Business Analytics - The Gateway to Informed Decisions
Despite the competitive advantage of using data-driven processes, research indicates businesses only use a frighteningly small percentage of all available data to drive business decisions. Adopting the practice of using business analytics can change that stat significantly. With business analytics, business users have complete control and understanding of enterprise data. The outcome is more informed decisions, greater confidence, and quicker action for better business outcomes.
In this article, we’ll explain everything you need to know about business analytics including the different types of analytics, the organizational benefits of analytics, common challenges, and cloud-based applications used to streamline the process.
What is business analytics?
Business analytics is the process of using data to understand how the trends, developments, and factors are affecting the way business units—and different workflows and processes—are functioning. Effective business analytics inform users what is currently happening with their organization, why it’s happening, and what they can do to adjust operations in order to optimize business value.
Historically, the various tools used for business analytics were extremely technical and used a great deal of IT department resources. When business users wanted to view the trends affecting their data, IT needed to go through a time-consuming process to retrieve this information. However, business analytics has vastly improved in the last couple of years—many self-service software options with automated access are now available in the market.
More importantly, business analytics has evolved from a simple historical analysis of enterprise data to a process that produces insights on current and future business developments. Additionally, because of the scale, speed, and variety of big data, using business analytics can help organizations better understand and utilize data.
Breaking down business analytics
Business analytics is generally thought of in terms of its two main components: business intelligence (BI) and statistical analysis. Many organizations use BI for reporting, dashboards, visualizations, and online analytical processing (OLAP) of historical data analysis. It typically details the different data trends—which users can slice and dice as they see fit—over a period of time, such as the past week, month, or quarter. BI usually involves descriptive analytics and decision analytics.
Whereas BI tends to focus on decision analytics and descriptive analytics, statistical analysis is usually based on prescriptive and predictive analytics. Statistical analytics involves sophisticated, math-based statistical procedures to determine trends and relevant signals in data. These complex calculations help organizations determine future data trends based on current and historical data.
4 types of business analytics
The two main components of business analytics can be broken down further into four types of business analytics: descriptive, decision, predictive, and prescriptive. The most effective solutions typically rely on a combination of the four to provide a comprehensive analysis of data.
Descriptive analytics are focused on providing users with information on exactly what happened in the past. These analytics are used to gain insight on historical data from the reporting and dashboards of classic BI tools.
Decision analytics provide users with real-time insights on data related to current business operations. In this respect, decision analytics offer relevant insights for making timely decisions.
Predictive analytics use statistical analysis and machine learning techniques to predict data trends. This analysis method allows users to gain information on future outcomes of the metrics currently being tracked.
Prescriptive analytics goes one step beyond predictive analytics. While predictive analytics tell users what’s going to happen next, prescriptive analytics offers a recommended plan of action to achieve business objectives based on trend predictions.
Benefits of business analytics
Although there are many benefits of business analytics, most of them revolve around the ability to take quick action from data-based insights. By having access to historical, current, and predictive data, as well as specific recommendations for achieving business goals, business users can capitalize on this knowledge to achieve business objectives more efficiently. For example, business analytics can identify different customer segments, enabling marketing departments to create and deploy more effective targeted marketing campaigns in real-time.
Another benefit of being able to act quickly centers on improved decision making. The more knowledge organizations have about their business, the better they can allocate resources for specific projects or come up with strategies to increase revenue.
A key way business analytics helps organizations in any industry is by improving customer retention. Using business analytics, organizations are able to identify the different types of customers in a database via segmentation, and then pinpoint almost anything down to a particular product or service customers are significantly satisfied with. By using this information to also focus on customers who downgrade products or services, or stop patronizing companies altogether, business analytics can predict customer behaviors that might lead to customer churn—and provide recommendations for preventing it.
Business analytics challenges
Although there are a myriad of benefits associated with the proper use of business analytics, there are several significant challenges. The biggest ones involve:
- Identifying the right data: Today, relevant data comes from a variety of internal and external data. Companies must know which sources they need and how to best extract data from these sources.
- Integrating the right data: Because of the increase in data sources and connecting them to legacy systems, integrating data is a fundamental challenge. Organizations must integrate the right data efficiently in order to take relevant and fast action on data-driven insights.
- Self-service challenges: Oftentimes, self-service business analytics tools are a great option to keep up with the modern pace of business. However, unless the option is properly governed and implemented, self-service functionality can quickly lead to poor data quality and inaccurate results.
- Skills gap: There’s a learning curve associated with business analytics and a skills shortage of employees that know how to do it. Organizations must either train current employees or find individuals with a business analytics skillset.
Ellie Mae uses business analytics to identify operational inefficiency
Since 1997, Ellie Mae has provided innovative technology to automate mortgages for banks, credit unions, and mortgage companies. Focused on promoting compliance, quality, and efficiency, the company found its database had become siloed by way of business analytics. Experiencing extremely high email bounce rates and returned direct mail, the accuracy of Ellie Mae’s more than 100,000 leads came into question. Worse yet, these database inaccuracies were leading Ellie Mae to make inconsistent and misinformed business decisions.
By instituting a single source of truth and improving the operational efficiency of its lead-management process, Ellie Mae quickly spotted and removed contact duplications and outdated information. Reducing its customer list from 100,000 names to a solid list of 60,000 quality leads, Ellie Mae quickly shifted its 70% bounce rate to an 80% delivered rate.
Cloud-based business analytics
Due to the increasing use of the cloud as a means of accessing data resources, cloud-based business analytics has become a critical component for today’s enterprise organizations. As we know business analytics is useful for analyzing data—in both on-premises and cloud environments—however, the cloud’s scalability has made it a number one choice for housing enterprise data. Additionally, the cloud expands the type of business analytics use cases to include different aspects, network optimization, compliance information, governance infrastucture, and more.
There are several solutions organizations can use in the cloud to help facilitate cloud-based business analytics. These include platforms designed for big data such as Hadoop and processing engines like Apache Spark. Because of the volume of big data, data visualization tools and self-service analytic platforms are also key for implementing cloud-based business analytics. These tools make cloud-based analytics ideal for analyzing all data, including on-prem data in hybrid clouds.
As a comprehensive solution that supports both on-premises and cloud environments, Talend Data Fabric addresses many of the challenges associated with business analytics. With more than 900 connectors to the most productive cloud data sources and tools, organizations can access and integrate the right data to perform the best business analytics processes.
Try Talend Data Fabric today to simplify your business analytics infrastructure.
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