The new paradigm of Business Intelligence

Traditional Business Intelligence (BI) solutions have been improving decision-making for industries for quite some time now. This holds especially with the growth of business analytics, both from the analysis and visualization point of view.

Let us ask an honest question to ourselves: “Is this is enough in today’s business context across industries?” The answer is that it may be helpful, but only to a certain extent.

The traditional enterprise data warehouse can store transactional information from multiple systems, including Excel/flat files with some structure or pattern. An analytical model can be created with or without an OLAP layer to provide rich analytics in terms of dashboards and reports. Mining models can be created and integrated with a data warehouse or OLAP layer to build predictive analytics. Ultimately, these business analytics tools should help the target audience discover ‘What,’ ‘When,’ ‘How,’ ‘Where,’ and ‘What may (will)’ happen concerning their business.

Independent Software Vendors (ISVs) from various industries have built their products and solutions using business intelligence modules to provide better insights into business processes. However, these business analytics are limited to the information captured through the structured software.

Today, we have significantly evolved in capturing vast amounts of non-transactional, unstructured business data from disparate sources and storing it. This has inevitably paved the way for a new generation of business intelligence tools like Power BI. The usage of ‘Hadoop’ as a Big Data platform, in addition to an enterprise data warehouse or substitute, has started to rise. The key drivers for this paradigm shift are:

New Business Insights (viz., The Internet of Things)

New Technologies (viz. AI ML and data engineering)

Reduced Costs

The new technologies help enhance data management, new deployment options, and advanced analytics for ISVs to make their software products suitable for today’s business needs. This is the game changer for ISVs. Usually, the applications have progressed by leaps and bounds in the healthcare, retail, and e-commerce space. Here are some top use cases that apply to ISVs across industries:

Optimize Funnel Conversion by achieving more growth in sales of products/services with lowered costs.

Behavioral Analytics to help analyze customer/employee behavior and customize offerings to maximize gains

Customer segmentation for improved targeting of the right products/services at the right place and time. This will be more accurate than first-generation BI as the results will be combined with macro-environmental non-transactional data associated with the transactional data captured through traditional source systems.

Predictive analytics for better planning and forecasting. The results of predictive models are more accurate when we feed a larger volume and a variety of data compared to the first generation of business intelligence technologies.

Market Basket Analysis to better bundle the service offerings to the right target audience

Predict security threats by analyzing historical breaches and prepare better for the future.

Fraud detection to identify possible fraudulent activity beforehand and minimize losses

In this new era of social, mobile, analytics, and cloud (SMAC), the data is generated through sensor systems, machines, mobiles, weblogs, or interactions through social media. This data is unstructured, voluminous, and varied. However, it is critical when combined & analyzed with the structured data captured through traditional systems. Today, after a transformation over time, the new ecosystem of business intelligence includes two critical components:

Data Refinery

Ingests raw, detailed data in batches and real-time data into a managed data store

Distills the data into useful information and distributes results to other systems

The critical use of Hadoop today

Computing Platform

Used for exploring data and developing new analytics models

It may also be used for prototyping new analytics-driven LOB applications and for temporary analytic solutions

May employ an RDBMS or Hadoop

Enables users to blend new types of data with existing information to discover ways of improving business processes

Allows users to experiment with different types of data and analytics before committing to a particular solution

May employ an RDBMS or Hadoop-based solution running on-premises or in the cloud – Hadoop is especially well-suited to processing large amounts of multi-structured data

Represents a shift in the way organizations build analytic solutions:

Increases flexibility and provides faster time to value because data does not have to be modeled or integrated into an EDW before it can be analyzed

Extends traditional business decision-making with solutions that increase the use and business value of analytics throughout the enterprise

This way, a paradigm shift has occurred in business intelligence tools and platforms. Unsurprisingly, it also creates a dire need for ISVs to modernize and build their software products and applications with the above capabilities. The ISVs that have sensed this and have started implementing the change are gaining a competitive advantage.

The rise of the Cloud and Disrupting the BI / DW ecosystem

Business Intelligence analytics tools and BI analytics are ushering in a new age of business. Combined with mobile BI and open-source BI tools, the BI market will surely surge ahead.

The ISVs have already realized the potential of the cloud and moved their software and applications, offering customers SaaS-based services. The next big question in their mind is: “Can a Business Intelligence module can be moved there?” This has its advantages and apparent challenges that can be addressed. This has given rise to ‘AaaS – Analytics As a Service.’ Stay tuned for our next blog, where we deep dive into this.

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