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Data Analytics & Business Intelligence

Business Intelligence vs Predictive Analytics: Key Differences Explained

Business Intelligence vs Predictive Analytics: Key Differences Explained

Business intelligence and predictive analytics are vital in business development and performance. Both increase the outcome capabilities and efficiency of the business. But are they both the same? Do they have the same purpose, methodology, tools, or technologies? 

Business intelligence vs. predictive analytics have significant differences with respect to various aspects. Let’s look at this comparative guide to understand these fields and know the difference between them.

What is Business Intelligence?

Business intelligence, or BI, is a technical method of examining data and deriving meaningful insights. These driven insights are helpful for executive managers and stakeholders of business to make crucial decisions. 

BI is a software program that offers easy and accurate conversion of statistical data into actionable insights. The data are analyzed, and the report is presented with dashboards, graphs, charts, summaries, and target points.

Furthermore, BI collects, stores, analyzes, and interprets business data. These are done through extensive techniques like data mining, data visualization, descriptive analysis, and performance benchmarking. This way, an unstructured form of raw data is converted into a structured form, which becomes crucial for upper-level management of businesses for further decisions.

Why Use Business Intelligence?

Business intelligence offers many benefits to the users. It aims to provide better and actionable insights to facilitate strategic planning and increase the operational efficiency of the departments. It has online analytical processing (OLAP), known for its component, which includes multi-dimensional analytical queries. 

Business executives can apply these techniques and processes to their business model and optimize their revenue streams. Accurate conduct and decisions have a long-lasting effect on the customers and the market. Furthermore, you can learn Writing Analytical Queries for Business Intelligence with CCSLA and integrate BI into your business. 

Business Intelligence Tools


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BI tools help in faster analysis, increased competitive advantage, more governed data, and better efficiency. You can get the following BI tools to maximize their benefits: 

  • Tableau
  • Qlik Sense
  • Sisense
  • Looker
  • Microsoft: Power BI 
  • SQL Server
  • SAP business objects
  • Zoho 

What is Predictive Analytics?

Predictive analytics comes under business intelligence, where it carries out the prediction and forecasting process of business models. It analyzes raw data and prepares its outline based on forecasting for future reference and decision-making. Advanced and complex data are arranged in a pattern, and then these patterns are further analyzed with different models to prepare reports that align with the business objectives. 

Data scientists usually carry out this process by preparing different models like forecast, classification, time series, outlier, and clustering models. These models are forecasted, and suggestions are made based on the history of customer preferences and service feedback. Predictive analytics are often paired up with big data and machine learning. 

Why is Predictive Analytics Important?

Predictive analytics have many reliable solutions that help businesses to save time and money. It allows businesses to target the right point and take the right approach. Many retailers and hotel restaurant managers use predictive analytics to determine their prices based on the historical pattern. This way, they can opt for a better solution and maximize their profit. Furthermore, once infused into your workflow, predictive analytics automatically guides you to choose the right course of action and achieve your target goals. 

Critical Elements of Predictive Analytics

You need to consider its essential elements to roll out successful predictive analytics. Follow the given tips to use predictive analytics for your business effectively: 

  • Choose the suitable sources: Choosing suitable data sources to analyze is very important. You can get your relevant data from the business or generate raw data by yourself. 
  • Clean data: Once you obtain data, build a model by incorporating data and sorting them in the most relevant form. List the data as per the problems so that it gives better solutions. Scrub off the inaccuracies, formatting errors, and duplicate entries. 
  • Automation and machine learning: Large datasets need to be sorted and made small for consideration. Use automation features on predictive tools to get the right results. Use your machine-learning skills to command automation. 
  • Build in terms of business objectives: While building a model, align the solution with business objectives and goals. 

Predictive Analytic Tools

Following are some most popular predictive analytics tools that you can use: 

  • R programming
  • Cloudera
  • Apache Spark
  • Tableau
  • Microsoft HDInsight
  • Splunk
  • SAS
  • Excel 
  • QlikView

Business Intelligence vs Predictive Analytics: Comparison Table

BasisBusiness Intelligence Predictive Analytics 
Focus BI focuses on descriptive analysis and visualization to derive meaningful information for the executives and stakeholders. Predictive analytics discovers patterns and trends behind data and then those algorithms are used to predict future outputs. 
Data typesUses structured data from different sources. Uses both structured and unstructured data. 
Emphasis Emphasizes the reporting and visualization process. Involves advanced and complex statistics data and algorithms. 
User skillsHelps in data exploration and self served analysis. Requires programming knowledge and expertise. 
PurposeSupports strategic planning, monitoring, and helps in the decision-making process. Finds patterns and trends in data, sets correlation and outliers in them for further analysis. 
Tools Tableau, Qlik Sense, Sisense, Microsoft: Power BI, SQL Server, etc.  R programming, Spark, Splunk, Excel, QlikView, etc. 
Skill requirementData analysis, problem-solving, communication skills, data visualization, interpretation, critical ability, etc. Statistical analysis, algorithm knowledge, forecasting, predictive modelling, machine learning, model evaluation, etc. 

Fundamental Differences Between Business Intelligence vs Predictive Analytics


To have a better in-depth understanding of business intelligence vs. predictive analytics, fundamentals need to be precise.

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1. Role 

Business intelligence is a branch that follows descriptive analysis. The data is analyzed in BI, and strategies are planned for the operation’s effectiveness. 

Predictive analytics uses data for statistical algorithms and forecasting. It poses a query list and then works on it to solve it. With quantitative data and algorithms, future data patterns are identified. 

2. Data Analysis

BI processes raw data and performs different processes on it to clean data. Structured data from internal sources are collected from financial systems or ERPs. These data are utilized for the BI process.

Predictive analytics leverages both unstructured and structured data. It can be from various sources: social media, IoT devices, or external drives. Instead of generating reports, predictive analysis generates forecasts for product launches, talent acquisition, or sales cycles. 

3. Purpose

The purpose of business intelligence is to provide relevant information to businesses for their efficient performance. It aims to facilitate better insight into the decision-making process. Learn to Analyse and Visualise Data using Tableau with the CCSLA course. 

Furthermore, predictive analytics aims to anticipate future occurrences with better solutions. It assists in problem-solving and uses forecasting to identify future patterns from past trends analysis. 

4. Insights

Companies often use BI to gain effectiveness in their business operations. It strengthens the current performance, situation, policies, and practices. The insights from the BI process allow upper-level management to make significant decisions for the business.

Predictive analytics detects ongoing or past patterns and helps data scientists derive new solutions with better-mitigated risks and efficiency. It yields potential scenarios and possibilities of future events. 

5. Tools and Technology

BI uses descriptive and statistical methods with data visualization techniques to analyze data and derive actionable insights. BI follows the latest technology, which makes the entire process accurate and fast. The latest tools of BI have made it more efficient. Tools like SAS, Tableau, and BI Power prepare presentations and readable visualization with charts, groups, and dashboards. 

Predictive analytics relies majorly on technologies like data mining and statistical algorithms. This transforms the data and forecast models with algorithms for future reference. The predictive analysis tools make the process far more accessible. Tools like Splunk, Excel, and QlikView are some frequently used software that helps in better analysis. 

7. Strategic Value

Both BI and predictive analytics offer strategic value to businesses. However, business intelligence and predictive analytics both have different outcomes and approaches. BI is used for the optimization and performance management of businesses. Companies usually employ such tools to observe growth and success metrics. Furthermore, predictive analytics helps companies identify a strategically driven path ahead. It contributes to risk management by providing information on future trends and preferences. 

8. Business Performance

BI tools offer a highly intuitive interface with report-making, dashboarding, and data-handling features. This enhances the performance of business, and it becomes easy to interpret big data. Executives get insights from BI and implement the solutions in their operations to increase efficiency and maximize the profitability of the company. 

Predictive analytics allows users to interpret higher levels of algorithms and machine learning. With various predictive tools, analysis becomes easy and convenient. A forward-looking perspective is developed with this process. Business uses the outcomes of these in their business along with BI to get maximum benefits. 

How BI is Used Across Different Industries?

BI helps businesses identify patterns and derive insights based on data. It uncovers the hidden factors that are essential for companies to know. Today, business intelligence is used in an ample space in the businesses. It has a wide range of applications in the industry. 

Enterprises use it across every department to gain effectiveness and efficiency in their performance. Let’s see how business intelligence and predictive analytics are used across industries. 

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Sales and Marketing 

The most popular field where BI is used at a high pace is sales and marketing. BI tools help in analyzing customer preferences and sales performance. It allows businesses to optimize marketing campaigns. With customer data analysis, companies set their target segments, marketing messages, customer acquisition strategies, and customer retention techniques. 

Finance and Accounting

BI tools not only limit themselves to sales or marketing, they also extend to the finance and accounting industry, Businesses can track financial performance, generate reports, and maintain cash flows. Real-time access to financial data helps companies make more informed and ideal decisions. Finance managers use BI tools to manage risks and the business finances appropriately. 

Supply Chain Management

Supply chain management is another field where BI plays an essential role. Businesses optimize their supply chain processes by collecting and analyzing various data related to inventory, production, and cost. Overall operational efficiency is maximized by better planning and setting KPIs to measure the improvement. 

How is Predictive Analytics Used Across Industries?

Predictive analytics follows retrospective analysis that provides insights based on past trends and patterns. The data extraction and prediction to build models make it ready to integrate into the organization’s policies and practices. 

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Maximize Sales Performance

Predictive analytics is a leading industry software that provides better assistance to executives in major decision-making. It works as a world-class sales team by reducing the usage of resources that are draining and use of elements that are abundant and preferred by customers. The recruitment drives and hiring practices are also made a lot easier with this tool. 

Retail and Market

With the right intelligence, businesses can maximize their workflow and gain the advantage of the right move. The predictive analytic process helps them in marketing, advertising, and optimizing inventory based on historical data and consumer’s past preferences. 

To get real-time experience in handling projects and Microsoft, you can learn Data Engineering on Microsoft Azure and implement the learning in these industries.  

Logistics, Manufacturing, and Supply Chain 

Manufacturers often need help optimizing and maintaining track of their revenues, especially inventories. Predictive analysis helps them tackle this problem and provides solutions to manage their inventory, production, and material stocks accurately. 

The transport lines, supply chain, and logistics help are kept on track and controlled better. Furthermore, feedback and reviews are considered to improve planning and design. 

Human Resources

Companies need the best and most talented employees, which is ensured by the HR of the businesses. The HR department makes use of predictive analysis to get well-versed and talented professionals for the company. The analytics help them to approach selected candidates and inform them about the policies and benefits. 

Healthcare 

After the pandemic, healthcare has started using different tools to manage and maintain records effectively. Predictive analytics helps businesses in healthcare to model diseases’ causes and effects. The hospital facilities are utilized and modified by analyzing the demand and patient requirements. 

Bottom Line 

Though there is a vast difference between business intelligence and predictive analytics, both have similar objectives to serve business goals. New insights are gained by these methods so that businesses can implement and maximize their operational efficiency and revenue. You can leverage any of this analysis per your business requirement to make better decisions and lead a strategic advantage over your competitors. 
Many data scientists and analysts work with businesses to help them grow and prosper. To get comprehensive knowledge of data analytics and become a professional analyst, you can get the CCSLA Data Analytics & Engineering Bootcamp program. It offers expert skill development, industry networking, innovative solutions, comprehensive learning, and 24-hour support on your journey.

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