Business analytics is the process of transforming data into insights to improve business decisions. Data management, data visualization, predictive modeling, data mining, forecasting simulation, and optimization are some of the tools used to create insights from data.
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:
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.
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.
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