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Business Intelligence or Data Analytics: What Is Better for Business?

Business Intelligence or Data Analytics What Is Better for Business

Business Intelligence or Data Analytics: What Is Better for Business?

If you're new to business intelligence (BI) and data analytics or trying to decide if this is the right strategy for your business, it can be challenging to make sense of your options. This blog will help you with a comprehensive overview of BI and data analytics in one place. 

Every day, your business generates an immense amount and diversity of data. To make intelligent decisions, identify challenges, and be financially successful, you require tools to convert your data into precise, actionable insights.  

Business intelligence (BI) and data analytics are data management solutions leveraged to comprehend historical and modern-day data better and generate insights.  

If you are wondering what is the difference between BI and data analytics solutions and which is better suited for your business requirements? This blog covers everything for you to know about business intelligence and data analytics solutions. 

This is a complete guide to understanding the differences between business intelligence (BI) and Data Analytics in 2022. 

Let's dive right in. 

What Is Business Intelligence? 

Business intelligence (BI) is software that consumes business data and showcases it in user-friendly formats such as dashboards, charts, graphs, and reports. BI tools facilitate business users to access diverse categories of data such as semi-structured and unstructured, present and historical, third party, and in-house data sets. Users can analyze this data to have insights into the business performance. 

Companies can leverage the insights gained from business intelligence to enhance business decisions, classify challenges, identify market trends, and find innovative business opportunities. 

What Is Data Analytics? 

Data analytics includes the processes, tools, and techniques of data analysis. It comprises the management, collection, and storage of data sets. Data analytics aims to perform statistical analysis on data to identify trends and resolve issues. It shapes business processes and enhances decision-making to enable business outcomes. 

Data analytics empowers companies to automate decisions, connect intelligence, and take actions. Modern-day tools access, prepare and analyze data to operationalize analytics and track outcomes. 

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The Benefits of Business Intelligence Software 

Business intelligence tools are helpful for organizations to remain competitive and capitalize on revenue streams. Let us explore the benefits of business intelligence software. 

Benefits of Business Intelligence Software

1. Valued business insights 

BI tools assist companies in comprehending what is working and what is not. Businesses can measure employee productivity, revenue, and department-specific performances. Setting precise alerts is helpful for busy executives to remain on top of these metrics and KPIs that are significant for the business. 

2. Enable competitive analysis 

BI software helps with budgeting, planning, and forecasting. It is an effective way to remain ahead of the competition. The approach goes beyond standard analysis and is effortless to execute with a business intelligence (BI) tool. 

3. Identify market trends 

Employees can identify new opportunities and use external market data with internal data to spot recent sales trends by analyzing customer data and market conditions and evaluating business problems through BI software tools. 

4. Increase revenue and expand margins 

Through BI tools, companies can connect to their customer's pain points, explore their competitors, and enhance their operations for a better revenue cycle and expansions in profit margins.  

Challenges with Business Intelligence 

Business intelligence is practical, and its precise utilization assists organizations in enhancing productivity levels. Let's explore the challenges with business intelligence tool usage.  

Challenges with Business Intelligence

1. Data breaches 

The security problems are the most significant challenge faced by BI. If you use BI tools to manage sensitive information, a fault in the process could expose it, hurting your business, clients, and employees. 

2. Higher pricing 

Business intelligence software can be costly. The hardware and IT staff costs are additional. While the likelihood of a higher ROI can validate this, the initial price can be an obstruction to small-sized companies

3. Tough to analyze diverse data sources 

The more you encircle your BI, the more data sources you will leverage. A diverse source can be advantageous in offering enhanced analytics, but systems may have difficulty working across wide-ranging platforms. 

4. Poor information quality 

In this digital world, you have more data than ever. However, this can prove challenging if there is an excess of poor data sets. This means that many BI tools that scrutinize information are of poor quality and can slow down involved procedures. 

5. Confrontation to acceptance 

One of the BI challenges is staff members not wanting to blend it into their current operations. If your organization doesn't accept these systems across all domains, it won't be result-oriented. 

How to select the right business intelligence solution for your business

Benefits of Data Analytics 

The rich diversity of data sets that organizations generate accommodate precious insights, and data analytics is the approach to unlock them. Let's take a look at the benefits of using data analytics. 

1. Predictive Analytics 

Predictive analytics uses historical data to predict future events or outcomes, emphasizing how likely they are to occur. This type of analysis allows businesses to make better decisions about allocating their resources now and in the future, leading to better overall performance. 

Whether a bank is trying to lend more loans or a retail company is running an email marketing campaign that needs to be optimized, predictive analytics can help you make better decisions. 

2. Use of Prescriptive Analytics 

Once you predict probable outcomes, prescriptive analytics assists in regulating those outcomes, which are advantageous to your business in the longer term, it aids you to comprehend how and which variables can be used to accomplish the needed result. This will assist in responding to business and operational transformations while enabling real-time decision-making. 

The significant elements of prescriptive analysis are applied statistics, machine learning, and natural language processing. 

3. Lessen risk and manage setbacks 

Data analytics can assist an organization in comprehending risks and taking protective measures.  

Risks comprise customer or staff theft, missed receivables, employee security, and legal accountability.  

For instance, a business can leverage data analytics to reduce risks and confine losses after a setback. If a company misjudges product demand, it can leverage data analytics to comprehend the optimum price for a clearance sale to trim down inventory.  

4. Improve security 

Companies can leverage data analytics to detect the reasons for past data breaches by visualizing pertinent data. This insight can assist IT teams locate susceptibilities and covering them. For example, the IT department can use data analytics applications to analyze, process, and envisage their audit logs to comprehend the origins of an attack. On the other hand, if you leverage basic BI applications to manage sensitive information, a fault in the process could lead to significant security issues.  

Challenges with Data Analytics 

Enjoying the benefits of data analytics is simpler said than done. Some challenges can obstruct abilities to gather and use analytics. Let's explore the challenges with data analytics. 

1. Lack of talented resources  

The evaluation of diverse data (variety) is critical when a high quantity (volume) of data is being generated every minute (velocity). The immense data flow has built exponential opportunities for data science and data analytics in the marketplace.  

Companies must hire a data scientist with a restricted budget with multidisciplinary competencies who understand data evaluation and even manage business operations.

2. Gaining meaningful insights 

Using data is only as effective as the queries you seek to answer. Competencies are the most significant barriers to generating meaningful insights using big data. The lack of structured data engineering methodologies is the most technical barrier to deriving insights. 

3. Bringing all-embracing data to the data platform 

Loading and transforming the data sets into the data warehouse has sometimes been challenging due to numerous data sources. Here, data engineering skills become vital for data analysts to make data access seamless. 

4. Vagueness of data management 

There are many challenging technologies accessible within every technical aspect, like ETL tools, visualization tools, and technologies such as OLTP/OLAP. There are many choices obtainable to select. However, the challenge is making the top choices and the risk of big data acceptance. 

5. Data storage and swift retrieval 

The data storage and approachability of data generate a requirement to have data lakes and data warehouses that can enable storage, processing, and retrieval of data whenever needed. The real trouble starts when the data lake or warehouse tries to blend unstructured data from diverse sources, which comes across faults in the parallel data processing.  

How the Future Looks for Business Intelligence 

The success of an organization with business intelligence relies on the greater acceptance of this technology by the average user base. Let's explore the prospects of business intelligence. 

Collaborative business intelligence 

Today's BI tools are independently operated by clients and are isolated to a broader network. However, industry specialists predict that the growth of digital business intelligence with BI tools will become more extensive, connected, and collaborative. 

In a prescient ComputerWeekly blog, Brian McKenna discussed this innovation in the business intelligence domain, saying BI will be offered through "shared and immersive analytic experiences." 

Machine learning will steer insight and self-service 

BI software is predicted to become increasingly intuitive. An ML system can use rules and experience to swiftly identify new data, see if present data fits within compliance rules, and grant quick access. 

With ML-driven BI, constraints to "what-ifs" are removed. AI can analyze trends and historical patterns to make informed predictions on your data inquiries. 

These predictive functions will enable better decision-making, addressing compliance in the processes. As David A. Teich explains in a Forbes article, data exploration unlocks the unknowns when requesting data sets not yet accessible. Here, an ML system can speed that procedure, leveraging rules and experience to swiftly find new data sets, check if present data is suitable for compliance and provide prompt access. 

Machine learning will steer insight and self-service 

BI software is predicted to become increasingly intuitive. An ML system can use rules and experience to swiftly identify new data, see if present data fits within compliance rules, and grant quick access. 

With ML-driven BI, constraints to "what-ifs" are removed. AI can analyze trends and historical patterns to make informed predictions on your data inquiries. 

These predictive functions will enable better decision-making, addressing compliance in the processes. As David A. Teich explains in a Forbes article, data exploration unlocks the unknowns when requesting data sets not yet accessible. Here, an ML system can speed that procedure, leveraging rules and experience to swiftly find new data sets, check if present data is suitable for compliance and provide prompt access. 

How the Future Looks for Data Analytics 

Data analytics is predicted to alter the approach drastically we will do business in the future. Let's explore its future aspects. 

Rise of augmented analytics  

Augmented analytics refers to automating insights leveraging natural language processing (NLP). This scenario is the subsequent phase in data analytics.  

It helps to manage multifaceted data sets at scale and enables professionals at all levels to turn data-driven. By bringing data science to a broader group of users, augmented analytics assist in addressing the growing shortage of expert professionals. 

Use of "X" analytics 

X Analytics is a Gartner term wherein X means a data variable, be it structured or unstructured comprising text analytics, audio analytics, and video analytics.  

With AI-steered analytic tools and data visualization, X analytics stands to play a crucial part in envisaging and preparing for future scenarios like deadly diseases and disasters. 

Storytelling and visualizations replacing dashboards 

Self-service BI tools swiftly replace conventional dashboards with new competencies developed to assist users in telling stories with data sets.  

More advanced charts, graphs, and heatmaps are used to showcase contextual insights in an approach that gets professionals to focus on results.  

Graph analytics craft visual representations of explicit relationships that can alter users' approach to correlating between data points. 

Apply augmented data management 

Augmented data management leverages AI and ML to handle metadata and data integrations automatedly. These techniques assist employees to be more performance-oriented, reducing the burden of manual activities and trimming down errors.  

Remember, machine learning is hard to deploy for servers because it differs from regular, easy-to-deploy software. For the same reason, we are making it easier by using MLOps, which studies machine learning and makes it easier to deploy. 

The Future Of MLOps: A Must Read For Data Science Professionals

Examples of How Leading Companies Use BI to Drive their Success 

From top financial companies such as American Express to social media leader Facebook, the most successful organizations across the globe use BI. Here's how some are using BI tools to empower their operations. 

American Express 

American Express has leveraged financial technologies to build new payment service products. The organization's trials in the Australian marketplace have enabled it to identify up to 24% of all Australia-based users who will close their accounts within four months. 

Using that insight, the company takes progressive steps to retain its customer base. BI also assists the company in precisely spotting fraud and safeguarding customers whose card data may be negotiated. 


The leading beverages brand, Coca-Cola, gains from social media data. Leveraging the AI-steered image-recognition technique, the organization can tell when pictures of its drinks are posted online. 

This data, clubbed with the power of BI, offers the organization significant insights into who is consuming their drinks, where they are located, and why they refer to the brand digitally. The insight assists in serving consumers with more targeted ads, which are four times more prospective than a generic ad to lead in a click. 

Examples of How Leading Companies Use Data Analytics to Drive their Success 

Using the power of data analytics helps organizations enhance business operations, trim down costs, improve decision-making and facilitate the launching of more customized products. Here's how some are using data analytics to enable their operations. 


The health and fitness company Fitbit allows its devices to gather data on its user's activities and food consumption patterns. It generates dashboards that users can monitor and track through mobile applications. So, it offers enhanced lifestyle preferences and choices for its users. 


The food giant company Dominos analyses its users' cross-channel and cross-device behavior and even links its consumers' online and offline behavior for data analytics. The company boosted monthly revenue by 6 percent and trimmed advertisement spending by 80 percent year-on-year using these data analytics.  

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Comparison Table: Business Intelligence vs. Data Analytics 

Comparison Basis 


Business Intelligence 


Data Analytics 




BI refers to the insights needed to improve business decision-making. 


When predictive and prescriptive analytics are combined with ML, data mining, and modeling, it leads to game-changing decision-making. 


The objective is to offer support in decision-making and help in business growth. 


The objective is to model, clean, forecast, and transform the data as per the business requirements. 




BI can be enabled by leveraging BI tools. It is implemented on historical data stored in data warehouses and data marts. 


Data analytics can be enabled by leveraging data storage tools. BI tools can also implement. 


Debugging Techniques  In the case of business intelligence, it is probable to debug the mechanism only with the assistance of the historical data provided and as the end-user demands.  


On the other hand, data analytics is debugged as the presented model to convert data sets into a precise and useful format. 


Use Cases  BI tools are used by a wide variety of companies in every industry. The most common use cases include customer analytics, financial analysis, productivity improvements, and business process improvements. 


Data analytics solutions are used in business for fraud detection and prevention, marketing campaign analysis, customer experience improvements, elevating business efficiency and productivity, etc. 


Approximate Cost  As BI solutions prices are often not readily accessible, specialists have pegged the average price at $3,000 every year annually. This is apart from the various versions that service providers may provide. 


Data analytics tools range from free to $ 10,000.00 or more every year, depending on the number of users and business needs. 
Skill Sets needed for business intelligence development 



Execute SQL queries which include design, code, test, and aggregate the outcomes to generate valuable insights.  Execute SQL, perform the export, transform, and load (ETL) procedures, data modeling, and analysis. 


We hope that all the information we've provided in this article has been helpful to you. With so many different things to consider, it can be difficult for companies to know where to start when choosing between BI and data analytics solutions for their business. However, it's important to remember that there's no such thing as a perfect system—each one has its pros and cons, and thus should be used depending on what will serve your company best. 

If you want to implement the ideas described above or seek a more thorough engagement of BI & data analytics solutions, feel free to contact Zuci System's data science and analytics services team. 


Janaha Vivek

I write about fintech, data, and everything around it | Assistant Marketing Manager @ Zuci Systems.