Companies throughout the world are leveraging data to make the most of the information that their organization collects, stores and processes. Research shows that data-driven organizations are more successful in a number of ways. For example, data-driven organizations are:
- 162 percent more likely to surpass their revenue goals
- 23 times more likely to acquire customers
- 3 times more likely to report a significant improvement in decision-making
These impressive statistics are prompting business leaders to ensure that they are using the best data processing solutions. As terms like business intelligence, data science and predictive analytics have become regularly discussed concepts in the corporate world, it’s important for these leaders to understand the similarities, differences and complementary features of these processes and technologies.
For example, when it comes to business intelligence vs. data analytics, are there differences in the technologies, careers and outcomes in the two fields?
While business intelligence and data analytics have a lot in common, they are not the same. Consider some of the key differences between these growing fields, how they work together in the real world and the skills required to work in these positions.
Understanding Business Intelligence
Business intelligence (BI) refers to the methods, tools and technologies that companies use to acquire and process data, converting it into meaningful insights. The term “business intelligence” also refers to the insights themselves. So, one could say “business intelligence” to describe the process of transforming raw sales data into meaningful insights about customer trends. One could also 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 percent off of an additional item.
BI tends to include tools like dashboards and data mining technology. Business intelligence also includes the use of data analytics, which clarifies part of the answer to the earlier question of whether or not there is a difference 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 term data analytics refers to the process of examining raw data to find patterns and trends in order to draw conclusions from the data. 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.
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.
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.
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.
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 Analysis 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, BI tools and data analysis can unite in powerful ways that benefit companies, their clients and consumers.
Coming Together in the Cloud: Cost-Savings and Increased Efficiency through Combined Business Intelligence and Data Analytics
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 percent and improved report-running time by 40 percent. Head of Business Intelligence Mats Mälhammar further explains 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.”
Empowering Customers: Analytics, Visualization and Cloud-Based Infrastructure Unite to 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 professionals and data analysts have somewhat overlapping skill sets, there are some key distinctions between the two occupations 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 the business 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.
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