructure and services, including databases, applications, and cloud-native technologies. It is designed to support enterprise workloads and applications.
Notable services: Oracle Cloud Infrastructure (OCI), Oracle Autonomous Database, Oracle Cloud Applications.
Website: Oracle Cloud
Alibaba Cloud
Alibaba Cloud is a leading cloud service provider in Asia and offers a wide range of cloud computing services, data storage, and AI capabilities.
Notable services: Elastic Compute Service (ECS), Alibaba Cloud Object Storage Service (OSS), Alibaba Cloud Machine Learning Platform.
Website: Alibaba Cloud
Salesforce (Heroku)
Salesforce provides a cloud-based platform known for its CRM solutions. Heroku, a subsidiary of Salesforce, is a cloud platform for building, deploying, and managing applications.
Notable services: Salesforce CRM, Heroku Platform as a Service (PaaS).
Website: Salesforce, Heroku
made BI even more important, focusing on predictive analytics, real-time data, and AI-powered insights.
Responsibilities and day-to-day tasks of a BI consultant
A BI consultant helps businesses understand their data and use it to make smarter decisions. Unlike other roles, a BI consultant specializes in turning complex data into clear, actionable insights. They don’t just collect or analyze data — they connect the dots to show what’s working, what’s not, and what can be improved.
BI consultants provide a “big picture” view of the business using data. For example, they can show which products bring the most profit or where the company is losing money.
They turn raw numbers into easy-to-understand visuals like dashboards and reports, helping C-level management make decisions.
They spot hidden trends or inefficiencies and help companies save time, money, and resources.
BI consultants forecast trends based on historical data, which helps businesses plan ahead and stay competitive.
Other roles, like analysts or developers, may focus on gathering data or creating tools, but a BI consultant bridges the gap between data and business strategy. Among their responsibilities are data analysis and reporting, system development, data integration, performance monitoring, client collaboration, training, and support.
BI consultant’s day-to-day tasks
Meeting with stakeholders to discuss business goals and data requirements.
Designing data models and visualizations.
Troubleshooting and optimizing BI systems.
Writing SQL queries or working with ETL tools to integrate and process data.
Analyzing trends to provide strategic recommendations.
Documenting processes and ensuring data quality.
Senior-level BI consultants have a bit different responsibilities. For starters, they include leadership – Senior BI consultants are expected to lead BI projects, manage junior consultants, and coordinate across teams. Employers also expect more strategic insights and high-level recommendations based on complex data analyses. Senior BI consultants will also handle executive-level communication, align BI initiatives with company goals, develop large-scale, scalable BI architectures, and ensure the accuracy and integrity of BI tools and systems.
How do senior BI consultants differ?
Senior consultants handle more complex datasets and advanced BI tools, so they have a deeper expertise in business intelligence. They also play a leadership role as they mentor teams and oversee project timelines. Senior consultants can drive decisions at the enterprise level and often interact with C-level executives, presenting insights and strategies at a higher level. In other words, senior BI consultants take on broader responsibilities.
When does your business need a senior BI consultant?
A senior BI consultant is an expensive specialist who tackles complex challenges. How to make sure that our business will benefit from an expert of such a level? Here are five needs a senior BI consultant can cover:
You have strategic growth plans. If you need data-driven strategies for market expansion or new ventures. For example, you’re a company expanding globally that needs insights into regional markets and customer behavior.
You have a data overload. When your data is complex or unmanageable, getting actionable insights becomes hard. For example, you’re an e-commerce platform that struggles to analyze customer purchasing patterns across millions of transactions.
Your decisions are inefficient. You might have a problem if your current BI processes don’t support real-time decisions. For example, you’re a logistics company that needs predictive analytics to optimize delivery routes.
Your BI systems underperform. Think of hiring a senior BI specialist if your BI tools fail to deliver the expected ROI or insights. For example, if your retail chain’s dashboard is outdated and can’t handle modern analytics needs.
You have advanced analytics needs. You might need an expert if you’re integrating AI, ML, or advanced forecasting models. For example, you’re a healthcare firm that wants predictive analytics for patient care and resource allocation.
BI consultant salary
Salaries for BI consultants are quite high and, as always, depend on many factors, with the location of your specialist being the most influential one. Check out some average annual salaries for mid-level and senior BI consultants in different regions:
United States: $101,000 for a mid-level BI and $120,000 for a senior-level specialist
Canada: $83,000 for a mid-level and $94,000 for a senior BI
United Kingdom: $67,000 for a mid-level and $92,000 for a senior BI
Israel: $69,000 for a mid-level and $77,000 for a senior BI
Germany: $53,000 for a mid-level and $73,000 for a senior BI
India: $9,000–$14,500 for a mid-level and $14,500+ for senior BI
Vietnam: $21,000 for a mid-level and $28,000 for senior BI
Poland: $26,000 for a mid-level and $34,000 for senior BI
Ukraine: $21,000 for a mid-level and $28,000 for senior BI
With staff augmentation, though, you don’t have to limit your choice of talent to your location. Hire the best specialists from any country you want and save up to 60% on direct labor costs!
