Business Intelligence vs Data Analytics: Differences, Similarities, and Uses (2024)

Should your company focus on BI, or should you go big with data analytics and invest in a team of professional data scientists?

Business Intelligence vs Data Analytics: Differences, Similarities, and Uses (2024)

What's the difference between Business Intelligence and data analytics? 

Aren't they both used to help businesses leverage data to improve their decision-making? 

Should your company focus on BI, or should you go big with data analytics and invest in a team of professional data scientists? 

By the end of this post, you'll know the answers to these questions.

But first, a brief introduction. 

What is Business Intelligence (BI)?

Business Intelligence (BI) takes all your company's data and uses it to support better decision-making across your organization. It aims to improve and streamline how your leaders decide things as your operations grow larger and more complex.   

Remember, we live in an age where every single block of your business collectively generates an overwhelming amount of data. 

BI lets you make sense of the chaos by providing non-data scientists with an organized and more digestible view of your company's performance. 

What is Data Analytics? 

Data analytics provides methods for making sense of large datasets to generate meaningful information. 

No matter the field, be it business, healthcare, education, and so on, the end goal of data analytics stays consistent: transform raw data into actionable insights to inform decisions.

Data analytics can be broken down into four types — each meant for different objectives:

  • Descriptive analytics — Focuses on recapping old data to describe what happened. It answers the question, "What happened?"
  • Diagnostic analytics — Goes further by finding reasons for past events and trends. It answers the question, "Why did it happen?"
  • Predictive analytics — Creates statistical model and forecasts to predict future outcomes and trends. This answers the question, "What will happen?"
  • Prescriptive analytics — Lastly, this is about discovering the most optimal route forward based on the available data. It answers the question, "How can I make it happen?" 

Each type of data analytics uses different tools and methods, but they all work together to generate insights that can support your company's plans and decisions. 

Data Analytics vs Business Intelligence: What's the Difference?

While there are a lot of parallels between BI and data analytics, it's important to know their fundamental differences.

1. Focus and scope

BI analytics is all about the big picture — enabling top-level business leaders to make strategic decisions using structured data and simpler statistical methods. 

Data analytics, on the other hand, focuses on more specific datasets for the purpose of predicting trends or solving problems. It can work with both structured and unstructured data through complex analysis tools and techniques.

2. Data types

BI mostly uses organized data from a company's own systems, including sales numbers, employee records, financial reports, and so on. These datasets usually come in the form of internal databases, spreadsheets, and data warehouses that are already cleaned and optimized for reporting. 

In contrast, data analytics uses less structured data that don't fit in a single, traditional database. This includes data from external sources like social media, CRM software, website analytics tools, and advertising networks.  

3. Techniques

BI requires simpler toolkits and techniques, such as basic statistical methods, reporting, and data visualization tools. These techniques may include SQL querying and Online Analytical Processing (OLAP), which may be rough around the edges from the backend but smooth and readable to frontend users. 

Data analytics uses more advanced techniques and tools like predictive models and machine learning to uncover deeper insights.

4. Timeframe

In terms of time period, BI primarily looks into historical data to help create better decisions today, which will impact business performance down the stretch.  

Data analytics, however, can utilize both forward-looking and retrospective data. While BI is mainly designed to help analyze past and present performance, data analytics is used to provide businesses with a glimpse of what's ahead: predicting customer behavior, modeling future risks, understanding emerging trends, and uncovering opportunities early.

5. Data sources

BI systems leverage data from consolidated (often internal) systems, like your cloud-based data warehouse and SQL servers. 

Sure, you can compile external data into a single source of truth and build your BI dashboard through integrations. But before data becomes useful for BI, it must be cleaned and structured first for comprehensive reporting.

Data analytics, in turn, don't depend on rigid data workflows and can depend on both internal and external sources. This may include market reports, industry benchmarks, social media campaign reports, and even weather forecasts.  

6. Skill requirements

When it comes to skill requirements, BI is more approachable than data analytics. While you may need business smarts and basic data skills to analyze BI dashboards, datasets should already be translated into readable visualizations and tables. 

But to be successful with data analytics, you're required to have a blend of skills in statistical analysis, data mining, and predictive modeling. Data analytics professionals are also equipped with scripting knowledge to fulfill their responsibilities. 

7. Tools

Perhaps the easiest way to understand the differences between BI and data analytics is to look at the tools used in each. 

For BI, you have enterprise business intelligence and dashboarding platforms like Polymer, Power BI, and Tableau. These are rather easy-to-learn platforms sprinkled with "Quality of Life" features, like report automation, AI data analysis tools, and drag-and-drop report builders.

On the flip side, data analytics uses tools like Project Jupyter, Databricks, Apache Superset, and programming languages like R or Python. While it's possible for non-data scientists to grasp data analytics through self-learning, certain data analytics tools and programming languages may take several months or even years to master.

To summarize, data analytics is about understanding specific trends, patterns, and insights to solve specific problems. BI, on the other hand, is geared toward analyzing business performance to empower future decision-making.

Data analytics is for specialists and data scientists. BI, on the other hand, is for executives, managers, and — with a streamlined dashboarding platform — employees. 

This brings us to the next section.

Consolidate Data Analytics and Business Intelligence with Polymer

BI and data analytics may require different toolsets for specific use cases. But if you have a BI and data dashboarding software that's flexible enough, you can conduct tasks related to both BI and data analytics in one platform. 

Cue in Polymer; primarily a BI platform, but with advanced features for seamless data management and analysis.

First and foremost, Polymer lets you consolidate data from multiple external sources. You can easily import data from Google Analytics, Facebook Ads, Zendesk, Linear, Shopify, Google Sheets, or spreadsheets straight from your computer. 

You can also request a new connector if you're looking to include data from a specific platform. 

While building readable dashboards is a huge part of Polymer, it also offers visualization tools that are useful for data analytics tasks. 

For example, scatterplots and correlation charts let you visualize how one metric or KPI affects another, which is useful if you're conducting predictive analytics. 

Got your attention? 

Here are other powerful data visualization features that can help you turn any dataset into actionable nuggets of insights:

  • Heatmap
  • Time series (timeline) 
  • Line plot
  • Pivot table
  • ROI calculator
  • Outlier chart
  • Dependency wheel

Polymer also incorporates machine learning and AI to help non-data scientists make sense of blended data. 

Perhaps the most intuitive way to do this is through PolyAI — a built-in AI chatbot that turns prompts and questions into ready-to-use, interactive data visualizations. 

Just click 'PolyAI' from the dashboard editor, pop in your question or prompt, and give it a few seconds to generate your data visualization. 

Some of the other ways to use Polymer's AI tools are:

  • Generate instant insights based on relevant data questions. Create useful data visualizations in one click using "Suggested Insights." This allows even those who have zero background in data analysis to utilize raw information in their decision-making. 
  • Get suggestions when creating data visualizations. Not sure how to best configure yoru chart? Polymer's block editor comes with a handy "Suggest for me" button that can automatically configure your data visualization based on what you want to know. 
  • Automate diagnostic, predictive, and prescriptive analytics. Using the "Explain" feature, Polymer can explain data visualizations in layman's terms as well as create predictions and prescriptive actions. The results of this feature may not be as robust as insights from an actual professional analyst, but it's an excellent starting point for non-technical users in helping them learn data analytics.

Even with AI features aside, Polymer still has plenty of tricks up its sleeve. 

You can create custom metrics, set global filters, standardize your data headers, blend data from multiple sources, and more.

Try Polymer Today

Good news — you can see what Polymer is all about for free by creating a trial account here

Experience how a single platform makes both BI and analytics accessible regardless of your data science background. There's absolutely zero coding required to reap the full benefits of BI. 


Posted on
May 3, 2024
under Blog
May 3, 2024
Written by
Saif Akhtar
Growth Manager @ Polymer Search. Passionate about all things Startup, RevOps, and Go-to-Market. Ex-VC and startup accelerator who loves hacking MVPs.

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