Since many years, we have seen how traditional Business Intelligence solutions have helped industries make better decisions. This has been especially true for the growth of analytics from both the visualization and analysis point of view. Let’s be honest and ask ourselves the question: “Is this enough in today’s business context across all industries?” The answer to that question is yes, but only to an extent. Traditional enterprise data warehouses can store transactional information from multiple systems. This includes Excel/flat files with a certain structure/pattern. To provide rich analytics, an analytical model can be built with or without an OLA layer. To build predictive analytics, mining models can be integrated with a data warehouse. The target audience should be able find the ‘What, When, & How’, and ‘Where’ information relative to their business.

Independent Software Vendors (ISVs), which are from different industries, have combined their products and solutions with Business Intelligence modules to give better insight into the business processes. These insights only include the information that is captured by the software.

Information technology has made a huge leap in the storage and retrieval of large amounts of unstructured, non-transactional business data. This is what has led to a new generation Business Intelligence technologies. It is becoming more common to use ‘Hadoop’ as a Big Data platform, either as an addition to or substitute for enterprise data warehouses. These are the key drivers of this paradigm shift:

1. New Business Insights, e.g. The Internet of Things
2. New Technologies
3. Lower Costs

These new technologies allow ISVs to use ‘Enhanced Data Management’, “New Deployment Options” and “Advanced Analytics” to make their software products fit for modern business needs. Independent Software Vendors, or ISVs, will see this as a game-changer. Applications have advanced by leaps in the Healthcare, Retail, and e-commerce sectors. Below are five of the most common use cases that ISVs can apply across all industries.

1. Optimize funnel to increase sales of products/services at lower costs
2. To help analyze employee/customer behavior and tailor offerings to maximize profits
3. Customer segmentation to improve targeting and reach the right products/services in the right places and at the right times. Because the results are combined with transactional data from traditional sources, this will make it more accurate than the first generation BI.
4. Predictive Analytics to improve planning and forecasting. Predictive models produce more accurate results when we provide more data than the first generation of Business Intelligence technologies.
5. Market Basket Analysis to better bundle the services offered to the right audience
6. Predict security risks by analyzing past breaches and preparing better for the future
7. Fraud detection to identify potential fraudulent activity and minimize losses

The new SMAC era is characterized by data generated either through sensors, systems and machines, mobiles, web logs, social media interactions, or through the use of systems, machines, computers, mobiles, web logs and other devices. These data are unstructured, large, diverse, and critical when combined with structured data from traditional systems. Let’s now see how each area has changed in Business Intelligence.

The new ecosystem of Business Intelligence will look like this when we put them all together:

Let’s look at the highlighted boxes.

Data Refinery

• Raw data can be ingested in batches or real-time to a managed data storage
• Transforms data into useful information.
• Hadoop is a crucial tool for today’s business.

Computing Platform

• This is used to explore data and develop new analyses and models.
• It can also be used to prototype new analytics-driven LOB apps and temporary analytic solutions.
• You may use an RDBMS, Hadoop, or other Hadoop.
• Allows users to combine new data with existing information to find ways to improve business processes
• This allows users to try different data types before committing to one solution.
• You may use an RDBMS, Hadoop-based solution on-premises or in the cloud. Hadoop is particularly well-suited for processing large volumes of multi-structured information.
• This represents a paradigm shift in how organizations create analytic solutions.

1. 1. Data doesn’t have to be modeled or integrated into an EDW before being analyzed. This increases flexibility and speeds up the time to value
   2. Enhance traditional business decision-making with solutions that increase analytics’ business value throughout the enterprise.

This is how the paradigm shift in Business Intelligence has occurred. It is now that ISVs are required to modernize and build software products and applications with these capabilities. ISVs who have recognized this and started to implement the change/update their products will be able to gain a competitive edge.

Disrupting the BI/DW ecosystem

Business Intelligence analytics and BI analytics will bring a new era to business. The BI market is sure to grow with the addition of mobile BI and open-source BI tools.

ISVs already recognized the potential of the cloud and have moved their software and apps there, offering SaaS-based services for customers. Their next question is “Can a Business Intelligence Module be moved there?” These have their own benefits and challenges that can be solved. This is what has led to ‘AaaS – Analytics As a Service. In the next blog, we will discuss his work in greater detail…