Business Analysis Definition
Let’s first understand the difference between data analytics and traditional analytics. These two words are often used interchangeably, but there are differences. Traditional data analytics refers to the process of analyzing large volumes of collected data to gain insights and make predictions. Business Analysis (sometimes called business analytics) takes this idea in the context of business insights, often by pre-building business content and tools to accelerate the analytical process.
Specifically, the business analysis includes:
Acquire and process historical business data
Analyze this data to identify trends, patterns, and root causes
Make data-driven business decisions based on these insights
In other words, data analysis is more like a general description of the modern analysis process. Business analytics implies a narrower focus, and due to the fact the volume of statistics grows, its capabilities will become more general, creating greater value for businesses around the world.
Using cloud analytics tools, companies can integrate data from different departments such as sales, marketing, human resources, and finance to show how one department's numbers will affect other departments in a unified view. In addition, tools such as visualization, predictive insights, and scenario modeling will provide a variety of unique insights across the enterprise.
Use business analysis tools
Business data analysis provides insights through the synergy of many independent components. Business analytics tools process data and create insights through reporting and visualization, but the process begins with ingesting data in the infrastructure. The standard workflow of the business analysis process is as follows:
Data collection: IoT devices, apps, spreadsheets, and social media are all sources of data. Regardless of where the data comes from, all data needs to be aggregated and accessible, and cloud databases can greatly simplify the collection process. Data Mining: Once facts arrive and are stored (normally in a facts lake ), they have to be looked after and processed. Machine learning algorithms can speed up this process by identifying patterns and repeatable actions, such as establishing metadata for data from a specific source, allowing data scientists to focus more on gaining insights rather than manually handling logistical tasks.
Descriptive Analytics: What's Now? Why is this happening? Descriptive data analysis can answer these questions and help us better understand the story behind the data.
Predictive analytics: With enough data and enough processing for descriptive analytics, business analytics tools can start building predictive models based on trends and historical context. These models can be used to inform business and enterprise choices for future decisions.
Visualization and reporting: Visualization and reporting tools help break down numbers and models to help users easily understand what is being presented. Such tools not only help simplify presentations but also help users such as seasoned data scientists and business people discover new insights quickly.
Business Analytics vs. Business Intelligence
On the surface, there may not seem to be much difference between business analytics and business intelligence. There is indeed an overlap between business analytics and business intelligence, but there are still some differences between the two.
Of course, the connection between the two terms is very close. Business intelligence uses historical and current data to understand what happened in the past and the present. Business analytics, on the other hand, builds on business intelligence and tries to make educated predictions about what might happen in the future. To make data-driven predictions about the likelihood of future outcomes, business analytics will leverage a variety of next-generation technologies, such as machine learning, data visualization, and natural language queries. Advantages of Business AnalyticsThe benefits of business analytics will permeate every corner of the enterprise. Data from various departments will be consolidated into a single source and fully synchronized in the end-to-end process, ensuring seamless flow between individual data or communications, thereby delivering the following benefits:
Data-driven decision-making: Business analytics can help make tough decisions smarter, and smart decisions are backed by data. Quantifying root causes and clearly identifying trends provides more informed insight into the future of your business, including human resource budgets, marketing campaigns, manufacturing and supply chain needs, and sales outreach programs.
Ease of visualization: Business analytics software can do two things take large amounts of data and transform it into simple and effective visualizations. First, insights are easily accessible to business users with just a few clicks. Second, by using a visual format to store data, new ideas can be discovered simply by looking at data in different formats.
"What-if" scenario modeling: Predictive analytics creates models for users to identify trends and patterns that affect future outcomes. Previously, this area was the domain of experienced data scientists. Today, business users can leverage machine learning-based business analytics software to generate these models within the platform and create multiple "what-if" scenarios with slightly different variables to quickly tune the models without relying on complex algorithms.
Augmented Analytics: All of the points above consider the ways in which business data analytics can accelerate user-driven insights. But when business analytics software is empowered with machine learning and artificial intelligence, the power of augmented analytics can be unlocked. Augmented analytics can automate processes by self-learning, adapting, and processing large volumes of data to generate insights without human bias.
Business Analysis Use Scenarios
More and more business units are struggling to gauge the overall impact of their decisions and budgets on the business. With business analytics software, every department and every kind of business can leverage data to drive strategic decisions:
Marketing: Identify success and influence through analytics
Which customers are more likely to respond to marketing emails? What was the ROI of the last marketing campaign? More and more marketing departments are trying to figure out the overall impact of their projects on the business. With analytics capabilities powered by artificial intelligence and machine learning, businesses can leverage data to drive strategic marketing decisions.
Human Resources: Find and Share Talent Insights Through Analytics
What's really driving employees' career decisions? A growing number of HR executives are trying to better understand the overall business impact of their projects. With the right analytics capabilities in place, HR leaders can quantify and predict outcomes, understand the hiring pipeline, and consider employee decisions holistically. learn more
Sales: Boosting Performance Through Analytics
What are the critical moments for converting leads into sales? In-depth analysis can dissect the sales cycle and help users identify the variables that drive purchases. Factors such as price, availability, geographic region, and season can all be turning points in a customer journey, and analytics software provides the tools to identify moments of truth. learn more
Finance: How can analytics power predictive organizational budgets
to improve margins? Finance needs to work with every department, including HR and sales. This means that innovation is always critical, especially when finance departments are faced with a deluge of data. With analytics software, you can help finance the future with machine learning-based predictive modeling, detailed analysis, and deep insights. learn more
Successful cases of business data analysis
Companies of all sizes and industries can use business analytics to transform their operations, decision-making, and forecasting. Listed below are some examples of how our advanced business analytics cloud solutions have helped businesses improve profits.
For example, Western Digital accelerated data access in its mission-critical business applications (including ERP, EPM, and SCM) by 25 times, allowing it to focus on capturing strategic insights, driving innovation, and improving the customer experience rather than having to integrate point systems for analysis data.
Adventist Health: Adventist Health is committed to providing personalized health care. This strategy takes a holistic software approach to deploy a unified cloud that includes Oracle EPM Cloud, Oracle ERP Cloud, Oracle HCM Cloud, and Oracle Analytics and Enterprise Data Management and Planning.
Analytics Tools and Solutions for Your Business - Act Now
With the Oracle Cloud Free Tier, new users get free forever access to two Oracle Autonomous Databases with a host of features like object storage and data egress. Additionally, new users can take advantage of free credit to try out Oracle Analytics and other powerful business services.
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