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What is Behavioral Analytics?
What is Diagnostic Analytics?
The Difference Between Data Analytics and Statistics
Data Analytics vs. Business Analytics
What is Data Analytics?
The Difference Between Data Analytics and Data Visualization
Data Analytics vs. Data Science
Quantitative vs. Qualitative Data
Data Analytics Processes
Data Analytics vs. Data Analysis
Data Analytics Lifecycle
Data Analytics vs Business Intelligence
What is Descriptive Analytics?
What Is Google Analytics 4 and Why Should You Migrate?
Google Analytics 4 and eCommerce Tracking
GA4 Migration Guide
Understanding Data Streams in Google Analytics 4
GA4 vs. Universal Analytics
Understanding Google Analytics 4 Organization Hierarchy
Benefits and Limitations of Google Analytics 4 (GA4)
What are the New Features of Google Analytics 4 (GA4)?
What Is Customer Data?
Collecting Customer Data
Types of Customer Data
The Importance of First-Party Customer Data After iOS Updates
CDPs vs. DMPs
What is an Identity Graph?
Customer Data Analytics
Customer Data Management
A complete guide to first-party customer data
What Is a Customer Data Platform?
Customer Data Protection
Difference Between Big Data and Data Warehouses
Data Warehouses versus Data Lakes
A top-level guide to data lakes
Data Warehouses versus Data Marts
Best Practices for Accessing Your Data Warehouse
What are the Benefits of a Data Warehouse?
Data Warehouse Architecture
What Is a Data Warehouse?
How to Move Data in Data Warehouses
Data Warehouse Best Practices — preparing your data for peak performance
Key Concepts of a Data Warehouse
Data Warehouses versus Databases: What’s the Difference?
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
How to Create and Use Business Intelligence with a Data Warehouse
How do Data Warehouses Enhance Data Mining?
Data Security Strategies
How To Handle Your Company’s Sensitive Data
How to Manage Data Retention
Data Access Control
Data Security Technologies
What is Persistent Data?
Data Sharing and Third Parties
What is Consent Management?
What is PII Masking and How Can You Use It?
Data Protection Security Controls
Data Security Best Practices For Companies
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Data Analytics vs Business Intelligence
You have probably come across the terms business intelligence and data analytics. Although some people confuse the two terms, and business intelligence is sometimes considered to encompass data analytics, there are significant distinctions between the two.
Business intelligence (BI) used to be exclusively done by SQL experts, producing status reports for the business. The field has since evolved to include data analytics. This is partly because of the demand for regular dynamic reporting and analysis, but also because most company data is now stored in the cloud — in data warehouses and on customer data platforms (CDPs) — and these systems can be easily administered by other staff such as data analysts. Understanding the difference between data analytics and business intelligence is integral to running a profitable, modern business that utilizes data in a meaningful way.
Adopting both BI and data analytics techniques is a great way to gain a greater understanding of the day-to-day running of your business, and to improve business decision-making.
What is data analytics?
Data analytics is a process that’s centered around data analysis and answers specific, well-defined questions about a business. This involves creating ongoing reports and predictions, by designing and creating an automated system for consuming and monitoring data. As well as doing data analysis, data analytics also includes collecting and preparing data, visualizing and communicating the results of the analysis, and setting up the automated systems for producing regular reporting.
There are four types of data analytics: descriptive, diagnostic, predictive, and prescriptive. They can each answer different types of questions.
Descriptive analytics is the most basic type of analytics and describes particular events that happened, such as when streaming services determine which programs are trending in order to list them on their home screen.
Diagnostic analytics can be used to find out why something happened: for example, why customers are canceling their subscriptions to a service.
Predictive analytics can be used to predict what is expected to happen in the future. For example, a marketing department may rely on predictive analysis to forecast trends, which can help them work out the best time to run a campaign.
Finally, prescriptive analytics is the most complex type and uses advanced statistical modeling and machine learning to work out what a company should do next. Predictive and prescriptive analytics is often done by data scientists, but they can still be considered part of data analytics.
What is business intelligence?
In its simplest form, business intelligence is the process of collecting data from various business operations, storing it, and analyzing it. The primary purpose of BI is to understand the overall direction and operations of the company and to support better business decision-making using data. It achieves this by producing reports for managers to help them with their decisions. These reports can give information about what’s happening inside the business, but may also focus on external factors that affect the business – for example, they may produce an analysis of a market within which they want to operate.
BI usually involves explaining the reasons behind a business's past performance as well as reflecting on the overall growth of operations. It uses concrete data to give insight into business records, which allows business officials to efficiently evaluate the overall journey and where the company is heading. Business intelligence is also often tasked with playing through different scenarios to assist business decision-making. For example, it might attempt to answer questions like “what is the likely effect on signups if we raise prices?”
Historically, business intelligence involved manually producing reports for a business. However, as stakeholders would request regular reports on a monthly or quarterly basis, producing the exact same reports became quite tedious for the BI analysts. Modern BI has now evolved to rely on automated regular reports, which often come from in-house data analytics processes, making data analytics part of business intelligence.
The difference between data analytics and business intelligence
The purpose of both data analytics and business intelligence is to support the decision-making process and thereby grow the company. However, each method does this in different ways. Although data analytics is a crucial part of business intelligence overall, there are key differences between these terms.
- Frequency of reports: BI has historically involved answering one-off questions, whereas data analytics involves answering the same question on a regular basis. This could include things like monthly reports on whether signups have gone up or down.
- Scope: BI is about trying to understand the overall direction and operations of the company, whereas data analytics within a company is about answering specific questions, so is smaller in scope. A BI analyst usually has a higher salary than a data analyst, possibly because the role requires high-level business knowledge.
- Temporal focus: Data analytics includes predictive and prescriptive analytics, which is more complex and is focused on the future, whereas BI tends to just focus on descriptive data, having a more historical focus.
- Technical skills: A data analyst tends to have more technical skills than a BI analyst (advanced SQL, statistical analysis, and some programming skills such as Python and R), whereas the BI analyst is often a more holistic expert on the business and may even research data outside of a company to better understand the market and business context.
- Reporting: the reporting and data visualization style will differ depending on the data type and scenario. If you want to forecast future trends based on past data, data analytics reporting is best. In contrast, business intelligence is best if you want to pull large amounts of current data to generate a report.
Business intelligence tools and techniques
There are several tools and techniques that you can use to improve BI in your company. These techniques include:
Data analytics: a valuable business intelligence method that raises hypotheses for the business to explore. Data analytics is now a big part of modern BI, whether it be descriptive, diagnostic, predictive, or prescriptive analytics.
- Real-time monitoring: answering business-related questions in real-time, so decisions can be made instantly. For example, product stock levels need to be known in real-time to avoid selling an item that is not in stock – a monthly report of this information won’t be good enough. Another example is financial traders being provided with information about exchange rates in real-time, which they need to know as this information is constantly changing.
- Data mining: finding patterns in large datasets, using statistical analysis, machine learning, or using simple SQL queries. This can be done by data analysts or data scientists.
- Text mining: transforming unstructured text into structured data so that it’s easier to mine the data for useful information. This tends to be done by data scientists.
- Benchmarking: comparing your business’s processes and performance metrics against industry standards.
- Performance management: a data-driven approach that involves setting business goals, defining key performance indicators (KPIs), and analyzing data to determine whether you are meeting your goals and what is holding you back. Performance management software can be used to allow business leaders to manage this process themselves.
Benefits of BI and data analytics
Data analytics is now a common technique used within business intelligence, and many modern-day business intelligence tools use elements of data analytics. Your business intelligence approach will depend on the business scenario and what your company wants to achieve, and in many cases, companies’ business intelligence approaches include data analytics.
Both business intelligence and data analytics profoundly affect business growth, allowing companies to better understand their audience, sales, and internal business efficiency. Using both together enables you to answer all kinds of different questions: describing the past, predicting the future, or working out what your business should do next.
Traditionally, business intelligence refers to answering one-off business-related questions; however, when data analytics is used for business purposes, it is considered to be part of business intelligence. As cloud computing services grow in popularity, data analysts are now able to provide regular reports and answers to questions to which a BI analyst would have previously provided a one-off answer. There is still demand for answers to one-off questions, and this is still done by a BI analyst, but data analysts can create a pipeline to answer these questions more regularly.
Business intelligence analysts have a holistic focus on the business and anything affecting it, so their focus is wide, whereas data analysts focus on answering specific questions and automating their reports so the same analysis can be run regularly.
Due to the more specific nature of the data analyst's job, they tend to have a deeper understanding of the data and the specific area they answer questions about, and they often have more experience with basic programming and statistics than the business intelligence analyst.
Rather than focusing on the differences and thinking of “business intelligence vs. data analytics,” it is more useful to think of data analytics as a technique that can help business intelligence. Both BI analysts and data analysts often work together collaboratively to deliver important insights to your business.