Nie znaleziono wyników

Twoje wyszukiwanie nie dało żadnych wyników.

Zalecamy wypróbowanie następujących rozwiązań, aby znaleźć to, czego szukasz:

  • Sprawdź pisownię wyszukiwania słowa kluczowego.
  • Użyj synonimów dla wpisanego słowa kluczowego, na przykład spróbuj wpisać „aplikacja” zamiast „oprogramowanie”.
  • Wypróbuj jedno z popularnych wyszukiwań wskazanych poniżej.
  • Rozpocznij nowe wyszukiwanie.
Popularne pytania

What is business analytics?

Business analytics defined

Let’s start by differentiating between data analytics and traditional analytics. The terms are often used interchangeably, but a distinction does exist. Traditional data analytics refers to the process of analyzing massive amounts of collected data to get insights and predictions. Business data analytics (sometimes called business analytics) takes that idea, but puts it in the context of business insight, often with prebuilt business content and tools that expedite the analysis process.

Specifically, business analytics refers to:

  • Taking in and processing historical business data
  • Analyzing that data to identify trends, patterns, and root causes
  • Making data-driven business decisions based on those insights

In other words, data analytics is more of a general description of the modern analytics process. Business analytics implies a narrower focus and has functionally become more prevalent and more important for organizations around the globe as the overall volume of data has increased.

Using cloud analytics tools, organizations can consolidate data from different departments—sales, marketing, HR, and finance—for a unified view that shows how one department’s numbers can influence the others. Further, tools, such as visualization, predictive insights, and scenario modeling deliver all kinds of unique insights across an entire organization.


Using business analytics tools

Business data analytics has many individual components that work together to provide insights. While business analytics tools handle the elements of crunching data and creating insights through reports and visualization, the process actually starts with the infrastructure for bringing that data in. A standard workflow for the business analytics process is as follows:

Data collection: Wherever data comes from, be it IoT devices, apps, spreadsheets, or social media, all of that data needs to get pooled and centralized for access. Using a cloud database makes the collection process significantly easier.

Data mining: Once data arrives and is stored (usually in a data lake), it must be sorted and processed. Machine learning algorithms can accelerate this by recognizing patterns and repeatable actions, such as establishing metadata for data from specific sources, allowing data scientists to focus more on deriving insights rather than manual logistical tasks.

Descriptive analytics: What is happening and why is it happening? Descriptive data analytics answers these questions to build a greater understanding of the story behind the data.

Predictive analytics: With enough data—and enough processing of descriptive analytics —business analytics tools can start to build predictive models based on trends and historical context. These models can thus be used to inform future decisions regarding business and organizational choices.

Visualization and reporting: Visualization and reporting tools can help break down the numbers and models so that the human eye can easily grasp what is being presented. Not only does this make presentations easier, these types of tools can help anyone from experienced data scientists to business users quickly uncover new insights.

Using business analytics tools

Business analytics vs. business intelligence

On the face of it, there may not seem to be much difference between business analytics and business intelligence. Some overlap does exist between the two, but looking at business analytics versus business intelligence still creates a gap that needs some explanation.

Certainly, the terms are extremely connected, but business intelligence uses historical and current data to understand what happened in the past and what is happening now. Business analytics, on the other hand, builds on the foundation of business intelligence and attempts to make educated predictions about what might happen in the future. In order to make data-driven predictions about the likelihood of future outcomes, business analytics uses next-generation technology, such as machine learning, data visualization, and natural language query.

Benefits of business analytics

Business analytics benefits impact every corner of your organization. When data across departments consolidates into a single source, it syncs up everyone in the end-to-end process. This ensures there are no gaps in data or communication, thus unlocking benefits such as:

Data-driven decisions: With business analytics, hard decisions become smarter—and by smart, that means that they are backed up by data. Quantifying root causes and clearly identifying trends creates a smarter way to look at the future of an organization, whether it be HR budgets, marketing campaigns, manufacturing and supply chain needs, or sales outreach programs.

Easy visualization: Business analytics software can take unwieldy amounts of data and turn it into simple-yet-effective visualizations. This accomplishes two things. First, it makes insights much more accessible for business users with just a few clicks. Second, by putting data in a visual format, new ideas can be uncovered simply by viewing the data in a different format.

Modeling the what-if scenario: Predictive analytics creates models for users to look for trends and patterns that will affect future outcomes. This previously was the domain of experienced data scientists, but with business analytics software powered by machine learning, these models can be generated within the platform. That gives business users the ability to quickly tweak the model by creating what-if scenarios with slightly different variables without any need to create sophisticated algorithms.

Go augmented: All of the points above consider the ways that business data analytics expedite user-driven insights. But when business analytics software is powered by machine learning and artificial intelligence, the power of augmented analytics is unlocked. Augmented analytics uses the ability to self-learn, adapt, and process bulk quantities of data to automate processes and generate insights without human bias.

Business analytics use cases

More and more departments are trying to better understand how their decisions and budgets affect the business at large. With business analytics software, it’s possible to use data to drive strategic decisions, regardless of task or department:

Marketing: Analytics to identify success and impact
Which customers are more likely to respond to an email campaign? What was the last campaign’s ROI? More and more marketing departments are trying to better understand how their programs affect the business at large. With AI and machine learning powering analysis, it’s possible to use data to drive strategic marketing decisions. Learn more

Human Resources: Analytics to find and share talent insights
What actually drives employee decisions regarding their career? More and more HR leaders are trying to better understand how their programs affect the business at large. With the right analytical capabilities, HR leaders are able to quantify and predict outcomes, understand recruitment channels, and review employee decisions en masse. Learn more

Sales: Analytics to optimize your sales
What is the critical moment that converts a lead to a sale? In-depth analytics can break down the sales cycle, taking in all of the different variables that lead to a purchase. Price, availability, geography, season, and other factors can be the turning point on the customer journey—and analytics offer the tool to decipher that key moment. Learn more

Finance: Analytics to power predictive organizational budgets
How can you increase your profit margins? Finance works with every department, be it HR or sales. That means that innovation is always key, especially as finance departments face larger volumes of data. With analytics, it’s possible to bring finance into the future for predictive modeling, detailed analysis, and insights from machine learning. Learn more

Business data analytics success stories

Companies of all sizes and industries can transform their operations, decision-making, and projections by using business analytics. Here are a few stories of how our industry-leading business analytics cloud solutions helped businesses improve their bottom line.

Western Digital, for example, can access data 25X faster across their mission-critical business applications—including ERP, EPM, and SCM—enabling their business to focus on strategic insights, innovation, and improved customer experience instead of how to integrate point systems to analyze data.

Adventist Health: Adventist Health aims to provide whole-person healthcare, a strategy supported through its holistic software approach of deploying a unified cloud that includes Oracle Cloud EPM, ERP, HCM, and Analytics, along with enterprise data management and planning.


Analytics tools and solutions for your business—Get Started

With Cloud Free Tier, new users get Always Free access to two Oracle Autonomous Databases loaded with a host of features, such as object storage and data egress. In addition, new users get free credits to try Oracle Analytics and other powerful business services.

Make faster and more confident decisions for your business with Oracle Analytics Cloud.