Business Intelligence vs Data Analytics: 5 Key Differences
- Business intelligence tells you what; data analytics tells you why.
- BI looks at the past, while data analytics looks at the future.
- Ordinary users can perform BI tasks, while data analytics requires experts.
- Only data analytics methods can interpret unstructured data.
- BI produces more predictable outcomes than data analytics.
Data is the fuel that powers every modern business. With so much at stake, leaders must ensure that they’re using the right methods to extract maximum value from their data. In this article, we’ll compare two similar but different data processing methods: business intelligence vs data analytics.
Table of Contents
- Business Intelligence vs Data Analytics: The Main Concepts
- What is Business Intelligence?
- What is Data Analytics?
- What are the Differences between Business Intelligence vs Data Analytics?
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Business Intelligence vs Data Analytics: The Main Concepts
We live in a world of data-driven decisions, where business leaders rely on analytics to help them understand their industry, their customers, and themselves.
But how does analytics lead to action? How does an SQL query influence a decision in the boardroom? What are the practical applications of data?
Businesses have numerous processes and technologies that help them turn raw data into something actionable. These methods fall into two categories:
- Analysis: Looking back at historical events to try to establish what has happened in the past.
- Analytics: Extrapolating patterns that will help to predict future events.
Business intelligence (BI) and data analytics contain elements of both. BI is mostly focused on reporting, but those reports may contain elements of analytics. Data analytics focuses on the future, but understanding the future requires an understanding of historical patterns, which you can only acquire through analysis.
To understand business intelligence vs data analytics, let’s take a look at each one in-depth.
What is Business Intelligence?
Business intelligence is a collection of processes and technology that all have one goal: to give business leaders the information they need for decision-making.
The simplest form of BI is something like a sales report. To prepare such a report, an analyst might pull sales data from the system. They will then prepare this data and verify the data quality to ensure that it’s an accurate representation.
But you can’t just hand a spreadsheet or CSV file to a manager. The final step in BI is to turn the data into something easy to understand, such as a graph or a dashboard.
The main processes of BI are:
- Acquisition: Pulling data from a trusted source, such as an on-premise database or a cloud service.
- Validation: Cleansing and verifying data to ensure that it’s accurate and tells the full story.
- Analysis: Identifying the most important metrics within data, such as Key Performance Indicators (KPI).
- Visualization: Creating simple and accessible representations of KPIs and other metrics so that business users can get an at-a-glance view of current performance.
Some BI tools include elements of business analytics. For example, companies that use just-in-time ordering systems need analytics to help anticipate demand, and these analytics insights can end up in reports.
However, BI is generally taken to mean a focus on analysis. Managers need access to reports and dashboards that can tell them what they need to know about performance—the more detailed these visualizations, the more effective their decision-making.
What is Data Analytics?
Data tells us about the past, but it can also reveal a lot about the future. For example, a grocery store might have data that tells them that they sell a lot of pumpkins in October. From this data, they can extrapolate that they will need to order extra pumpkins for next October.
Data analytics uses sophisticated modeling tools to dive deep into the data and find patterns that have a predictive value. These patterns can be extremely complex, and they return insights that you would never discern with traditional methods.
For example, companies like Amazon and Netflix can be eerily accurate when they show you products or movies that you might like. How do they guess your interests so precisely? By performing data analytics on records from thousands of customers who match your behavioral profile.
The data analytics process typically goes like this:
- Aggregation: The data team will pull disparate data sources together in a single repository, like a data lake or a data warehouse.
- Exploration: Data exploration involves looking for patterns and clusters that might be significant for analytics purposes.
- Analytics: Data scientists use complex statistical methods like regression analysis and association rule mining to find useful insights within the data.
- Utilization: Most analytics projects result in some kind of visualization, which is then used to guide business decisions. Analytics tools can also link directly to other systems, such as ERP, and change the direction of automated processes.
There is some overlap between analytics and BI. For example, at the exploration stage, you need a degree of analysis to help produce the predictive models that offer insight into future behavior. Also, you can use BI tools to produce visualizations for analytics-related insights.
However, there are some important differences between the two disciplines. Let’s take a closer look at business intelligence vs data analytics.
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What are the Differences between Business Intelligence vs Data Analytics?
While BI and analytics are often interdependent, they are different in some notable ways.
What vs Why
BI and analytics both try to answer business questions with data. However, they each focus on a different type of question.
Business intelligence focuses on answering “what” questions. What were our sales figures for Q1? What department is underperforming? What is the failure rate in our manufacturing process? These are vital questions that allow managers to get a quick understanding of current states. BI dashboards are essential for day-to-day operations, while reports and charts help ensure that the business remains aligned with long-term goals.
Data analytics tries to answer “why” questions. Why are customers choosing particular products? Why are we struggling to convert leads? Why do we have too much inventory? Why do some staff work overtime at weekends? “Why” questions are much more open-ended than “what” questions. Data analytics can produce some highly unpredictable results, which may result in a business having to change their long-term goals. Often, this can be positive, such as when analytics identifies a new market opportunity.
Past vs Future
Data is a record of something that happened, like customer interaction or an IoT device input. This means that data only ever describes the past. With a little finesse, however, data can help you predict the future.
Business intelligence mostly focuses on summarizing past events for business users. If you want a report on Q1 sales, you gather all sales data for Q1 and condense it down into a few headline figures, such as sales per employee or department. Time boundaries are often essential in BI. Most BI reports will look at data within a defined period, whether that’s a single day or a full year. If there are insufficient data for that period, then the resulting report might not be useful.
Data analytics looks at the past to tell us about the future. Patterns in data often have a strong predictive value. However, it takes good tools and talented data scientists to discern these patterns. Analytics is a data-hungry practice, so there may not be any limitations on the data available for queries. Data scientists prefer to work with a large body of data, so in some cases, this might include information that stretches back over several years.
Business Users vs Data Scientists
Both disciplines require skilled staff to gather, cleanse, validate, and integrate the data. This often involves an automated ETL process and a repository such as a warehouse
Business intelligence tasks are accessible to people with minimal tech skills, as long as they have access to a reliable source of data. There are powerful BI tools such as Chartio, Tableau, and Looker that integrate right to a data repository without requiring lots of coding. Business users can get to work with these BI tools right away. They might also use the most familiar of BI tools: Excel. Excel’s visualizations are not as stylish as some rivals, but even technophobic users find them easy to create and understand.
Data analytics can sometimes involve specialist staff. For large-scale projects, an organization might bring in a data scientist who has a deep understanding of statistical methods, as well as programming experience in R or Python. There are powerful solutions such as Google BigQuery and Amazon QuickSight that can help make data analytics easier. However, even with such tools, most analytics projects will require guidance from people with a background in maths or databases.
Structured Data vs Unstructured Data
Structured data is anything in a relational database table. Everything else is unstructured data, including text files, images, PDFs, system logs, and audio recordings.
Business intelligence works exclusively with structured data and semi-structured data (files containing data exports, like CSVs and JSONs). Information comes from business systems, such as CRM, ERP, sales tools, and website logs. The quality of BI depends on the quality of the data. When setting up a new BI process, the business must ensure that the incoming data is complete and accurate. Otherwise, the resulting visualizations will be inaccurate.
Data analytics is a lot less restricted. Analytics is faster and easier when working with structured data, which is why many organizations rely on ETL and data warehouses to support their analytics efforts. However, analytics can also work with unstructured repositories, such as data lakes. Analytics requires sophisticated processes to navigate these massive structures and to interpret unstructured data. But the results can offer astonishingly detailed insights into the current and future state of the organization.
Results vs Insights
As well as asking different questions, these methodologies produce two different types of answers.
Business intelligence produces answers that fit within a known range. This can be a continuous value, such as a percentage, or discrete values, such as a dollar amount at the end of a report. This type of information has immense practical value. Managers can look at their dashboards and get a clear, unambiguous view of current performance. They can also easily perform like-for-like comparisons, such as comparing the results of two productivity reports.
Data analytics is less predictable. The results of an analytics process are insights, a name that describes exactly what they are: an insight into your business. This may be a negative insight (analytics is often used to detect fraud), or it may be something more positive, such as a predictive insight into customers’ buying patterns. Insights are not as uniform as the results of BI. Insight data from analytics projects can vary greatly in scope and format. It’s up to the business leaders to figure out how to turn each insight into action.
Ultimately, it’s not a matter of business intelligence vs data analytics, but BI and analytics working together. BI is essential for day-to-day operations, while analytics helps to shape strategy. Rather than keeping BI and analytics separate, now is the time to consider combining both activities. They both have a lot in common – most notably their requirements for a constant supply of fresh, reliable data.
A cloud-based ETL like Xplenty can help build reliable data repositories that will support all of your analysis and analytics needs. Book your demo of Xplenty today and see how it can help your business.