Business Intelligence vs. Data Analytics: What Is the Difference?
In today’s data-driven world, it is crucial for organizations to harness the power of data to make informed decisions. Research shows that data-driven organizations are::
- 162% more likely to surpass their revenue goals
- 23 times more likely to acquire customers
- Outperforming their peers in terms of operational efficiency (81% vs. 58%)
These impressive statistics are prompting business leaders to seek the best data processing solutions. In doing so, leaders must understand the key similarities and differences between business intelligence vs. data analytics.
While business intelligence and data analytics have a lot in common, they are not the same. Consider how experts in business intelligence and data analytics understand these concepts and how an advanced education in business administration can prepare leaders for the modern, data-driven business environment.
Business Intelligence vs. Data Analytics: Defined
Business intelligence and data analytics are both important ways to harness the power of data, but they differ in focus and approach.
Business intelligence (BI) is a set of tools and methodologies used to gather, analyze and present data to support business decision-making. It involves collecting and analyzing data from sources such as databases and spreadsheets to provide insights into business operations and performance. BI tools typically include:
- Dashboards
- Reports
- Data visualization tools
BI tools allow users to monitor key performance indicators (KPIs) and other metrics to make data-driven decisions.
Data analytics is a broader field that involves using statistical and quantitative methods to analyze large sets of data and extract insights and patterns. Data analytics can be used to solve complex problems, identify trends and predict outcomes. It often involves more advanced techniques such as:
- Predictive modeling
- Machine learning
- Data mining
With these differences in mind, delve deeper into the differences between — and uses of — business intelligence and data analytics.
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Understanding Business Intelligence
One could say “business intelligence” to describe the process of transforming raw sales data into meaningful insights about customer trends. Or one could use the phrase to describe the insight that customers of a clothing website during a specific season made more purchases when they were offered free expedited shipping than they did when they were offered 20% off of an additional item.
When it comes to business intelligence vs. data analytics, it’s important to note that data analytics can be used outside of a BI process, such as in education or the government. But when it comes to the relationship between BI and data analytics, BI is understood to include the process of data analytics.
The Four Types of Data Analytics
The field of data analysis produces four types of analytics, each of which can be meaningful in helping companies achieve their desired outcomes. Those four types of analytics are known as descriptive, diagnostic, predictive and prescriptive.
1. Descriptive Data Analytics
The most fundamental and simple form of data analytics, descriptive analysis looks at raw data and extracts insights about what has already occurred or is currently occurring. For example, descriptive analytics may determine from data regarding a past year’s marketing efforts that the February campaign was the most successful. Much of what is seen in data visualizations, like infographics, is an example of descriptive data analytics.
2. Diagnostic Data Analytics
Built on the foundation of descriptive analytics, diagnostic data analytics go one step further and consider why something happened or is happening. Take the marketing campaigns scenario, for example. Where descriptive analytics stops at describing the simplest fact — marketing efforts succeeded the most in February — diagnostic analytics might look into demographic data that reveals a high number of social media conversions among married women.
This insight may reveal that the company does especially well with women purchasing Valentine’s Day gifts for their husbands. It could encourage the company’s marketing department to prioritize that campaign each year and to dream up new campaigns oriented toward gift-giving between couples throughout the year.
3. Predictive Data Analytics
If descriptive analytics explains what happened (or is happening) and diagnostic analytics explains why something happened (or is happening), then predictive analytics say what may happen in the future. Predictive analytics considers both company and industry data to determine what types of trends may be on the horizon. For example, the hospitality industry uses predictive analytics to determine tourist trends.
4. Prescriptive Data Analytics
Used in tandem with predictive analytics, prescriptive data analytics recommend business decisions for particular scenarios. In the case of the hospitality industry, for example, predictive analytics may show that visitors come during certain months of the year in far greater numbers than they do at other times. Prescriptive analytics can empower hotels, restaurants and attractions to optimize their operations through recommendations regarding key areas like staffing, setting prices and offering special deals.
How Business Intelligence and Data Analytics Work Together in the Marketplace
Business intelligence platform Datapine explains that business intelligence tools can help companies know what happened and how, while analytics can uncover why it happened. When leveraged in tandem, business analytics and data analysis can unite in powerful ways that benefit companies, their clients and consumers.
Combine Business Intelligence and Data Analytics For Efficiency
Swedish pharmaceutical company Apoteket AB was growing and needed a more comprehensive data analytics solution. Their store managers were dependent upon BI reports, but query times and user experience were subpar. The company decided to implement Oracle Analytics Cloud, which features advanced analytics capabilities as well as artificial intelligence and machine learning solutions that modernize and produce high-quality business intelligence.
The solution reduced the cost of running analytics by 66% and improved report-running time by 40%. Head of Business Intelligence Mats Mälhammar further explained to Oracle how business operations at Apoteket AB have improved.
“With Oracle Analytics Cloud, we can stay one step ahead in providing our customers with reliable, credible and efficient service,” Mälhammar said. “We leverage the most advanced analytical features that are available on the market. And our BI is ready for the future, including AI capabilities and access to Oracle Mobile.”
Empower Customers and Improve Online Private Marketplaces
Digital advertising and marketing technology company Gamoshi buys and sells online advertising, which requires access to real-time data in large quantities. They needed a solution that would not only gather and analyze that data but present it in meaningful ways to their customers. Gamoshi migrated to Amazon Web Services, specifically using Amazon Redshift, an insight-generating data warehouse, and Amazon Kinesis Data Analytics, which empowers users to gain actionable insights from streaming data with serverless, fully managed Apache Flink.
Gamoshi’s business intelligence and data analytics are better synchronized than ever before, which has benefited the company in several ways. Co-founder and CEO Moshe Moses says that the company is now the most efficient it has ever been and has cut its time-to-market for big data from 1-2 weeks to one day. This, Moses says, has been revolutionary for their users, who are also enjoying a greater date range when querying data than they could previously access. By bringing data analytics and business intelligence tools together in a streamlined way, Gamoshi is better able to help its customers make data-driven decisions.
Career Profiles: Business Intelligence vs. Data Analytics
While business intelligence and data analytics have overlapping skill sets, there are some key distinctions between the occupations in the two areas and the skills they require. Business intelligence specialists, for example, need to be skilled in querying structured data, while data analysts also need to know how to interact with unstructured data. The primary goal of a BI analyst is to create, improve and present business reports with actionable analytical insights. A data analyst tends to work more technically with the data, examining it for patterns.
Business intelligence professionals need to have strong business and finance expertise. A data analyst, on the other hand, needs to be skilled in data wrangling, statistics and modeling. It’s important to note that terms like data analysis, business intelligence and data science are continually evolving. That said, business intelligence professionals tend to be more people-facing as they share and lead from their reports.
According to Payscale, data analysts made a median annual salary of about $65,100, and business intelligence analysts made about $72,100.
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Companies throughout the world are leveraging data to make the most of the information that they collect, store and process. Understanding the applications of business intelligence vs. data analytics can give business leaders a competitive edge.
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