Business Intelligence (BI) vs. Business Analytics
Business Intelligence vs. Business Analytics, what’s the difference? There are many differences of opinion, but generally, both describe the use of data in making business decisions. So what are the differences? And should you care? In this blog, we aim to outline how the terms are used differently in the Industry.
What is Business Intelligence (BI)?
Broadly speaking, business intelligence tools include both reporting, visualization, and analytics functions used to interpret large volumes of data. These BI Analytics tools and applications are used to analyze data from business operations and transform raw data into meaningful, useful and actionable information. BI Tools are used both inside the enterprise and within software applications to provide insight from operational data, financial data, and more.
Though there can be many variations in the feature sets of BI platforms, the main deliverables of any BI stack are Dashboards, Reports, and more recently Self-Service capabilities.
Of the different types of BI, Embedded BI aims to provide a seamless integration of analytical capabilities directly into your applications.
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What is Business Analytics?
Business analytics is heavily statistically focused and uses analysis techniques such as descriptive, predictive, and prescriptive analytics. It also consists of a set of solutions, methods, skills and best practices used to gain insights for understanding current business realities and business planning.
The breadth and depth of statistical analysis capabilities found in business analytics products can vary greatly. However, the primary use of business analytics remains the same: to drive decision making.
What’s the Difference?
So what’s the difference? Where does the term “business analytics” fit within business intelligence? The statistical techniques categorized by business analytics are often found within the category of business intelligence. The main focus of a “business analytics” product is to create actionable business intelligence. When in doubt, the term business analytics is used to focus more specifically on the statistical capabilities of a BI product.
Why are BI and BI Analytics So Important?
Both business intelligence and BI analytics have huge potential upsides in decision support and business workflow enablement and improvement. Both give businesses the power to visualize and analyze data in new ways helping to drive business efficiency. BI systems embedded in business applications help empower application users with in-context analysis in the systems they already used for more efficient workflows.
Data used in the data analysis process can come from a variety of sources such as data warehouses, big data sources, and traditional relational data sources. The type of data is dependent on the business need and type of analysis used.
Historically, statistical analysis and general data analysis was done by statisticians, data scientists, and data analysts. But the rapid expansion of data capture and need for faster analysis has driven the development of easier to use platforms designed for the self-service creation of reports, dashboards, and visualizations.
Types of Business Intelligence Tools
As mentioned, business intelligence tools incorporate a wide variety of tools and methodologies for use in data analysis. Some of the more common types of tools include traditional reporting, enterprise-level reporting, real-time analysis, mapping analysis, OLAP, KPI dashboarding, cloud reporting, data discovery tools, self-service analytics, and big data analytics.
Data discovery and self-service have become standard in many BI platforms for use by non-technical end users as previously mentioned. The ability to visualize data in a meaningful way quickly has also increased as different types of visualizations have been demanded.
The majority of the tools mentioned to this point focus on visualizing data from the past or from the present to some degree, but the usage of more advanced forms of analysis are becoming more widely used some of which for use in predictive analytics. These types of analysis consist of big data analysis, predictive analytics, statistical analysis, and machine learning. All of which is being more readily used for general business use rather than for embedded use. With this shift towards more technical analysis also comes a shift back towards more technical end users such as data scientists.
JReport’s Embedded Business Intelligence Tool
JReport empowers applications with embedded analytics designed for OEM customization and embedding. It has many of the traditional BI capabilities you would come to expect, but with the added ability to fully integrate and white label for use in third-party applications. It supports advanced, pixel-perfect reporting, dashboarding and KPI dashboarding, self-service, as well as a variety of other BI Tools. JReport supports many different security standards and has full multi-tenancy capabilities for use in a variety of different deployment models. With JReport you can also scale up and down as your user demands shifts over time. With failover protection, load balancing, and a node-cluster architecture that has no single points of failure JReport allows you to provide analytics to your application’s end users at peak load times with zero downtime.
How JReport’s Embedded Business Intelligence Tool Empowers OEM
So how do software companies leverage BI and BI Analytical Tools?
Software companies use embedded analytics to do several things. Embedded analytics can help improve their products value proposition by providing users with more advanced future proof analytical capabilities which can increase competitive advantage, as can the decreased time-to-market. The developmental efficiency of embedding versus developing something from scratch can also mean your development team has more time to focus on your core capabilities rather than BI, as can the administrative efficiency of having a full-fledged application and support structure vs. developing something yourself. Embedding analytics also has a lower total cost of ownership over time, while providing the same rich, branded experience your customers come to expect. To summarize, most software companies who aren’t in the BI industry don’t want to be in the BI industry and therefore choose to leverage 3rd party tools to further extend their applications feature sets.
5 Core BI Analytics Capabilities
JReport supports many different analytics capabilities, but here are the highlights:
- Embed Seamlessly- As mentioned previously, JReport is built to embed into other applications, and it does so with an extensive set of APIs that allow you to embed both on the front and back end so your users experience a seamless, cohesive experience. Whitelabeling with CSS also allows you to match the look of your application so end users won’t even know they’re using a third-party product.
- Advanced Reports and Dashboards- JReport supports complex reporting and dashboarding requirements. Pixel-perfect reporting, as well as self-service analytics, can empower organizations to visualize data in the ways that are most meaningful to them. Dashboards can be created from scratch, and widgets can be set to synchronize with each other for in-context analysis of summary level data.
- Security Controls- JReport supports both SSO and multi-tenancy. You can define data permissions down to the row and column level so users only see the data they’re supposed to, and you don’t have to redesign your application’s security infrastructure to do so.
- Self-Service BI- With JReport, your user can quickly and easily build, edit, and share their own reports, dashboards, and widgets. Collaborative reporting, data discovery, and decision support have never been easier.
- Scale Quickly and Easily- JReport is designed to scale. With a cluster-node architecture, you can scale as need be to meet your peak load times without having to worry about performance. Fault tolerance and configurable failover allow you to easily manage your application’s BI needs.
The Future of BI Analytics
To this point, it can be said that the majority of BI analytics has been focused on seeing where we’ve been, where we are now, and the direction we’re heading into. Understanding and analyzing our past data and our real-time data to help predict our future through visualization has been a core part of the workflows and decision-making processes we use every day. And mainly it has been in helping us recognize patterns and to inform us, because when you boil down all of the capabilities of traditional BI that has been the innate goal from the start. But as the size of our data has grown it has in some ways, become more and more difficult to draw connections between different types of data points. Partly, because our data lives in different places, in different data sources, and different schemas. And partly because there are limitations to our ability to connect seemingly independent points of data and even more so how that data is interwoven into the daily problems of business.
One of the biggest trends to help resolve these issues has been in the creation of data warehouses and big data sources. Large data repositories which are less affected by the technical performance limitations of traditional relational databases, and whose structure doesn’t have to be as rigid. The future of BI analytics will continue to evolve around these types of data sources and will evolve ever more sophisticated ways of utilizing ever larger amounts of data through visual analysis.
But the standard toolkits of BI analytics solutions today will not categorically remain the same long-term. The two traditional types of BI analysis: descriptive analytics which describes what has happened and what is happening, and predictive analytics which describes what might happen, will soon be joined by a wholly new type of analysis, prescriptive analytics or what can happen and what’s the best possible outcome. Machine learning tools will allow decision makers and end users alike to ask the question, “What if?”, without having to fundamentally understand the underlying patterns which may drive an issue and it will change everything about how we analyze data.
That’s not to say that traditional BI won’t have its place in the future of BI. In fact, there are many avenues left to explore in how descriptive and predictive analytics can be integrated into day-to-day reporting and decision making.
And in fact, predictive analytics is still in many ways in its infancy. Either way, there will always be utility in being able to see where we came from and the current state of things.
- Business intelligence and business analytics are for the most part, synonymous with each other. But in some cases, the use of business analytics can refer to more sophisticated types of analysis.
- JReport has a fully embeddable business intelligence solution that allows you to empower users with the ability to visualize their data in reports, dashboards, and widgets. By embedding a business intelligence solution in your application, you can save time, capital, and energy when compared to building it yourself.
- The future of BI analytics will continue to contain descriptive and predictive analysis, but may also contain prescriptive analysis as well.