Business Intelligence and Data Analytics Similarities & Differences

Business Intelligence And Data Analytics April 01, 2021

Lauren Christiansen

Writer

Insight Into Business Intelligence and Data Analytics

It is critical to capitalize on big data and analytics to maintain a competitive edge and increase profits. As a business owner, it can be overwhelming to know which tools and technologies to use to make better decisions. Many entrepreneurs have key objectives and goals, but a lack of the right tools make it harder to succeed. In a world where technology evolves by the day, how should an owner move forward?

It's critical to gain a deeper understanding of the various data-related terms that big enterprises talk about. Smaller organizations may think these terms don't apply to them, but they are mistaken. With the right expertise and technologies, both small and large businesses can extract insights that help increase profits, attract new customers, and improve sales.

Read ahead for an overview of the similarities and differences between two critical data-related practices in the workplace.

Business Intelligence and Data Analytics

Experts in the data science field tend to use and throw around a lot of terms. Because many data-related technologies and tools have similar functionality, it's easy to become confused.

For example, business intelligence and data analytics are two important practices in the business world. Unfortunately, many owners don't understand the difference between them. So, what is business intelligence vs. analysis and analytics? How do they differ and how are they similar?

Business intelligence handles all of the real-time big data an organization collects to optimize decision-making and streamline current business operations. BI tools include the strategies and technologies an owner uses to assess the company's current and past performance.

In contrast, owners use business analytics and software solutions to predict a company's future performance. Data analytics helps owners create better business plans to achieve future goals. In summary, BI is more of an assessment of day-to-day operations, whereas BI analytics prioritizes the future health of a company.

Here is a more in-depth overview of BI and BA and how an organization uses them to achieve key objectives.

Data Analytics

A business analyst uses analytics software to assess and understand all of the business data the organization acquires through the data mining process. In today's digital world, data analysis is critical to achieve financial success and make good business decisions.

Data analytics is a broad practice that most data management specialists group into 3 separate categories. These include -

Descriptive Analytics - A data scientist transforms unstructured data into a data visualization, which summarizes historical data. High-level executives use the visualization to make data-driven decisions.
Predictive Analytics - PA uses descriptive analytics to predict customer needs, sales, and other trends.
Prescriptive Analytics - Utilizes descriptive analytics and PA to recommend future actions. Leaders use data insights to mitigate risks, make better products, improve customer service, and increase the bottom line.

Business Intelligence vs. Data Analytics

Business intelligence uses DA to improve decision-making, but not all organizations use data analytics.

Some companies prefer to see how they operate in real-time, and use historical information to make their own predictions. BI is better for leaders to monitor daily activity. Other differences include -

1. BI is Required Data vs. DA Modifies Data

BI handles data sets that an organization needs to make decisions and generate strategies. Data analysis uses predictive modeling and other tools to modify existing analytics data. It is more meaningful and tells a bigger story than BI data alone.

2. BI Plan of Action vs. DA Digestible Information

BI uses the actionable insights from any data analytics data and suggests a course of action. DA takes any findings from raw data and makes it understandable to business users. The way an analyst presents any of these findings should align with business requirements.

3. BI vs. DA Example

To understand how both BI and DA work, it's helpful to see an example. To illustrate, a retail store owner may use DA tools to analyze customer intelligence data. He finds that the majority of customer purchases come from 3 locations.

Furthermore, most customers are male. The owner increases marketing campaigns in those 3 locations and advertises heavily towards males. He also increases the market share with a new line geared towards women.

In contrast, an owner may use BI tools or a dashboard to assess employee performance or monitor inventory management. Or, the same retail owner may use BI to analyze the way customers responded to a marketing campaign. He may use these insights to improve future advertising decisions.

In summary, BI uses descriptive analytics to assess the current status of a business situation. The user then takes these insights to optimize decision-making moving forward. DA uses predictive analytics and descriptive analytics to drill down into data sets, categorize them, and predict future trends.

4. Data Analytics More Statistical, BI More Creative

A data analyst needs to use statistics, science, and a series of intelligence tools to extract insights. All of these insights a data scientist provides to an organization are based on evidence rather than guesswork. Any specific recommendations will come from machine learning and numbers.

Business intelligence insights are also based on statistics, but it also utilizes creative thinking on behalf of the leadership team. Management may take historical and current data to come up with their own strategies, whereas DA creates recommendations based on science. An organization may use one or both practices, depending on its array of business needs.

Key Takeaways of Business Intelligence and Data Analytics

In conclusion, here is what to know about intelligence vs. data analytics -

Descriptive, predictive, and prescriptive analytics are the three categories of data analytics. Descriptive analytics describes data, predictive analytics forecasts future trends, and prescriptive analytics creates recommendations for future actions.
Business analytics uses required data whereas data analytics modifies data that already exists. DA transforms information into a more understandable way through the use of visualizations. BI helps the leadership team develop a plan of action.
BI assesses the current status of business operations, customers, or other metrics. DA predicts future trends.
BI requires more creativity on behalf of the leadership team. Recommendations from DA are all based on statistics and software solutions.