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There’s a common misconception that business intelligence vs. data analytics means the same thing.
Are they terms for the same concept? Or different but related terminologies.
Here’s the main distinction:
Data analytics is the broad field of using data and tools to make business decisions. Conversely, Business intelligence (BI) refers to the procedural and technical infrastructure that collects, stores, and analyzes the data produced by a company’s activities.
It’s a broad term encompassing data mining, process analysis, performance benchmarking, and descriptive analytics.
Some of the charts you can use in business intelligence vs. data analytics include:
Google Sheets is one of the go-to popular data visualization tools among professionals and business owners worldwide.
But the visualization tool has pretty basic Box Plot, Radar, and Scatter Plot charts. And this implies you’ve got to invest extra effort and time to edit the charts to align with your needs.
You can download and install a specific add-on in Google Sheets to access ready-to-use charts (highlighted above).
In this blog, we’ll address the following:
Before addressing the question above, we’ll address the following: what is business intelligence?
BI (Business Intelligence) are processes, architectures, and technologies that convert raw data into actionable insights.
You can use it to transform data into actionable intelligence and knowledge. Business intelligence directly impacts your business’s strategic, tactical, and operational decisions.
Besides, it supports fact-based decision-making using historical data rather than assumptions and gut feelings. BI tools perform data analysis and create reports, summaries, dashboards, maps, graphs, and charts to provide users with detailed intelligence.
We’ll provide the in-depth distinction between business intelligence vs. data analysis in the coming section. Also, we’ll address the following question: why is business intelligence important?
Check out below the benefits of business intelligence vs. data analysis.
An effective Business Intelligence system serves to identify key patterns and trends in your business.
You can use the methodology to understand the implications of various organizational processes and changes, allowing you to make informed decisions and act accordingly.
Business intelligence can help streamline efficiency and productivity and can potentially increase revenue.
You can leverage the BI to:
Sales and marketing teams use the Customer Relationship Management (CRM) application to track customers along the funnel.
CRMs house all customer communications and interactions, which means they’re a wealth of data and information.
Business Intelligence (BI) can help you understand what your competitors are doing and adjust. Gathering intelligence from your niche rivals cuts the learning curve and other costs associated with testing.
In the coming section, we’ll address the following: how business intelligence works.
Business intelligence data is stored in warehouses built for individual departments and business units, often tied to an enterprise.
In addition, data lakes based on Hadoop clusters or other systems are used as repositories or landing pads for analytics data, especially for log files, sensor data, text and other unstructured or semi-structured data.
BI data can include historical information and real-time data gathered from source systems to support strategic and tactical decision-making processes.
Before it’s used in BI applications, raw data from different source systems is integrated, consolidated, and cleansed using quality management tools for reliable insights. To drive growth, especially in today’s competitive landscape, you need an in-depth understanding of business intelligence vs. data analytics.
In the coming section, we’ll address the following: example of business intelligence.
Whether you run small or medium-sized businesses (SMBs) or enterprises, you need intelligence to make your strategic moves. And this entails surveilling what your niche rivals are doing to shorten your learning curve.
At the center of business intelligence vs. data analytics, there’s visualization.
Visualization is displaying insights into data using visual images, such as charts, graphs, and maps. More so, it makes the data more natural for our brains to interpret. You can easily uncover trends, patterns, and outliers that exist in your data using charts and graphs.
Not long ago, the ability to create smart data visualizations was a nice-to-have skill. For the most part, it benefited design and data-minded businesses.
That’s changed.
Data visualization is a must-have skill for all businesses because it’s the only reliable way to predict (or mitigate) risks and capitalize on opportunities.
Decision-making increasingly relies on data, which comes at an overwhelming velocity, and in such gigantic volume. You can’t comprehend it without some layer of abstraction, such as charts, graphs, and maps.
In the next section, we’ll address the following question: what is data analytics?
Analytics are all activities related to investigating data for hidden answers.
The primary goal of the data analytics field is to make it easy for other stakeholders to access and understand insights.
You’ll agree when we say raw data has no value. Instead, it’s what you do with it that provides value.
In other words, it’s all the steps you take, both human and machine-enabled, to discover, interpret, visualize, and tell the story of patterns in your data.
Data analytics can help you:
It’s worth noting that the techniques and processes of data analytics have been automated by emerging technologies, such as artificial intelligence (AI) algorithms.
You might have got the idea about difference between business intelligence vs. data analytics but keep reading you will come to know more about it. Also, we’ll address the following question: why is data analytics important?
Data analytics supports both prediction and knowledge discovery capabilities.
You can easily gain insights into the current state of the business or process and forecasts reliably.
Knowing what customers want beforehand makes marketing campaigns more targeted.
You can easily customize your brand communication to target a segment of your entire customer base. Also, you can easily determine the segment of the customer base that will respond positively to the campaign.
Data analytics can identify potential opportunities to streamline operations or maximize profits.
Besides, you can easily identify potential problems and eliminate the process of waiting for them to occur.
In the coming section, we’ll address the following: example of data analytics.
Imagine you’re a marketer running an online ad campaign to promote a new smartphone.
You might start by targeting the ad to people who bought the previous version of the phone. You gather immense data about clicks, engagements, and conversions as your campaign runs.
The tools you choose to work with can decide whether you enjoy the benefits of business intelligence vs. data analytics or not.
Google Sheets is one of the trusted data visualization tools within business intelligence and analytics circles. You can use the spreadsheet tool’s graphs and charts to visualize your data. But Google Sheets has very basic graphs that require much work and time in tweaks and edits.
To create a compelling data story, you need charts that are easy to interpret. So, you’ve got to think beyond Google Sheets to succeed in business intelligence vs. analytics.
We suggest you supercharge your Google Sheets with third-party applications (add-ons) to access: ready-made, easy-to-interpret, and visually appealing charts.
The add-on we’ve tested and has provided remarkable results is ChartExpo.
ChartExpo is an add-on you can easily install in your Google Sheets to access insightful, ready-made, and visually-appealing charts.
Unlike other data visualization-based add-ons, ChartExpo does not require coding or programming skills.
Furthermore, it has a super-friendly user interface for everyone, irrespective of their computer skills. Secondly, it has over 50 ready-to-use charts you’ll never find in your Google Sheets.
ChartExpo produces charts that are incredibly easy to read and understand. Another thing we’ve noted with this tool is that it does not slow down your browser.
Below are some of the charts you can find in ChartExpo
The Box Plot simplifies bulky and complex data sets into quartiles and averages. Also, you can use the chart to pinpoint outliers in your data. Also, the chart segments key variables in quarters or (quartiles).
For instance, you can draw boxes to connect the first quartile to the third quartile.
Whiskers are lines that identify values outside of the average data points. Your data’s highest and lowest variables can be outliers, depending on their magnitude and frequency of occurrence.
Keep reading because we’ll address the difference between business intelligence vs. data analytics in the coming section.
A Scatter Plot is a visualization design that uses Cartesian coordinates to display insights into varying sets of data.
More so, it uses dots to display relationships between variables.
The Scatter Plot communicates insights using dots or markers between x and y-axes. Essentially, each of the chart’s dots appears “scattered”, hence its name. Scatter Plot can determine the causal effect relationship between key data points.
For instance, you can use the visualization design to track the relationship between profits and employees’ training in your business.
The Scatter Plot Correlation Graph communicates insights using dots or markers between its x and y-axes.
A Radar Chart is a two-dimensional chart you can use to display two or more key variables on an axis that starts from the same point.
The chart is straightforward to understand and customize. Furthermore, you can show several metrics across a single dimension.
Radar charts are best used for showing outliers and commonalities in your data. You can use Radar Chart Excel to display performance metrics, such as clicks, sessions, new users, and page views.
Also, the graph can help you to display insights into different data points on a radial axis. The visualization design is often used to compare multivariate data sets.
You can plot the chart in a Cartesian plane where the x-axis is wrapped around the perimeter.
This section will use a Box and Whisker Plot to visualize the data below.
| Departments | Age |
| Development | 34 |
| Development | 33 |
| Development | 32 |
| Development | 30 |
| Development | 29 |
| Development | 21 |
| Development | 38 |
| Development | 29 |
| Development | 37 |
| Development | 34 |
| Finance | 37 |
| Finance | 53 |
| Finance | 22 |
| Finance | 47 |
| Finance | 39 |
| QA | 46 |
| QA | 35 |
| QA | 51 |
| QA | 44 |
| QA | 41 |
| QA | 53 |
| QA | 41 |
| Graphics | 32 |
| Graphics | 35 |
| Graphics | 42 |
| Graphics | 28 |
| Graphics | 58 |
| Graphics | 31 |
| Graphics | 37 |
| Graphics | 38 |
| Training | 29 |
| Training | 43 |
| Training | 48 |
| Training | 48 |
| Training | 55 |
| Training | 38 |
| Training | 37 |
| Training | 26 |
| Training | 31 |
| HR | 30 |
| HR | 23 |
| HR | 29 |
| HR | 29 |
| HR | 51 |
| HR | 50 |
| HR | 27 |
| HR | 37 |
| HR | 30 |
Note how it’s challenging to draw meaningful insights from the tabular data (above).
To visualize the tabular data above, follow the simple steps below:
You will see add-on on the right section.
Let’s have more understanding about the difference between business intelligence and data analytics.
Besides, it’s implemented only on historical data stored in the warehouses.
After all, why look at analytics if you don’t intend to use them to take action to enhance future outcomes? Prescriptive analytics, however, rises above BI into the realm of data analytics.
Where do we draw the line?
On the other hand, data analytics requires a higher level of mathematical and statistical expertise. Data scientists take big data sets and apply algorithms for in-depth insights.
It relies on algorithms, simulations, and quantitative analysis to determine relationships, trends, and comparison insights. That doesn’t happen with BI.
The answer to any given question is viewed only once and used to inform the next question in solving a problem.”
Data Analytics are all activities related to investigating data for hidden answers.
Its key goal is to make it easy for other stakeholders to access and understand insights. It’s all the steps you take, both human and machine-enabled, to discover, interpret, visualize, and tell the story of patterns in your data.
Business Intelligence is made up of processes, architectures, and technologies that convert raw data into actionable insights.
You can use it to transform data into actionable intelligence and knowledge. Business intelligence directly impacts your business’s strategic and tactical decisions. Besides, it supports fact-based decision-making using historical data rather than gut feeling.
There’s a common misconception that business intelligence vs. data analytics means the same thing.
Data analytics is the broad field of using data and tools to make business decisions. Conversely, Business intelligence (BI) refers to the procedural and technical infrastructure that collects, stores, and analyzes the data produced by a company’s activities.
It’s a broad term encompassing data mining, process analysis, performance benchmarking, and descriptive analytics.
Google Sheets is one of the go-to popular data visualization tools among professionals and business owners worldwide.
We recommend our readers to use the ChartExpo add-on to supercharge their Google Sheets.
ChartExpo has different ready-made, insightful, and visually appealing charts. Besides, it has an intuitive interface, which means you don’t need coding or programming skills to visualize your data.
Sign up for ChartExpo today to access ready-made Box Plot and other charts for business intelligence and data analytics.
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